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REF: organize MultiIndex indexing tests
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fix mypy errors in pandas/tests/arithmetic/test_datetime64.py
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| 2020-02-05T18:52:59Z
| 2020-02-19T14:09:38Z
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|
CONTRIBUTOR
| null |
Part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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Are the contributors in the whatsnew v1.0.1 note correct?
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| 2020-02-05T19:02:26Z
| 2020-03-23T13:41:35Z
| 2020-03-23T13:41:35Z
|
MEMBER
| null |
Having a look at https://github.com/pandas-dev/pandas/milestone/69?closed=1, it seems there's some contributors (e.g. dsaxton , srvanrell) who aren't listed in the whatsnew page: https://pandas.pydata.org/docs/whatsnew/v1.0.1.html
> A total of 7 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
>
> Guillaume Lemaitre
>
> Jeff Reback
>
> Joris Van den Bossche
>
> Kaiqi Dong
>
> MeeseeksMachine
>
> Pandas Development Team
>
> Tom Augspurger
|
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MDExOlB1bGxSZXF1ZXN0MzcxNTQyNzMz
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fix type errors in pandas/tests/extension/json/array.py
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| 2020-02-05T19:21:46Z
| 2020-02-05T20:59:29Z
| 2020-02-05T20:59:22Z
|
CONTRIBUTOR
| null |
Part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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MDU6SXNzdWU1NjA1ODg3NjE=
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|
Change of rolling behavior in Pandas 1.0.0 vs 0.25.3
|
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| 2020-02-05T19:44:12Z
| 2020-02-05T19:52:14Z
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|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({
'a': np.arange(10, dtype=float),
'b': [3.0, np.NaN] * 5,
'key': list('ddeefffggh'),
})
expected = df.sort_values(['key', 'a']).assign(
rolled=lambda d: d.groupby('key')
.b.rolling(3, min_periods=1)
.apply(lambda x: x.mean(), raw=True)
.reset_index(level=0, drop=True)
)
```
#### Problem description
1.0.0 result:
```
a b key rolled
0 0.0 3.0 d 3.0
1 1.0 NaN d 3.0
2 2.0 3.0 e 3.0
3 3.0 NaN e 3.0
4 4.0 3.0 f 3.0
5 5.0 NaN f 3.0
6 6.0 3.0 f 3.0
7 7.0 NaN g NaN
8 8.0 3.0 g 3.0
9 9.0 NaN h NaN
```
0.25.3 behavior:
```
a b key rolled
0 0.0 3.0 d 3.0
1 1.0 NaN d NaN
2 2.0 3.0 e 3.0
3 3.0 NaN e NaN
4 4.0 3.0 f 3.0
5 5.0 NaN f NaN
6 6.0 3.0 f NaN
7 7.0 NaN g NaN
8 8.0 3.0 g NaN
9 9.0 NaN h NaN
```
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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|
`Series.replace` fails on categoricals with list
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| 3
| 2020-02-05T20:05:27Z
| 2020-02-17T16:59:53Z
| 2020-02-17T16:59:53Z
|
CONTRIBUTOR
| null |
Looks like this was introduced in #27026: not sure of the details of what was incorrect in the old version but I do know that this at least didn't error:
```python
cat = pd.CategoricalIndex(['a', 'b']).to_series()
cat.replace('a', 'A') # works
cat.replace(['a'], 'A')
~/model/.venv/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in replace(self, to_replace, value, inplace)
2440 inplace = validate_bool_kwarg(inplace, "inplace")
2441 cat = self if inplace else self.copy()
-> 2442 if to_replace in cat.categories:
2443 if isna(value):
2444 cat.remove_categories(to_replace, inplace=True)
~/model/.venv/lib/python3.7/site-packages/pandas/core/indexes/base.py in __contains__(self, key)
3898 @Appender(_index_shared_docs["contains"] % _index_doc_kwargs)
3899 def __contains__(self, key) -> bool:
-> 3900 hash(key)
3901 try:
3902 return key in self._engine
TypeError: unhashable type: 'list'
```
Seems like most of the methods used in this implementation already handle list-like inputs so it should be a pretty easy fix..?
#### Output of ``pd.show_versions()``
<details>
pandas : 1.0.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzcxNTgyMDIy
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CLN: inconsistent kwarg name
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| 2020-02-05T20:58:40Z
| 2020-04-05T17:34:04Z
| 2020-02-06T17:16:23Z
|
MEMBER
| null |
A couple of days ago the "setting" kwarg got introduced in core.indexing, but I should have called it "is_setter" to match the existing pattern in that module. This fixes that.
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| 0
| 2020-02-05T21:02:07Z
| 2020-02-06T23:58:05Z
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|
MEMBER
| null |
```
df = DataFrame({"A": [1, 2, 3]}, index=list("abc"))
>>> df.at[0]
ValueError: At based indexing on an non-integer index can only have non-integer indexers
>>> df.loc[0]
TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>
```
I would expect these to behave the same.
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| 6
| 2020-02-05T21:43:43Z
| 2020-02-06T21:05:06Z
| 2020-02-06T07:49:01Z
|
CONTRIBUTOR
| null |
- Need this for PR https://github.com/pandas-dev/pandas/issues/31677
cc. @TomAugspurger @jorisvandenbossche
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| 2020-02-07T08:49:26Z
| 2020-02-06T23:58:06Z
|
MEMBER
| null |
- [x] closes #31722
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This also (very) indirectly addresses #31683 which in turn will let us get rid of `CategoricalIndex.get_value` altogether.
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MDU6SXNzdWU1NjA2Njk4MDQ=
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API: add ignore_index keyword to dropna() and other similar functions
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| 2020-02-05T22:35:04Z
| 2021-07-28T04:52:15Z
| null |
CONTRIBUTOR
| null |
#### Code Sample
**Before**
```python
df = pd.DataFrame({'A': [0, pd.NA, 1]})
df.dropna().reset_index(drop=True)
```
**After**
```python
df = pd.DataFrame({'A': [0, pd.NA, 1]})
df.dropna(ignore_index=True)
```
#### Problem description
I have noticed a lot of dataframe reshaping functions acquiring the `ignore_index` keyword. (e.g. #30114). I think `df.dropna()` is a good candidate for this keyword, as are any other functions which change the index from being a consecutive sequence of integers
#### Expected Output
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'A': [0, pd.NA, 1]})
In [3]: df
Out[3]:
A
0 0
1 <NA>
2 1
In [4]: df.dropna().reset_index(drop=True)
Out[4]:
A
0 0
1 1
In [5]: df.dropna(ignore_index=True)
Out[5]:
A
0 0
1 1
```
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MDU6SXNzdWU1NjA2OTAxNDk=
| 31,726
|
pandas.read_excel() close the io stream
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| 2020-02-05T23:28:56Z
| 2020-02-06T05:56:45Z
| 2020-02-06T05:56:44Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
with open(r'a.xlsx','rb') as f: # input the .xlsx
df_top = pd.read_excel(f,'Sheet1',encoding = 'utf-8').iloc[:,0:2]
df_less = pd.read_excel(f,'Sheet2',encoding = 'utf-8').iloc[:,0:2]
```
#### Problem description
The first read_excel() works fine. But the second read_excel() get the following:
Traceback (most recent call last):
File "xxx.py", line 127, in <module>
df_less = pd.read_excel(f,'Sheet2',encoding = 'utf-8').iloc[:,0:2]
File "xxx\pandas\io\excel\_base.py", line 304, in read_excel
io = ExcelFile(io, engine=engine)
File "xxx\pandas\io\excel\_base.py", line 821, in __init__
self._reader = self._engines[engine](self._io)
File "xxx\pandas\io\excel\_xlrd.py", line 21, in __init__
super().__init__(filepath_or_buffer)
File "xxx\pandas\io\excel\_base.py", line 350, in __init__
filepath_or_buffer.seek(0)
ValueError: seek of closed file
It seems the read_excel() change the io object "f". Is that some new feature in 1.0? Older versions don't have the problem.
I think it should be fixed because a function should not change the outer variable.
#### Expected Output
read_excel() should not change the io stream and should be able to read the same io stream several times.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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TST/CLN: dtype test_construct_from_string
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MDExOlB1bGxSZXF1ZXN0MzcxNjUyNzgy
| 31,729
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BUG: Fix to_excel writers handling of cols
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| 2020-02-06T00:32:35Z
| 2020-05-01T22:02:17Z
| 2020-02-06T23:41:13Z
|
CONTRIBUTOR
| null |
- [x] closes #31677
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry - pending https://github.com/pandas-dev/pandas/pull/31723
Ref: https://github.com/pandas-dev/pandas/pull/31723
cc. @WillAyd @jbrockmendel
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MDExOlB1bGxSZXF1ZXN0MzcxNjcwNDM5
| 31,730
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fixed mypy errors in mypy-pandas.tests.extension.decimal.test_decimal
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| 1
| 2020-02-06T01:54:12Z
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CONTRIBUTOR
| null |
Part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: DataFrame.convert_dtypes fails on column that is already "string" dtype
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| 2020-02-06T03:06:19Z
| 2020-02-12T15:05:29Z
| 2020-02-12T15:05:29Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['d', 'e', 'f']})
In [2]: df.dtypes
Out[2]:
A object
B object
dtype: object
In [3]: df1 = df.convert_dtypes()
In [4]: df1.dtypes
Out[4]:
A string # <------ expected
B string # <------ expected
dtype: object
In [5]: df2 = df1.convert_dtypes()
In [6]: df2.dtypes
Out[6]:
A object # <------ NOT expected
B object # <------ NOT expected
dtype: object
In [7]: df3['A'] = 'test'
In [8]: df3
Out[8]:
A B
0 test d # <------ expected
1 test e # <------ expected
2 test f # <------ expected
In [9]: df3.dtypes
Out[9]:
A object # <------ NOT expected (but understandable)
B string # <------ expected
dtype: object
In [10]: df4 = df3.convert_dtypes()
In [11]: df4
Out[11]:
A B
0 test b'd' # <------ NOT expected (Why are they bytes now???)
1 test b'e' # <------ NOT expected
2 test b'f' # <------ NOT expected
In [12]: df4.dtypes
Out[12]:
A string # <------ expected
B object # <------ NOT expected
dtype: object
```
#### Problem description
The documentation for [DataFrame.convert_dtypes()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.convert_dtypes.html) *claims* that it will 'Convert columns to best possible dtypes using dtypes supporting pd.NA'. However, this does not appear to be the case. As you can see in `Out[6]` above, if the dtypes are already optimal, they will be converted back to objects.
Even worse, if some of the dtypes are optimal, but other dtypes are objects, they will be switched -- the optimal dtypes will become objects, and the objects will become optimal.
Some mysterious additional details:
- Assigning a string to an 'optimal' column changes the dtype to object. (`Out[9]`)
- If one column is a string, and the other an object `df.convert_dtypes()` will not only cause the string column to become object and the object column to become string, it will also change the strings in the newly created object column into bytes (`Out[11]`)
#### Expected Output
Anything which can be converted to one of the new nullable datatypes would be converted to one of the new nullable datatypes. Anything which is already a nullable datatype would remain as it is.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDU6SXNzdWU1NjA3NTIzNDU=
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Wrong start datetime for data_range
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| 2020-02-06T03:15:27Z
| 2020-02-06T04:09:48Z
| 2020-02-06T04:09:47Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>>pd.date_range(start='1/1/2018', periods=5, freq='M')
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
'2018-05-31'],
dtype='datetime64[ns]', freq='M')
```
#### Problem description
Why datatime start from '2018-01-31' ?
|
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MDExOlB1bGxSZXF1ZXN0MzcxNjk2MzYy
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Add --quiet option to isort command
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CONTRIBUTOR
| null |
- [x] closes #30974
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: list-like to_replace on Categorical.replace is ignored or crash
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| 2020-02-06T04:41:51Z
| 2020-02-17T17:00:54Z
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|
CONTRIBUTOR
| null |
- [X] closes #31720
- [X] tests added / passed
- [x] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Covers the case where `to_replace` is a list-like and `value` is a string. Other cases, like "`to_replace` is dict and `value` is None", or "`to_replace` and `value` are both lists" are handled earlier in generic.py
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BUG: groupby _cython_agg_blocks implicitly assumes unique columns
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| 4
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| 2020-09-15T22:33:48Z
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|
MEMBER
| null |
xref #31616, the two test cases that adds both have unique columns. Editing test_agg_split_object_part_datetime to make columns non-unique breaks it:
```
df = pd.DataFrame(
{
"A": pd.date_range("2000", periods=4),
"B": ["a", "b", "c", "d"],
"C": [1, 2, 3, 4],
"D": ["b", "c", "d", "e"],
"E": pd.date_range("2000", periods=4),
"F": [1, 2, 3, 4],
}
).astype(object)
df.columns = ["A", "B", "B", "D", "E", "F"]
>>> result = df.groupby([0, 0, 0, 0]).min()
pandas.core.indexes.base.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
cc @WillAyd @TomAugspurger
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MDExOlB1bGxSZXF1ZXN0MzcxNzEzODc2
| 31,736
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REF: turn _try_mi into MultiIndex._get_values_for_loc
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| 1
| 2020-02-06T05:18:42Z
| 2020-02-09T17:26:37Z
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MEMBER
| null |
Discussed in #31640.
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Backport PR #31723 on branch 1.0.x (DOC: Add 1.0.2 whatsnew)
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| 2
| 2020-02-06T07:49:29Z
| 2020-02-06T11:12:07Z
| 2020-02-06T11:12:07Z
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NONE
| null |
Backport PR #31723: DOC: Add 1.0.2 whatsnew
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CI: continuous-integration/travis-ci/pr build fails due to unavailable channel
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|
MEMBER
| null |
#### Problem description
**continuous-integration/travis-ci/pr** build currently seems to fail for everyone, regardless of PR. Error output below:
```
conda env create -q --file=ci/deps/travis-37.yaml
Collecting package metadata (repodata.json): ...working... failed
UnavailableInvalidChannel: The channel is not accessible or is invalid.
channel name: c3i_test
channel url: https://conda.anaconda.org/c3i_test
error code: 404
You will need to adjust your conda configuration to proceed.
Use `conda config --show channels` to view your configuration's current state,
and use `conda config --show-sources` to view config file locations.
The command "ci/setup_env.sh" failed and exited with 1 during .
Your build has been stopped.
```
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matplotlib's xaxis "margin" dropped when plotting using pandas
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| 4
| 2020-02-06T09:39:10Z
| 2020-02-06T10:53:38Z
| 2020-02-06T10:48:28Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = x
fig, axs = plt.subplots(2, 1, constrained_layout=True)
axs[0].plot(x, y, linestyle='', marker='.')
axs[0].set_title('matplotlib')
pd.Series(y, index=x).plot(ax=axs[1], linestyle='', marker='.')
axs[1].set_title('pandas')
fig.show()
```
#### Problem description
Matplotlib adds some "margin" on both sides of the horizontal axis, but pandas does not.
This is a problem because, when we plot using points, the extreme x values are shown confused with the vertical axis lines, which makes them very difficult to see.
Would it be possible to align pandas' behavior with matplotlib's one?
Thanks a lot.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.2.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0MzcxODE4Njg3
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TST: expand tests for ExtensionArray setitem with nullable arrays
|
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| 1
| 2020-02-06T10:22:46Z
| 2020-02-13T13:41:55Z
| 2020-02-13T13:39:47Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/31484 to add some more test cases (eg also test the validation done in the `check_array_indexer` (wrong length, missing values), as well for nullable integer arrays), similarly to the tests I added for `__getitem__`.
cc @charlesdong1991
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|
Rename mad() in mean_absolute_deviation()
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[] | null | 3
| 2020-02-06T10:44:28Z
| 2020-02-06T13:26:36Z
| 2020-02-06T11:09:38Z
|
NONE
| null |
#### Problem description
MAD is a overloaded shortcut: It stands for *M*edian *A*bsolute *D*eviation (more common), but also can be used as *M*ean *A*bsolute *D*eviation (which is used in pandas).
#### Expected Output
To avoid confusion, the method should be renamed from `.mad()` to `.mean_absolute_deviation()`, and when implemented the more commonly used `.median_absolute_deviation()`.
If you are fine with it, I would kick off a PR.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-24-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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MDU6SXNzdWU1NjA5Njk1NDM=
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|
pd.to_datetime() Error in MacOS - TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]") TypeError: dtype timedelta64[ns] cannot be converted to datetime64[ns]
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[] | null | 2
| 2020-02-06T12:05:07Z
| 2020-02-17T17:06:16Z
| 2020-02-17T17:06:15Z
|
NONE
| null |
#### Function Error in MacOS
```python
df = pd.read_csv('filemane.csv', encoding='latin-1')
df1['ProcessFinishTime'] = pd.to_datetime(df1['ProcessFinishTime'],errors='ignore')
df1['ProcessStartTime'] = pd.to_datetime(df1['ProcessStartTime'],errors='ignore')
```
#### Problem description
Hi Guys,
This is a problem that a faced in MacOS only about DateTime, I trying to convert format (12:54:58)
in to DateTime.
It run correctly in Windows(10) and Linux(ubuntu) as well, however, in MacOS i faced this problem that returns an error message below:
```
raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
TypeError: dtype timedelta64[ns] cannot be converted to datetime64[ns]
```
I search for and for while it's not cleary to me the reason for this error happens.
And my questions is:
**1**- This issue is particular for MAcOS?
**2**2 - I try to replace pd.to_datetime() for pd.to_timedelta(), however pd.to_datetime is too better.
**3**- I try to replace pd.to_datetime() for pd.to_timedelta(), however pd.to_datetime is too better.
Below is the documentation that i follow.:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html
Using latest version in both systems.
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|
Wrong char in Polish datetime month_name function
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[] | null | 4
| 2020-02-06T12:17:32Z
| 2021-07-28T04:53:51Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# using this:
df['date'].dt.month_name('PL')
# should return this:
df['date'].dt.month_name('PL').str.replace('ñ','ń')
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
Wrong char was used for Polish month name.
Pandas returns 'Styczeñ', should be 'Styczeń'.
ñ -> ń
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : 0.4.0
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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CI: Update travis-37.yaml Conda channel
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| 2020-02-06T13:36:53Z
| 2020-02-06T15:44:14Z
| 2020-02-06T15:44:12Z
|
CONTRIBUTOR
| null |
Remove unrequired channel
- [x] closes #31739
Cc @TomAugspurger
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New string data type aggregations (min, max, sum) work for DataFrames but not Series
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| 6
| 2020-02-06T13:40:53Z
| 2021-07-28T04:55:04Z
| null |
CONTRIBUTOR
| null |
```python
>>> s = pd.Series(['a', 'b'], dtype='string')
>>> s.max()
TypeError: Cannot perform reduction 'max' with string dtype
>>> df = s.to_frame()
>>> df.max()
0 b
dtype: object
```
#### Problem description
I assume this isn't supposed to work on DataFrames if it doesn't work on strings. min and sum do the same thing.
#### Expected Output
Consistent behavior for both dataframes and series
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0+untagged.1.gce8af21.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NjEwMjk0MzM=
| 31,747
|
read_parquet gives ArrowIOError
|
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| 2020-02-06T13:57:12Z
| 2020-02-06T19:03:16Z
| 2020-02-06T19:03:16Z
|
NONE
| null |
I am trying to open a .parquet file in pandas but I am failing at it. I tried the solution proposed in https://stackoverflow.com/questions/54201799/pandas-cannot-read-parquet-files-created-in-pyspark but it is not working for me.
Does anyone have a clue of how to fix this ?
####
python code:
pd.read_parquet('filepath')
####
Error received in the consol:
ArrowIOError Traceback (most recent call last)
<ipython-input-32-623a6e60c94a> in <module>
----> 2 pd.read_parquet('filepath'')
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parquet.py in read_parquet(path, engine, columns, **kwargs)
308
309 impl = get_engine(engine)
--> 310 return impl.read(path, columns=columns, **kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parquet.py in read(self, path, columns, **kwargs)
123 kwargs["use_pandas_metadata"] = True
124 result = self.api.parquet.read_table(
--> 125 path, columns=columns, **kwargs
126 ).to_pandas()
127 if should_close:
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, read_dictionary, filesystem, filters, buffer_size)
1272 read_dictionary=read_dictionary,
1273 buffer_size=buffer_size,
-> 1274 filesystem=filesystem, filters=filters)
1275 else:
1276 pf = ParquetFile(source, metadata=metadata,
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in __init__(self, path_or_paths, filesystem, schema, metadata, split_row_groups, validate_schema, filters, metadata_nthreads, read_dictionary, memory_map, buffer_size)
1058
1059 if validate_schema:
-> 1060 self.validate_schemas()
1061
1062 def equals(self, other):
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in validate_schemas(self)
1090 self.schema = self.common_metadata.schema
1091 else:
-> 1092 self.schema = self.pieces[0].get_metadata().schema
1093 elif self.schema is None:
1094 self.schema = self.metadata.schema
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in get_metadata(self)
558 metadata : FileMetaData
559 """
--> 560 f = self.open()
561 return f.metadata
562
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in open(self)
565 Returns instance of ParquetFile
566 """
--> 567 reader = self.open_file_func(self.path)
568 if not isinstance(reader, ParquetFile):
569 reader = ParquetFile(reader, **self.file_options)
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in _open_dataset_file(dataset, path, meta)
940 read_dictionary=dataset.read_dictionary,
941 common_metadata=dataset.common_metadata,
--> 942 buffer_size=dataset.buffer_size
943 )
944
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\parquet.py in __init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size)
135 self.reader.open(source, use_memory_map=memory_map,
136 buffer_size=buffer_size,
--> 137 read_dictionary=read_dictionary, metadata=metadata)
138 self.common_metadata = common_metadata
139 self._nested_paths_by_prefix = self._build_nested_paths()
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\_parquet.pyx in pyarrow._parquet.ParquetReader.open()
C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\error.pxi in pyarrow.lib.check_status()
ArrowIOError: Invalid parquet file. Corrupt footer.
####
version:
python 3.7.6
pandas 1.0.1
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MDExOlB1bGxSZXF1ZXN0MzcxOTEzMzI1
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BUG: Fixed encoding of pd.NA with to_json
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| 4
| 2020-02-06T13:57:26Z
| 2020-02-08T08:17:10Z
| 2020-02-08T08:11:34Z
|
CONTRIBUTOR
| null |
- [x] closes #31615
- [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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Backport PR #31745 on branch 1.0.x (CI: Update travis-37.yaml Conda channel )
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| 2020-02-06T23:41:55Z
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NONE
| null |
Backport PR #31745: CI: Update travis-37.yaml Conda channel
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Decoding issue when reading in py3 a datetime64 hdf data that was created in py2
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| 1
| 2020-02-06T17:30:08Z
| 2020-02-09T21:34:20Z
| 2020-02-09T21:34:20Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
### In Python 2, create the `.hdf` file with a `datetime64[ns]` column
```python
#python 2
>>> d = {"data": pd.Timestamp("2020-02-06T18:00")}
>>> df = pd.DataFrame(d, index=[1])
>>> df
data
1 2020-02-06 18:00:00
>>> df.dtypes
data datetime64[ns]
dtype: object
>>> df.to_hdf('py2_data.hdf', 'py2_data')
```
### Reading the hdf in python 2 works just fine
```python
# python2
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
data
1 2020-02-06 18:00:00
>>> df.dtypes
data datetime64[ns]
dtype: object
```
## Reading the hdf in **Python 3** the dtype of the data column is set to `int64` instead of `datatime64`
```python
# python 3
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
data
1 1581012000000000000
>>> df.dtypes
data int64
dtype: object
```
#### Problem description
The reason why the _dtype_ is being loaded as a `int64` in python 3 is because pandas is not reading correctly the `value_type` attribute from the hdf file. The current implementation does *not* explicitly decodes to "UTF-8" the attribute value when its type is `np.bytes_`, which causes it to read the dtype as `b'datetime64` instead of the correct `datetime64` value. That, in turn, causes it to treat the raw value `1581012000000000000` with an inferred dtype of `int64` instead of coercing it to a `M8[ns]` dtype that would give the correct `2020-02-06 18:00:00` datetime value.
This is a similar problem to the one from issue #15725, but that one was when reading the _index_ from the _hdf_ but this one is when reading the data.
The fix for this seems to be really straight forward, you just need to ensure the value of the`value_type` attribute is decoded to _UTF-8_ when reading it from the hdf file. More specifically, [in this line of pytables.py](https://github.com/pandas-dev/pandas/blob/v1.0.1/pandas/io/pytables.py#L2727)
```diff
diff --git a/pytables.py b/pytables.py
index 06c9aa1..9d074d5 100644
--- a/pytables.py
+++ b/pytables.py
@@ -2724,7 +2724,7 @@ class GenericFixed(Fixed):
if isinstance(node, tables.VLArray):
ret = node[0][start:stop]
else:
- dtype = getattr(attrs, "value_type", None)
+ dtype = _ensure_decoded(getattr(attrs, "value_type", None))
shape = getattr(attrs, "shape", None)
```
I can make the change and open a PR, as it seems to be really straight forward, just let me know what you prefer.
Note: this is an issue only when the ".hdf" file is created in python 2 using the _fixed_ format. If it is created using the _table_ format it works as expected.
Thanks!
#### Expected Output
The expected output is that in python 3 it will parse the dtype of the columns correctly, so in this case it should be:
```python
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
data
1 2020-02-06 18:00:00
>>> df.dtypes
data datetime64[ns]
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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F string fixes
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| 1
| 2020-02-06T17:33:01Z
| 2020-02-06T21:43:36Z
| 2020-02-06T21:43:30Z
|
CONTRIBUTOR
| null |
Related to issue https://github.com/pandas-dev/pandas/issues/29547
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REF: _cython_agg_blocks
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| 2020-02-06T17:37:16Z
| 2020-08-27T20:18:58Z
| 2020-02-08T00:41:34Z
|
MEMBER
| null |
This is effectively an alternative approach to #31616, trying to use a 1-pass approach.
This does _not_ address the uniqueness problem discussed in #31735.
cc @TomAugspurger @WillAyd
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CLN/TST: organize DatetimeIndex tests
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1/many
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`raw=True` no longer applies to groupby().rolling() in 1.0.0
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| 1
| 2020-02-06T19:13:09Z
| 2020-02-10T12:50:32Z
| 2020-02-10T12:50:32Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'id': [1, 1, 1], 'value': [1, 2, 3]})
def foo(x):
print(type(x))
return 0.0
```
When setting `raw=True`
```
>>> df.groupby("id").value.rolling(1).apply(foo, raw=True, engine='numba')
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
id
1 0 0.0
1 0.0
2 0.0
Name: value, dtype: float64
>>> df.groupby("id").value.rolling(1).apply(foo, raw=True, engine='cython')
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
id
1 0 0.0
1 0.0
2 0.0
Name: value, dtype: float64
>>> df.groupby("id").value.rolling(1).apply(foo, raw=True)
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
id
1 0 0.0
1 0.0
2 0.0
Name: value, dtype: float64
```
#### Problem description
This changes the behavior of `raw=True`, it seems it no long allows user to pass numpy array to a rolling udf.
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CLN: misplaced TimedeltaIndex tests
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BUG Decode to UTF-8 the dtype string read from a hdf file
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| 5
| 2020-02-06T20:02:14Z
| 2020-02-10T13:49:36Z
| 2020-02-09T21:34:20Z
|
CONTRIBUTOR
| null |
Fixes GH31750
The dtype value wasn't being decoded to `UTF-8` when reading a DataFrame
from a hdf file. This was a problem when reading a hdf that was
created from python 2 with a fixed format as the dtype was being read as `b'datetime'`
instead of `datetime`, which caused `HDFStore` to read the data as
`int64` instead of coercing it to the correct `datetime64` dtype.
- [x] closes #31750
- [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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MDExOlB1bGxSZXF1ZXN0MzcyMDg2NDQw
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Implement some reductions for string Series
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MEMBER
| null |
- [x] closes #31746, closes #18588
- [x] tests added / passed
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REF/TST: misplaced tests in tests.indexes.period
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MDU6SXNzdWU1NjEyNTE3NTU=
| 31,759
|
DEPR: disallow mutating operations in groupby.apply
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| 2020-02-06T20:28:43Z
| 2020-06-28T06:53:45Z
| 2020-06-28T06:53:45Z
|
MEMBER
| null |
A common issue [citation needed] comes up in groupby.apply when a user passes a function that modifies its argument in-place. This is because we call the function on the first group first to see if we can use a fastpath, and then again when we apply it to all groups.
We could add a kwarg to specify if a function may mutate its data, e.g. `gb.apply(myfunc, may_mutate=True)` in which case we could avoid re-calling the function (probably means no fast-path). With `may_mutate=False` we could use a context in which we set the data's flags to be immutable before calling anything on it.
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|
groupby().agg() calls the user function for an extra time with empty inputs in 1.0.0
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| 10
| 2020-02-06T21:11:37Z
| 2020-02-21T18:38:13Z
| 2020-02-21T18:38:13Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"key": ["a", "b", "c", "c"], "value": [1, 2, 3, 4]})
def foo(x):
print(f"called with {x}")
return len(x)
df.groupby("key")["value"].agg(foo)
```
#### Problem description
In 1.0.0, this code outputs:
```
called with Series([], Name: value, dtype: int64)
called with 0 1
Name: value, dtype: int64
called with 1 2
Name: value, dtype: int64
called with 2 3
3 4
Name: value, dtype: int64
Out[10]:
key
a 1
b 1
c 2
Name: value, dtype: int64
```
In 0.25.3, the code outputs:
```
In [5]: df.groupby("key")["value"].agg(foo)
called with 0 1
Name: value, dtype: int64
called with 1 2
Name: value, dtype: int64
called with 2 3
3 4
Name: value, dtype: int64
Out[5]:
key
a 1
b 1
c 2
Name: value, dtype: int64
```
In 1.0.0, `foo` is called one more time with empty input, which can break user code
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyMTI0MTA2
| 31,761
|
replaced old format strings
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| 2020-02-06T21:36:36Z
| 2020-03-07T21:17:30Z
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|
NONE
| null |
Changed the string formatting to f-strings.
ref #29547
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Rolling Apply Docs Broken
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| 2020-02-06T22:05:07Z
| 2020-03-10T08:35:48Z
| 2020-03-10T08:35:48Z
|
MEMBER
| null |
A google of "rolling apply" for me yields:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.apply.html
Which results in an HTTP 403. Working link now is:
https://pandas.pydata.org/docs/reference/api/pandas.core.window.rolling.Rolling.apply.html
As pointed out by @TomAugspurger seems to be an issue with `window.Rolling.apply` vs `window.rolling.Rolling.apply` (/docs should be a symlink to /pandas-docs/stable)
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Assigning scalars to nullable dataframe columns reverts to default dtypes.
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[] | null | 11
| 2020-02-06T22:49:44Z
| 2021-07-28T04:57:03Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, dtype='string')
In [3]: df.dtypes
Out[3]:
A string
dtype: object
In [4]: df['A'] = 'test'
In [5]: df.dtypes
Out[5]:
A object
dtype: object
In [6]: df['A'] = pd.Series(['test'] * len(df), dtype='string', index=df.index)
In [7]: df
Out[7]:
A
0 test
1 test
2 test
In [8]: df.dtypes
Out[8]:
A string
dtype: object
# Don't need to set the index with pd.array()
In [9]: df['A'] = pd.array(['test'] * len(df) , dtype='string')
In [40]: df.dtypes
Out[40]:
A string
dtype: object
```
#### Problem description
Because the new nullable datatypes are not yet the default, it's very tricky to convince dtypes to stay as nullable dtypes. Helpful shortcuts for assigning repeating values to a dataframe column infer the default types, even if the column is currently a nullable type, and the data being added can be represented in the same nullable type. I'm not sure if it's best just to wait until the nullable dtypes become the default, and live with the workarounds shown above for now, or if perhaps another keyword could be added to [`df.assign()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html) to specify the intended dtype of a column.
Alternatively, is there any scope for adding dtype information to scalars?
Something like:
```python
df['A'] = pd.typed('test', dtype='string')
# OR
df['A'] = pd.StringDtype('test')
```
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MDExOlB1bGxSZXF1ZXN0MzcyMTY2MTI5
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Backport PR #31729 on branch 1.0.x (BUG: Fix to_excel writers handling of cols)
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| 2020-02-06T23:41:26Z
| 2020-02-07T00:38:54Z
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NONE
| null |
Backport PR #31729: BUG: Fix to_excel writers handling of cols
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MDExOlB1bGxSZXF1ZXN0MzcyMTk1NTYz
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REF: Remove CategoricalIndex.get_value
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| 2020-02-09T15:47:49Z
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MEMBER
| null |
It is no longer needed following #31724.
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MDExOlB1bGxSZXF1ZXN0MzcyMTk2NTE0
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REF: share _partial_date_slice between PeriodIndex/DatetimeIndex
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MDExOlB1bGxSZXF1ZXN0MzcyMjE3MjM4
| 31,767
|
TST: parametrize some indexing tests
|
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2/many
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MDExOlB1bGxSZXF1ZXN0MzcyMjI1NTQ3
| 31,768
|
CLN: trim unreachable indexing code
|
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| 3
| 2020-02-07T04:11:11Z
| 2020-02-09T20:58:48Z
| 2020-02-09T20:42:31Z
|
MEMBER
| null |
make _set_value not return anything (the DataFrame docstring in particular is misleading)
|
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MDExOlB1bGxSZXF1ZXN0MzcyMjk3OTY2
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|
ERR: improve error message for invalid indexer
|
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| 0
| 2020-02-07T08:47:08Z
| 2020-02-08T10:27:06Z
| 2020-02-08T10:26:59Z
|
MEMBER
| null |
Convert the long repr of the Index into just the name. So something like
```
cannot do slice indexing on <class 'pandas.core.indexes.datetimes.DatetimeIndex'> with these indexers [key] of <class 'float'>
```
becomes
```
cannot do slice indexing on DatetimeIndex with these indexers [key] of type float"
```
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read_from_sql() throws TypeError: 'NoneType' object is not iterable in sql.py
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| 2020-02-07T09:50:39Z
| 2020-02-11T08:45:47Z
| 2020-02-07T11:23:43Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pyodbc
import sys
from contextlib import contextmanager
import textwrap
import pandas as pd
@contextmanager
def open_db_connection(connection_string):
pyodbc.pooling = False
connection = pyodbc.connect(connection_string)
try:
yield connection
except pyodbc.DatabaseError as err:
error, = err.args
sys.stderr.write(error.message)
finally:
connection.close()
def create_query_string(sql_full_path):
with open(sql_full_path, 'r') as f_in:
lines = f_in.read()
query_string = textwrap.dedent("""{}""".format(lines))
return query_string
def query(server, database, driver, queryObj):
connection_string = 'DRIVER={};SERVER={};DATABASE={};Trusted_Connection=yes'.format(driver, server, database)
noCount = """ SET NOCOUNT ON; """
with open_db_connection(connection_string) as conn:
res = pd.read_sql_query(noCount+queryObj, conn)
return res
def get_data_set(sql_file_name, db_info):
query_string = create_query_string(sql_file_name+'.sql')
server = db_info['server']
database = db_info['database']
driver = db_info['driver']
df = query(server, database, driver, query_string)
#df.to_excel(sql_file_name+'_backup.xlsx', index=False)
return df
db_info = {'server' : 'wsp', 'database' : 'fin', 'driver' : 'SQL Server'}
df = get_data_set('equities', db_info)
```
SQL part
```sql
IF OBJECT_ID('tempdb..#tmp') IS NOT NULL DROP TABLE #tmp;
select *
into #tmp
from fin.dbo.items_info
IF OBJECT_ID('tempdb..#test1') IS NOT NULL DROP TABLE #test1;
select t.PositionID, b.SecurityID
into #test1
from #tmp as t
inner join fin.dbo.items as b on (b.PositionID = t.PositionID and b.StudyDate = '20191230')
where t.ast = 'eq';
IF OBJECT_ID('tempdb..#beta_index') IS NOT NULL DROP TABLE #beta_index;
select distinct isin, beta_index into #beta_index from md.data.equity;
IF OBJECT_ID('tempdb..#test2') IS NOT NULL DROP TABLE #test2;
select t.PositionID,
case
when count(i.beta_index)=0 then 1
else count(i.beta_index)
end as noIndex
into #test2
from #test1 as t
left join #beta_index as i on (t.SecurityID = i.isin)
group by t.PositionID;
select * from #test2
```
#### Problem description
I am trying to get a dataframe from the above query, however it throws TypeError: 'NoneType' object is not iterable when trying to pull results from #test2 table ('SET NOCOUNT ON;' is being used) . Everything up to that point and after works perfectly, however it looks like it has something to do with counting and grouping. I can pull #test1 table above just fine. No issues with other lengthy queries with insert, update, group though.
Additionally, this query works fine when running it in SSMS v18.4 (using it as a driver).
#### Expected Output
I expect to get an output from #table2 table. The select statement should return a non empty table (which it does in SSMS).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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MDU6SXNzdWU1NjE1ODY0NjA=
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BUG: df.to_markdown() with empty frame incorrect output
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| 9
| 2020-02-07T11:39:00Z
| 2020-12-12T23:28:05Z
| 2020-12-12T23:28:05Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'id': [], 'first_name': [], 'last_name': []}).set_index('id')
print(df.to_markdown())
```
```
| id | first_name | last_name |
||
```
#### Problem description
The markdown output of empty DataFrames does not seem right to me (see Expected Output).
#### Expected Output
```
| id | first_name | last_name |
|-----:|-------------:|------------:|
```
#### Render difference
##### Current
| id | first_name | last_name |
||
##### Expected
| id | first_name | last_name |
|-----:|-------------:|------------:|
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
numba : 0.45.1
</details>
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MDU6SXNzdWU1NjE1OTE3OTE=
| 31,772
|
BUG: setitem on string dtype with slices fails
|
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| 0
| 2020-02-07T11:50:24Z
| 2020-02-12T12:38:27Z
| 2020-02-12T12:38:27Z
|
MEMBER
| null |
```
In [33]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, dtype='string')
In [34]: df.loc[0:, 'A'] = "test"
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-34-bc65087be9d7> in <module>
----> 1 df.loc[0:, 'A'] = "test"
~/scipy/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
629 key = com.apply_if_callable(key, self.obj)
630 indexer = self._get_setitem_indexer(key)
--> 631 self._setitem_with_indexer(indexer, value)
632
633 def _validate_key(self, key, axis: int):
~/scipy/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1014 # actually do the set
1015 self.obj._consolidate_inplace()
-> 1016 self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
1017 self.obj._maybe_update_cacher(clear=True)
1018
~/scipy/pandas/pandas/core/internals/managers.py in setitem(self, **kwargs)
538
539 def setitem(self, **kwargs):
--> 540 return self.apply("setitem", **kwargs)
541
542 def putmask(self, **kwargs):
~/scipy/pandas/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
417 applied = b.apply(f, **kwargs)
418 else:
--> 419 applied = getattr(b, f)(**kwargs)
420 result_blocks = _extend_blocks(applied, result_blocks)
421
~/scipy/pandas/pandas/core/internals/blocks.py in setitem(self, indexer, value)
1801
1802 check_setitem_lengths(indexer, value, self.values)
-> 1803 self.values[indexer] = value
1804 return self
1805
~/scipy/pandas/pandas/core/arrays/string_.py in __setitem__(self, key, value)
260 raise ValueError("Must provide strings.")
261
--> 262 super().__setitem__(key, value)
263
264 def fillna(self, value=None, method=None, limit=None):
~/scipy/pandas/pandas/core/arrays/numpy_.py in __setitem__(self, key, value)
273 value = np.asarray(value, dtype=self._ndarray.dtype)
274
--> 275 self._ndarray[key] = value
276
277 def __len__(self) -> int:
IndexError: arrays used as indices must be of integer (or boolean) type
```
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MDExOlB1bGxSZXF1ZXN0MzcyMzc5MDM4
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BUG: fix StringArray/PandasArray setitem with slice
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| 2
| 2020-02-07T12:03:45Z
| 2020-02-12T13:14:26Z
| 2020-02-12T12:38:27Z
|
MEMBER
| null |
Closes #31772
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MDU6SXNzdWU1NjE2MTkwNzQ=
| 31,774
|
REGR: Timestamp subtraction raises TypeError, Non informative
|
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| 8
| 2020-02-07T12:51:01Z
| 2020-03-11T01:45:54Z
| 2020-03-11T01:45:54Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
print(pd.Timestamp('2101-01-01 00:00:00') - pd.Timestamp('1688-01-01 00:00:00'))
```
Outputs:
```
TypeError: unsupported operand type(s) for -: 'Timestamp' and 'Timestamp'
```
#### Problem description
The difference between the timestamps is too large for the Timedelta object. This can be traced back to these lines:
https://github.com/pandas-dev/pandas/blob/2dadd0f48949182dda412eb4032b5234c43c83c1/pandas/_libs/tslibs/c_timestamp.pyx#L303
For the example we can see that
```python
print(pd.Timedelta(pd.Timestamp('2101-01-01 00:00:00').value - pd.Timestamp('1688-01-01 00:00:00').value))
```
results in
```OverflowError: int too big to convert```
#### Expected Output
Ideally, the expected output is:
```
150845 days 00:00:00
```
Note the expected output matches that of pandas 0.25.3.
Alternatively, the error message should be at least somewhat informative, for example by propagating the OverflowError (removing the try/except block from L302-305) or raising a new TypeError (similar to L296).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDU6SXNzdWU1NjE2MjYwNTE=
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|
Feature Request: Sample method for Groupby objects
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| 3
| 2020-02-07T13:05:50Z
| 2020-08-02T17:39:24Z
| 2020-06-14T15:22:16Z
|
NONE
| null |
Many times it is desirable to be able to iterate over a small number of groups of a group by object. Currently there is no direct way to get a random sample of groups.
Similar to DataFrame.sample method, can we have Groupby.sample method which gives us a Dictionary{'group_name'->'group_labels'} containing n randomly selected groups?
#### Expected behaviour
```python
import pandas as pd
df = pd.DataFrame([['Male', 1], ['Female', 3], ['Female', 2], ['Other', 1]],
columns=['gender', 'feature'])
grouped_df = df.groupby('gender')
# Desired feature
grouped_df.sample(n=2)
# {'Female': Int64Index([1, 2], dtype='int64'),
# 'Male': Int64Index([0], dtype='int64')}
```
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cannot import name 'OrderedDict'
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| 2020-02-07T13:53:16Z
| 2020-02-07T17:01:55Z
| 2020-02-07T16:33:01Z
|
NONE
| null |
#### cannot import name 'OrderedDict'
trying to work on collab and panda failed to import 'OrderedDict'
here is the code
import pandas as pd
data = pd.read_csv('/content/causalnex/fulcase_causal.csv', delimiter=';', index_col=0)
data.head(5)
here is the message
/usr/local/lib/python3.6/dist-packages/pandas/io/formats/html.py in <module>()
8 from textwrap import dedent
9
---> 10 from pandas.compat import OrderedDict, lzip, map, range, u, unichr, zip
11
12 from pandas.core.dtypes.generic import ABCMultiIndex
ImportError: cannot import name 'OrderedDict'
any suggestion
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|
GroupBy aggregation of DataFrame with MultiIndex columns breaks with custom function
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| 4
| 2020-02-07T14:33:09Z
| 2020-03-12T02:32:55Z
| 2020-03-12T02:32:55Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(
np.random.rand(10, 4),
columns=pd.MultiIndex.from_product([[1, 2], [3, 4]]))
grp = df.groupby(np.r_[np.ones(5), np.zeros(5)])
grp.agg(lambda s: s.mean())
```
#### Problem description
The above call raises
```python
ValueError: Length mismatch: Expected axis has 4 elements, new values have 2 elements
```
because
https://github.com/pandas-dev/pandas/blob/cf01369d5d82362565d5c58ee4d6094782b0f8e1/pandas/core/groupby/generic.py#L970-L972
assumes that the original columns were only `Index`. Doing
```python
grp.agg('mean')
```
works as expected (result with MultiIndex columns).
#### Expected Output
That of
```python
grp.agg('mean')
```
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 561,683,368
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MDU6SXNzdWU1NjE2ODMzNjg=
| 31,778
|
Error: tuple index out of range
|
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[] | null | 1
| 2020-02-07T14:50:49Z
| 2020-02-07T18:29:50Z
| 2020-02-07T18:29:50Z
|
NONE
| null |
I/P
dims = np.shape(df)
print(dims)
O/P
(142, 3)
I/P
pixel_matrix = np.reshape(df, (dims[0]* dims[1], dims[2]))
print(np.shape(pixel_matrix))
O/P
IndexError Traceback (most recent call last)
<ipython-input-71-0f88d37eb802> in <module>
----> 1 pixel_matrix = np.reshape(df, (dims[0]* dims[1], dims[2]))
2 print(np.shape(pixel_matrix))
IndexError: tuple index out of range
How to solve this error?
|
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| 561,696,081
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MDU6SXNzdWU1NjE2OTYwODE=
| 31,779
|
Categorical.from_codes fails for the (new nullable) Int64 dtype
|
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| 1
| 2020-02-07T15:10:35Z
| 2020-02-12T14:12:51Z
| 2020-02-12T12:36:51Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> codes = pd.Series([1, 0], dtype="Int64")
>>> pd.Categorical.from_codes(codes, categories=["foo", "bar"])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 649, in from_codes
raise ValueError("codes need to be array-like integers")
ValueError: codes need to be array-like integers
```
#### Problem description
`Categories.from_codes` works with Series with the Numpy `"int64"` dtype.
```python
>>> codes = pd.Series([1, 0])
>>> pd.Categorical.from_codes(codes, categories=["foo", "bar"])
[bar, foo]
Categories (2, object): [foo, bar]
```
I would expect that it will work with the new Pandas `"Int64"` dtype.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 561,703,265
|
MDU6SXNzdWU1NjE3MDMyNjU=
| 31,780
|
Groupby get_group is not consistent - sometimes it returns the grouping columns, sometimes it doesn't
|
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[] | null | 1
| 2020-02-07T15:22:36Z
| 2020-09-09T12:37:30Z
| 2020-09-09T12:37:30Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(
{"a": range(50), "b": range(0, 100, 2), "c": list(range(10)) * 5}
)
grouper = df.groupby("c")
grouper.get_group(0)
```
#### Problem description
This is quite weird and unfortunately I can't replicate it. The previous piece of code randomly returns one of the two options:
1)
| | a | b | c |
|---:|----:|----:|----:|
| 0 | 0 | 0 | 0 |
| 10 | 10 | 20 | 0 |
| 20 | 20 | 40 | 0 |
| 30 | 30 | 60 | 0 |
| 40 | 40 | 80 | 0 |
2)
| | a | b |
|---:|----:|----:|
| 0 | 0 | 0 |
| 10 | 10 | 20 |
| 20 | 20 | 40 |
| 30 | 30 | 60 |
| 40 | 40 | 80 |
If I run it now, I get option 1 (which I assume it's the expected one, since `Groupby.get_group` doesn't say anything about dropping the column used in the groupby https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.get_group.html)
However, a few minutes earlier I ran this
```python
[grouper.get_group(i).do_something_with_c_column() for i in grouper.groups.keys()]
```
And it raised a `KeyError`. Which is the right behavior? Can anyone else replicate this?
#### Output of ``pd.show_versions()``
<details>
numexpr.utils - INFO - NumExpr defaulting to 8 threads.
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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| 561,706,557
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MDU6SXNzdWU1NjE3MDY1NTc=
| 31,781
|
numpy.core._exceptions while resample using spline interpolation
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[] | null | 1
| 2020-02-07T15:28:28Z
| 2021-07-28T05:01:12Z
| 2021-07-28T05:01:12Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
time value1 value2 value3
run_id
19 00:00:00 14.45 1000.0 5.800000
19 00:00:00.010000 14.45 1000.0 5.591578
19 00:00:00.020000 14.45 1000.0 5.878844
19 00:00:00.030000 14.45 1000.0 5.974964
19 00:00:00.040000 14.45 1000.0 5.643659
... ... ... ... ...
20 00:00:00.647290 17.46 1500.0 5.695907
```
#### Problem description
I have a grouped timeseries from measurement data which looks like this. I now would like to increase the sample rate from 0.01s to 0.001s using the following code.
```python
for name, group in data.groupby('run_id'):
temp = group.reset_index().set_index('time').resample('0.001S').interpolate(method='spline', order=2)
```
This will end up in the following exception.
```python
numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'greater' input 1 from dtype('float64') to dtype('<m8[ns]') with casting rule 'same_kind'
```
Select a frequency for downsampling like this.
```python
temp = group.reset_index().set_index('time').resample('0.02S').interpolate(method='spline', order=2)
```
It seems to be working. The upsampling is working if I change the interpolation method to linear.
```python
temp = group.reset_index().set_index('time').resample('0.001S').interpolate(method='linear')
```
What I'm doing wrong here? I also tried an approach to apply the resampling directly to the groupby object like this.
```python
data.reset_index().set_index('time').groupby('run_id').resample('0.001S').interpolate(method='linear')
```
Does this make sense or do I need the apply() approach here?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.1
pytz : 2017.3
dateutil : 2.6.1
pip : 19.3.1
setuptools : 41.2.0
Cython : 0.27.3
pytest : 5.1.2
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.1.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 2.1.2
numexpr : 2.6.4
odfpy : None
openpyxl : 2.4.10
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.2.1
tables : None
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
</details>
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MDU6SXNzdWU1NjE3MDg3ODU=
| 31,782
|
Different behavior of numpy functions when used as a Python lambda function
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| 1
| 2020-02-07T15:32:27Z
| 2020-02-07T21:24:11Z
| 2020-02-07T21:23:43Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame().from_dict({"a": [1, 1, 1, 1], "b": [1, 2, 3, 4]})
df
a b
0 1 1
1 1 2
2 1 3
3 1 4
gdf = df.groupby("a")
gdf['b'].agg([("std1", lambda x: np.std(x))]) # ddof = 0
std1
a
1 1.118034
gdf['b'].agg([("std2", np.std)]) # ddof = 1
std2
a
1 1.290994
```
#### Problem description
To most Python/Pandas users, `np.std` and `lambda x: np.std(x)` should be interchangeable, as should any other method and its lambda equivalent. However, due to the implicit translation to `pd.std` that occurs in the latter example, these two calls use different default values of the `ddof` parameter, which can lead to real mismatches when refactoring code.
Given the discussion on closed Issue #13344, I gather that this is intended behavior, but it is nevertheless confusing. We may want to consider allowing for comparable output, at least as a default; if users want to override that behavior, they can explicitly provide a value for `ddof`.
#### Expected Output
BOTH aggregates output the `ddof = 1` calculation:
```
std2
a
1 1.290994
```
when no parameters are passed to the lambda.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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read_excel error for header=None and index_col as list
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| 0
| 2020-02-07T15:44:24Z
| 2020-07-10T13:54:00Z
| 2020-07-10T13:54:00Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
df = pd.read_excel('Book1.xlsx', header=None, index_col = [0, 1])
```
#### Problem description
Above leads to the following error. This seems to be related to the combination of header=None and index_col as a list. If I give an integer index_col, or leave out header=None (or specify a header) there is no error.
The only other reference to this issue I found was [this stack overflow question](https://stackoverflow.com/questions/48334347/read-excel-in-pandas-giving-error-for-no-header-and-multiple-index-cols).
I am seeing this in the latest release (1.0.1) as well as an older one (0.25.3). I haven't been able to install from master because I'm working on windows and don't have a compiler set up.
Example exel file (although the contents doesn't matter, it could be workbook with an empty sheet): [Book1.xlsx](https://github.com/pandas-dev/pandas/files/4171589/Book1.xlsx)
```
<redact>\lib\site-packages\pandas\io\excel\_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
332 convert_float=convert_float,
333 mangle_dupe_cols=mangle_dupe_cols,
--> 334 **kwds,
335 )
336
<redact>\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, converters, true_values, false_values, skiprows, nrows, na_values, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
883 convert_float=convert_float,
884 mangle_dupe_cols=mangle_dupe_cols,
--> 885 **kwds,
886 )
887
<redact>\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, dtype, true_values, false_values, skiprows, nrows, na_values, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
467 # Forward fill values for MultiIndex index.
468 if not is_list_like(header):
--> 469 offset = 1 + header
470 else:
471 offset = 1 + max(header)
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
```
#### Expected Output
A DataFrame with two column indexes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
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| 3
| 2020-02-07T15:54:02Z
| 2020-04-06T14:35:32Z
| 2020-04-06T14:33:38Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
import copy
import pandas as pd
import platform
print('Python Version', platform.python_version())
print('Pandas Version', pd.__version__)
a = [1.]
d = {}
d['a'] = a
df = pd.DataFrame(d)
print('Initial data frame')
print(df)
print('------')
\# This statement must be before the print statement
\# Removing this causes the bug to disappear
df['x'] = 0.0
\# Removing this causes the bug to disappear
print("df['a'].values=", df['a'].values)
print('-----')
\# Removing the copy causes the bug to disappear
\# copy.deepcopy(df) or df.copy() leads to the same result
\#df1 = copy.deepcopy(df)
df1 = df.copy()
\# set the first element of 'a' to value
value = -1.0
df['a'].values[0] = value
print("After setting df['a'].values[0] = %f" % (value))
print(df)
print('-------')
print("df['a'].values=", df['a'].values)
print('-------')
\# Removing this causes the bug to disappear
df['y'] = 0.0
print('Final')
print(df)
print('-------')
print("df['a'].values=", df['a'].values)
#### Problem description
First, we build a dataframe from a dictionary containing a list.
After that, we add a column, do add some print statements, copy the dataframe. The addition of the column and print statement are crucial to the bug showing up. The fact that the print statement matters leads me to believe that it's memory issue.
The critical point is that we set df['a'].values[0] = -1.0 which is somehow not permanent.
Lastly we add another column. This column addition is necessary for the issue to occur.
In short the issue looks like this:
a = [1.0]
df = pd.DataFrame({'a': a})
df['x'] = 0.0 # necessary for bug
print(df['a'].values) # necessary for bug
df1 = df.copy()
df['a'].values[0] = -1.0 # this is the statement which is ignored
print(df['a'].values)
print(df)
df['y'] = 0.0 # necessary for bug
print(df['a'].values)
At this point, we'd expect df['a'].values = [-1.0], but in fact df['a'].values == 1.0
I've tested this issue on a wide variety of versions. The last version where it works as expected is 0.16.2 and the version with the unexpected result is 0.17.1. I've tried this with python 3.6 / pandas=1.0.0 on Linux and python 3.7 / pandas 1.0.1 on OS X and get the same result.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.15.1
pytz : 2019.3
dateutil : 2.7.3
pip : 18.0
setuptools : 40.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
</details>
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MDU6SXNzdWU1NjE3NTQwMTI=
| 31,785
|
Crash when using align between a Series and a DataFrame and ffill (or bfill) method
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| 3
| 2020-02-07T16:49:59Z
| 2020-02-09T17:12:59Z
| 2020-02-09T17:12:59Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
x = pd.Series(range(10), index=range(10))
y = pd.DataFrame(0.0, index=range(20), columns=range(12))
# works fine (no fill method)
x.align(y)
# crash
x.align(y, method='ffill')
# works fine if x is converted to a DataFrame
x.to_frame().align(y, method='ffill')
```
#### Problem description
This problem only occurs on pandas version >= 1.0.0
Pretty simple bug to reproduce... Quite a serious issue! It should probably shows elsewhere also.
**When using align between a Series and a DataFrame with a fill method the code hits this
assert isinstance(obj, type(self)), type(obj)
AssertionError: <class 'pandas.core.frame.DataFrame'>**
My guess is that isinstance(obj, type(self)) try to assert that a Series is a DataFrame which is not the case.
#### Expected Output
Similar to x.align(y), with forward filled values.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyNTM2ODYw
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REF: remove iloc case from _convert_slice_indexer
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| 2
| 2020-02-07T18:12:27Z
| 2020-02-10T20:10:01Z
| 2020-02-09T17:06:36Z
|
MEMBER
| null |
_convert_slice_indexer is turning out to be one of the stickier methods to figure out, xref #31658.
The case with kind=None is only called from one place in core.indexing, and that is not reached in the tests. I'd like to either a) figure out a non-None kind to pass to it, or b) determine that it can never be reached so can be removed. Any thoughts on this are very welcome.
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plotting backends doesn't plot for the first time after importing pandas
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| 2020-02-07T18:28:42Z
| 2020-02-15T05:07:15Z
| 2020-02-15T05:07:15Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.set_option('plotting.backend', 'matplotlib')
pd.Series([1, 2, 3]).plot()
```
```python
import pandas as pd
pd.options.plotting.backend = 'matplotlib'
pd.Series([1, 2, 3]).plot()
```
```python
import pandas as pd
pd.Series([1, 2, 3]).plot(backend='matplotlib')
```
#### Problem description
If you use any of the above three options to plot. for the first time, it will just return something like `<matplotlib.axes._subplots.AxesSubplot at 0x7f6b28a75ac8>
`. And then, from the next time onwards it will plot the graph. Even if you use one option after another then also from the second time onwards it works fine.
#### Expected Output
It should plot the graph for the first time also.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.20.5-042005-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 1.0.0rc0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.0.1
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.44.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyNTUwNDc0
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|
BUG: Too aggressive typing in NDFrame.align
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| 6
| 2020-02-07T18:50:27Z
| 2020-02-12T21:49:41Z
| 2020-02-09T17:12:59Z
|
CONTRIBUTOR
| null |
- [x] closes #31785
The type checking was too aggressive. ``right`` has type ``Any``, so the wrapping in ``_ensure_type`` should not be done.
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MemoryError when using series.rolling().corr(other) with >1.0
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| 1
| 2020-02-07T19:24:00Z
| 2020-02-10T17:51:32Z
| 2020-02-10T17:51:32Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
srs1 = pd.Series(np.random.rand(11521),pd.date_range('2019-08-15', '2019-08-23',freq='1T'))
srs2 = pd.Series(np.random.rand(11521),pd.date_range('2019-08-15', '2019-08-23',freq='1T'))
srs1.rolling(pd.to_timedelta("12H")).corr(srs2)
```
#### Problem description
Running the code above results in the following error `Unable to allocate 314. TiB for an array with shape (43200000000000,) and data type int64` on pandas 1.0.1. Confirmed that this used to work on pandas 0.25.3.
#### Expected Output
The correct calculations
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : 0.13.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyNTcyNzQz
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|
TST: Making use of tm.external_error_raised
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| 2020-02-07T19:47:34Z
| 2020-02-29T10:32:49Z
| 2020-02-20T02:21:16Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
After #31130 got merged, I can do what I couldn't do [here](https://github.com/pandas-dev/pandas/pull/30998#discussion_r368177775)
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MDExOlB1bGxSZXF1ZXN0MzcyNTkzMTcx
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|
CI: fix feather test
|
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MDExOlB1bGxSZXF1ZXN0MzcyNTk0OTky
| 31,792
|
Some code cleanups
|
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- [ ] closes #xxxx
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NjE4ODIyMDk=
| 31,793
|
Difference between two Timestamp with different timezone object type fails
|
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[] | null | 11
| 2020-02-07T21:30:34Z
| 2021-07-28T05:02:29Z
| null |
NONE
| null |
I encountered an issue while calculating the difference between two timestamps
of different origin. The first timestamp comes from a function using the `arrow`
package. The timestamp is in UTC timezone. The other timestamp comes for another
package returning a `pandas.DataFrame`, also with UTC timestamps. An exception is
raised when trying to get the difference between the two timestamps. Here follow
some come that reproduces the error, with some details about the respective
time zones.
```python
import arrow
import pandas
def info(name, thing):
print(f"{name} | tzinfo: {thing.tzinfo} | tzinfo type: {type(thing.tzinfo)}")
arrow_now = arrow.utcnow()
info("arrow.utcnow()", arrow_now)
arrow_now_datetime = arrow_now.datetime
info("arrow.utcnow().datetime", arrow_now_datetime)
timestamp_from_datetime_from_arrow_now = pandas.Timestamp(arrow_now_datetime)
info(
"pandas.Timestamp(arrow.utcnow().datetime)", timestamp_from_datetime_from_arrow_now
)
pandas_now_timestamp = pandas.Timestamp.utcnow()
info("pandas.Timestamp.utcnow()", pandas_now_timestamp)
```
Running the above code produces:
```
arrow.utcnow() | tzinfo: tzutc() | tzinfo type: <class 'dateutil.tz.tz.tzutc'>
arrow.utcnow().datetime | tzinfo: tzutc() | tzinfo type: <class 'dateutil.tz.tz.tzutc'>
pandas.Timestamp(arrow.utcnow().datetime) | tzinfo: tzutc() | tzinfo type: <class 'dateutil.tz.tz.tzutc'>
pandas.Timestamp.utcnow() | tzinfo: UTC | tzinfo type: <class 'pytz.UTC'>
```
Both timestamps are in the same timezone, but the timezone object itself comes from different packages. Now when trying to get the difference between the two timestamps:
`timestamp_from_datetime_from_arrow_now - pandas_now_timestamp`
I get the following error:
`TypeError: Timestamp subtraction must have the same timezones or no timezones`
I would expect that if the Timestamp objects are initialized without error,
which is the case, that they are valid object that can work with each other,
independently from what were the timestamps build with. It's not the case.
#### System Info
<details>
- system: Ubuntu 19.04 disco
- python version: 3.7.4
- arrow version: 0.15.5
- pandas version: 1.0.0
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-38-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 6.0.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyNjEyMDU1
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BUG: Avoid casting Int to object in Categorical.from_codes
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| 2020-02-07T21:37:33Z
| 2020-02-12T15:34:46Z
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|
MEMBER
| null |
- [x] closes #31779
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzcyNjE4OTgz
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ENH: add from_dummies
|
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[] | null | 7
| 2020-02-07T21:59:08Z
| 2020-10-10T14:14:50Z
| 2020-05-26T09:43:37Z
|
MEMBER
| null |
Just a first draft
Will close #8745
Screenshot of examples
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|
ENH: add Series.info
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[] | null | 10
| 2020-02-07T22:12:38Z
| 2020-10-10T14:14:44Z
| 2020-10-07T16:39:28Z
|
MEMBER
| null |
- [x] initial draft to close #5167
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
screenshot of the example
------
Note to self: this change from `ba72b59b44f15076877e784e16cbc66aa03e0adc` breaks the tests
```diff
- # groupby dtype.name to collect e.g. Categorical columns
- counts = data.dtypes.value_counts().groupby(lambda x: x.name).sum()
+ counts = data._data.get_dtype_counts()
```
----
### Updated
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MDExOlB1bGxSZXF1ZXN0MzcyNjI3NDA1
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|
CLN: assorted indexing-related cleanups
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| 3
| 2020-02-07T22:26:40Z
| 2020-02-10T16:20:43Z
| 2020-02-10T10:39:25Z
|
MEMBER
| null |
- _setitem_with_indexer isnt consistent about whether or not it returns anything, make it always-None
- avoid using private loc method from DataFrame
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| 31,798
|
Failed to create the storer for xarray on HDFStore
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[] | null | 2
| 2020-02-07T23:45:06Z
| 2021-07-28T05:08:54Z
| 2021-07-28T05:08:53Z
|
NONE
| null |
When The code below tried to store into a hdf in case of xarray DataArray but it cased an error. Other datatypes
```python
import pandas as pd
import xarray as xr
import tempfile
def test_hdfstore():
with tempfile.NamedTemporaryFile('r', suffix='.hdf') as f:
with pd.HDFStore(f.name, "a") as store:
obj = xr.DataArray([[1,2],[3,4]],
dims=('x', 'y'),
coords={'x': ['A', 'B'], 'y':['a','b']})
store['my_obj'] = obj
```
#### Problem description
When a DataArray was tried to be stored in an HDF5 file but it caused an error like this:
```
File "C:\ProgramData\Anaconda3\envs\python37\lib\site-packages\pandas\io\pytables.py", line 556, in __setitem__
self.put(key, value)
File "C:\ProgramData\Anaconda3\envs\python37\lib\site-packages\pandas\io\pytables.py", line 1042, in put
errors=errors,
File "C:\ProgramData\Anaconda3\envs\python37\lib\site-packages\pandas\io\pytables.py", line 1682, in _write_to_group
s = self._create_storer(group, format, value, encoding=encoding, errors=errors)
File "C:\ProgramData\Anaconda3\envs\python37\lib\site-packages\pandas\io\pytables.py", line 1548, in _create_storer
raise TypeError("value must be None, Series, or DataFrame")
TypeError: value must be None, Series, or DataFrame
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.15.0
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyNjUxNzQ1
| 31,799
|
Added pd.NA to nulls_fixture
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| 7
| 2020-02-08T00:09:08Z
| 2020-02-20T04:51:36Z
| 2020-02-20T04:51:08Z
|
MEMBER
| null |
@jorisvandenbossche
I think we should do this. Simply xfailed issues up for discussion on how to resolve
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MDExOlB1bGxSZXF1ZXN0MzcyNjU0NTM3
| 31,800
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CI Fix w/o tm helper
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| 2020-02-08T00:24:11Z
| 2020-02-14T16:55:07Z
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MEMBER
| null |
Alternate to #31791
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| 561,939,938
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MDU6SXNzdWU1NjE5Mzk5Mzg=
| 31,801
|
to_json index with Null Value Broken in 1.0
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| 2020-05-08T21:24:26Z
| null |
MEMBER
| null |
Discovered by @ArtificialQualia in #31748
Previous behavior on 0.25.3:
```python
>>> pd.Series([1], index=[np.nan]).to_json()
'{"null":1}'
>>> pd.Series([1], index=[pd.NaT]).to_json()
'{"null":1}'
>>> pd.Series([1], index=[None]).to_json()
'{"null":1}'
```
And now on master:
```python
>>> pd.Series([1], index=[np.nan]).to_json()
'{"nan":1}'
>>> pd.Series([1], index=[pd.NaT]).to_json()
'{"null":1}'
>>> pd.Series([1], index=[None]).to_json()
'{"None":1}'
```
Note that pd.NA also just has its str representation written out:
```python
>>> pd.Series([1], index=[pd.NA]).to_json()
'{"<NA>":1}'
```
pd.NaT still works because of this logic:
https://github.com/pandas-dev/pandas/blob/6d3cc14fc52685511329cedf1e11d15651ac6a8e/pandas/_libs/src/ujson/python/objToJSON.c#L1455
So probably need to generalize for other "null" values
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REGR: cumsum regression with groupby call to agg
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| 2020-02-08T01:30:16Z
| 2020-03-11T18:34:48Z
| 2020-03-11T18:34:48Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
I want to define a custom function that I can pass to the `agg` method. It uses the `cumsum` method, which appears to be problematic recently.
```python
# Your code here
import pandas as pd
def max_test(s):
return s.cumsum().max()
#return s.max()
dummy_data = pd.DataFrame(
{'AIRLINE': {0: 'WN', 1: 'UA', 2: 'MQ', 3: 'AA', 4: 'WN'},
'ORG_AIR': {0: 'LAX', 1: 'DEN', 2: 'DFW', 3: 'DFW', 4: 'LAX'},
'DIST': {0: 590, 1: 1452, 2: 641, 3: 1192, 4: 1363}})
gb = dummy_data.groupby(['AIRLINE', 'ORG_AIR'])
result = gb.agg(
#'max'
max_test
)
print(result)
```
#### Problem description
Prior to Pandas 1.0rc this worked. It now raises an exception:
```
$ python /tmp/regpandas.py
Traceback (most recent call last):
File "/tmp/regpandas.py", line 16, in <module>
max_test
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 948, in aggregate
return self._python_agg_general(func, *args, **kwargs)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 936, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 641, in agg_series
return self._aggregate_series_fast(obj, func)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 666, in _aggregate_series_fast
result, counts = grouper.get_result()
File "pandas/_libs/reduction.pyx", line 376, in pandas._libs.reduction.SeriesGrouper.get_result
File "pandas/_libs/reduction.pyx", line 193, in pandas._libs.reduction._BaseGrouper._apply_to_group
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 913, in <lambda>
f = lambda x: func(x, *args, **kwargs)
File "/tmp/regpandas.py", line 4, in max_test
return s.cumsum().max()
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/generic.py", line 11331, in cum_func
result = self._data.apply(na_accum_func)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 440, in apply
applied = b.apply(f, **kwargs)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 403, in apply
result = self.make_block(values=_block_shape(result, ndim=self.ndim))
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 273, in make_block
return make_block(values, placement=placement, ndim=self.ndim)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 3041, in make_block
return klass(values, ndim=ndim, placement=placement)
File "/Users/matt/.env/pandas1/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 125, in __init__
f"Wrong number of items passed {len(self.values)}, "
ValueError: Wrong number of items passed 2, placement implies 1
```
#### Expected Output
```
$ python /tmp/regpandas.py
DIST
AIRLINE ORG_AIR
AA DFW 1192
MQ DFW 641
UA DEN 1452
WN LAX 1953
```
#### Output of ``pd.show_versions()``
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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MDU6SXNzdWU1NjE5NjU2NzY=
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DEPR: df.loc(axis)
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| 2020-02-08T03:56:53Z
| 2020-02-11T15:45:23Z
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|
MEMBER
| null |
Internally we only use this in a couple of places, so it would be easy to remove. It shows up in user_guide/advanced and in the 0.14.0 release notes.
Without it we could reduce some complexity in the indexing code.
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Backport PR #31748: BUG: Fixed encoding of pd.NA with to_json
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Backport PR #31748
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df.loc[0] = row, datatype changes
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| 2020-02-08T09:28:34Z
| 2021-07-28T05:10:42Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from numpy import NaN
row = [2015, 1, 7.0, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.4, NaN, NaN, NaN]
print([type(i) for i in row])
columns = ['year', 'month', 'day', '平均風速', '最大風速', '最大風向', '最大風速時間', '最大瞬間風速',
'最大瞬間風向','最大瞬間時間', '最多風向', '日照時間', '降雪', '最深積雪値', '最深積雪時間']
# Sorry some of the columns are in Japanese, but I believe it doesn't matter.
row_df = pd.DataFrame(columns=columns)
row_df.loc[0] = row
print(row_df.dtypes)
```
My problem is, after running
```python
row_df.loc[0] = row
```
,the datatypes change
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.8.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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Series.str.contains Question
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NONE
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#### Not contains Question
```python
# df is a DataFrame contains column "title", then I filter df by column "title" below, it works.
useless = df[df["title"].str.contains(r"[\d]+[册|本]", regex=True)]
# but I want a not contains value below, it raise error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
useless = df[not df["title"].str.contains(r"[\d]+[册|本]", regex=True)]
```
#### Problem description
How can I get a not contains value?
|
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Not contains Question
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| 2020-02-08T10:58:47Z
| 2020-02-08T13:07:49Z
| 2020-02-08T13:07:49Z
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NONE
| null |
#### Not contains Question
```python
# df is a DataFrame contains column "title", then I filter df by column "title" below, it works.
useless = df[df["title"].str.contains(r"[\d]+[册|本]", regex=True)]
# but I want a not contains value below, it raise error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
useless = df[not df["title"].str.contains(r"[\d]+[册|本]", regex=True)]
```
#### Problem description
How can I get a not contains value?
|
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MDExOlB1bGxSZXF1ZXN0MzcyNzE1ODU4
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CLN: some code cleanups in pandas/_libs/
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: add 'origin' and 'offset' arguments to 'resample' and 'pd.Grouper'
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| 36
| 2020-02-08T19:11:11Z
| 2020-05-10T16:45:31Z
| 2020-05-10T15:52:55Z
|
MEMBER
| null |
**EDIT:** this PR has changed, now instead of adding `adjust_timestamp` we are adding `origin` and `offset` arguments to `resample` and `pd.Grouper` (see https://github.com/pandas-dev/pandas/pull/31809#issuecomment-583884772)
----
Hello,
This enhancement is an alternative to the `base` argument present in `pd.Grouper` or in the method `resample`. It adds the `adjust_timestamp` argument to change the current behavior of: https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L1728
- `adjust_timestamp` is the timestamp on which to adjust the grouping. If None is passed, the first day of the time series at midnight is used.
Currently the bins of the grouping are adjusted based on the beginning of the day of the time series starting point. This works well with frequencies that are multiples of a day (like `30D`) or that divides a day (like `90s` or `1min`). But it can create inconsistencies with some frequencies that do not meet this criteria.
Here is a simple snippet from a test that I added that proves that the current behavior can lead to some inconsistencies. Inconsistencies that can be fixed if we use `adjust_timestamp`:
```python
import pandas as pd
import numpy as np
import pandas._testing as tm
import pytest
freq = "1399min" # prime number that is smaller than 24h
start, end = "1/1/2000 00:00:00", "1/31/2000 00:00"
middle = "1/15/2000 00:00:00"
rng = pd.date_range(start, end, freq="1231min") # prime number
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts2 = ts[middle:end]
# proves that grouper without a fixed adjust_timestamp does not work
# when dealing with unusual frequencies
simple_grouper = pd.Grouper(freq=freq)
count_ts = ts.groupby(simple_grouper).agg("count")
count_ts = count_ts[middle:end]
count_ts2 = ts2.groupby(simple_grouper).agg("count")
with pytest.raises(AssertionError):
tm.assert_index_equal(count_ts.index, count_ts2.index)
# test adjusted_timestamp on 1970-01-01 00:00:00
adjust_timestamp = pd.Timestamp(0)
adjusted_grouper = pd.Grouper(freq=freq, adjust_timestamp=adjust_timestamp)
adjusted_count_ts = ts.groupby(adjusted_grouper).agg("count")
adjusted_count_ts = adjusted_count_ts[middle:end]
adjusted_count_ts2 = ts2.groupby(adjusted_grouper).agg("count")
tm.assert_series_equal(adjusted_count_ts, adjusted_count_ts2)
```
I think this PR is ready to be merged, but I am of course open to any suggestions or criticism. :wink:
For instance, I am not sure if the naming of `adjust_timestamp` is correct. An alternative could be `base_timestamp` or `ref_timestamp` :thinking:?
Cheers,
----
- [X] closes #25226
closes #28302
closes #28675
closes #4197
closes #8521
- [X] Add 'origin' and 'offset' arguments to 'resample' and 'pd.Grouper'
- [X] tests added / passed
- [X] Add deprecation warning for `loffset` and `base` in the code
- [X] Add deprecation warning for `loffset` and `base` in the doc
- [x] Add examples in the doc for `origin` and `offset`
- [x] whatsnew entry (add deprecation notice with `offset` example)
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NjIwNjk5OTU=
| 31,810
|
BUG/API: df.loc[a, b] = c allows positional slice for a
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| 2020-02-08T19:59:33Z
| 2020-02-10T01:54:02Z
| 2020-02-10T01:54:02Z
|
MEMBER
| null |
```
dti = pd.date_range("2016-01-01", periods=5)
df = pd.DataFrame(np.random.randn(5, 2), index=dti, columns=["A", "B"])
>>> df.loc[4:6, "A"]
TypeError: cannot do slice indexing on DatetimeIndex with these indexers [4] of type int
>>> df.loc[4:6, "A"] = 0
# works as if we did df.iloc[4:6, 0] = 0
```
In this case there are plenty of alternatives that amount to doing `df["A"].iloc[4:6] = 0`, but this isnt robust to mixed-dtype frames, so I'm not sure if we have a supported way of doing this correctly.
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| 31,811
|
groupby returns non-pertinent combinations in result
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| 2020-02-08T22:43:04Z
| 2020-02-09T17:29:06Z
| 2020-02-09T17:29:06Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
d1 = ['foo', 'bar'] * 50
d2 = list('ABCD' * 25)
d3 = pd.date_range('2019-01-01', periods=100, freq='D')
d4 = list(range(1, 101, 1))
d5 = list(range(1000, 100001, 1000))
df = pd.DataFrame(dict(col1=d1, col2=d2, col3=d3, col4=d4, col5=d5))
bins = [0, 30, float('inf')]
grouped = df.groupby(['col1', 'col2', pd.Grouper(key='col3', freq='MS'),
pd.cut(df.col4, bins)])
summary = grouped['col5'].sum()
```
#### Problem description
The groupby() function returns a result that has every possible combination of the grouping criteria, and shows 'NaN' for non-significant combinations (see screenshot of the results below). This behavior was not observed in pandas version 0.24.2. Earlier version only returned combinations with values (ie, combinations with NaN values were not included in the result.)
#### Expected Output
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
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CLN: tests.indexing.common
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MEMBER
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A lot of code in there not doing anything, AFAICT leftover from ix cross-comparisons.
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MEMBER
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- [ ] closes #xxxx
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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also get the exceptions/messages in test_setitem_ndarray_3d to be maximally-specific
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REF: use public indexers in groupby.ops
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check parser_dates names in columns
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| 5
| 2020-02-09T02:54:54Z
| 2020-03-03T04:04:54Z
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|
CONTRIBUTOR
| null |
if column names are passed in parser_dates, make sure those
columns exist in dataframe.
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