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BUG: setting with iloc and no labels does not effect certain data types
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| 2020-05-16T22:09:57Z
| 2021-08-07T06:09:38Z
| null |
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, dtype="int64")
df.iloc[:, 0:1] = df.iloc[:, 0:1].astype("int32").values
print(df.dtypes)
```
Column "a" remains an int64 whereas it should be int32. This is similar to #33198, however the resolution there (taking the split path in _setitem_with_indexer) is not sufficient in this case where there are no labels. The issue appears to be the function
https://github.com/pandas-dev/pandas/blob/9f7b9fce94476c973652c7e42293c2e5086287ef/pandas/core/dtypes/cast.py#L86-L95
values enters here as int32 and gets converted to int64 in the call to maybe_convert_objects. Indeed, adding the line
values = values.astype(np.int32)
after the call to maybe_convert_objects gives the right output.
Should maybe_convert_objects take into account int32 vs int64, float32 vs float64, and others?
One last note: even if maybe_convert_objects returned int32 here, it would not resolve #33198.
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CLN: simplify freq/nanos checks
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CONTRIBUTOR
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ENH: Implement xlabel and ylabel options in Series.plot and DataFrame.plot
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BUG: pd.DataFrame.transform recursively loops in some cases
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| 2020-05-17T13:01:45Z
| 2020-06-03T18:05:38Z
| 2020-06-03T18:05:38Z
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.DataFrame({"a":[None]}).transform({"a":int})
```
#### Problem description
Executing the above causes recursion depth limit exception. This is confusing and it is harder to pinpoint/debug than the expected exception.
#### Expected Output
Something akin to the output of `int(None)`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 3.10.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.10.1
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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| 619,709,850
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MDU6SXNzdWU2MTk3MDk4NTA=
| 34,225
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BUG: Use of PeriodIndex with rolling transform aggregating function into cumulative aggregating?
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| 2020-10-03T00:59:34Z
| 2020-10-03T00:59:34Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
start = '2020-01-01 08:00'
end = '2020-01-01 12:00'
intervals = pd.period_range(start=start, end=end, freq = '30T')
values = [i for i in range(0, len(intervals))]
ser = pd.Series(values, index = intervals)
offset = pd.tseries.frequencies.to_offset('1h')
test_min = ser.rolling(window = offset, closed='left').min()
test_sum = ser.rolling(window = offset, closed='left').sum()
test_max = ser.rolling(window = offset, closed='left').max()
```
#### Output
```python
# Problem 1
test_min
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 0.0
2020-01-01 09:00 0.0
2020-01-01 09:30 0.0
2020-01-01 10:00 0.0
2020-01-01 10:30 0.0
2020-01-01 11:00 0.0
2020-01-01 11:30 0.0
2020-01-01 12:00 0.0
Freq: 30T, dtype: float64
```
```python
# Problem 1
test_sum
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 0.0
2020-01-01 09:00 1.0
2020-01-01 09:30 3.0
2020-01-01 10:00 6.0
2020-01-01 10:30 10.0
2020-01-01 11:00 15.0
2020-01-01 11:30 21.0
2020-01-01 12:00 28.0
Freq: 30T, dtype: float64
```
```python
# Problem 2
test_max
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 0.0
2020-01-01 09:00 1.0
2020-01-01 09:30 2.0
2020-01-01 10:00 3.0
2020-01-01 10:30 4.0
2020-01-01 11:00 5.0
2020-01-01 11:30 6.0
2020-01-01 12:00 7.0
Freq: 30T, dtype: float64
```
#### Problem descriptions
There are 2 problems:
* problem 1:
* min value on a `1H` window is not 0 given the input provided (see expected output)
* 2nd example with sum helps understand what happens with min: rolling is actually operating cumulated sum and cumulated min?!
* problem 2: the 0 at the second row in `test_max` shows that rolling with closed = 'left' is not able to handle PeriodIndex correctly. It seems it does not know that periods are themselves bins with a specific close parameter to be considered.
#### Expected Output
```python
# Problem 1
test_min
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 0.0
2020-01-01 09:00 1.0
2020-01-01 09:30 2.0
2020-01-01 10:00 3.0
2020-01-01 10:30 4.0
2020-01-01 11:00 5.0
2020-01-01 11:30 6.0
2020-01-01 12:00 7.0
Freq: 30T, dtype: float64
```
```python
# Problem 1
test_sum
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 1.0
2020-01-01 09:00 3.0
2020-01-01 09:30 5.0
2020-01-01 10:00 7.0
2020-01-01 10:30 9.0
2020-01-01 11:00 11.0
2020-01-01 11:30 13.0
2020-01-01 12:00 15.0
Freq: 30T, dtype: float64
```
```python
# Problem 2
test_max
```
```python
2020-01-01 08:00 NaN
2020-01-01 08:30 1.0
2020-01-01 09:00 2.0
2020-01-01 09:30 3.0
2020-01-01 10:00 4.0
2020-01-01 10:30 5.0
2020-01-01 11:00 6.0
2020-01-01 11:30 7.0
2020-01-01 12:00 8.0
Freq: 30T, dtype: float64
```
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.0.3
numpy : 1.16.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
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 : 0.48.0
</details>
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MDU6SXNzdWU2MTk3MTE3NjE=
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ENH: Add VariableForwardWindowIndexer
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[] | null | 3
| 2020-05-17T14:04:39Z
| 2020-09-11T06:07:23Z
| 2020-09-11T06:07:23Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to use the rolling() method with looking-forward window using a negative `window`.
I don't remember which other pandas function is using this approach of negative window for reversing the position of the window with respect to the actual index, but it is for the user very convenient.
I was convinced it was existing for rolling, but it appears it is not.
#### Describe the solution you'd like
```python
# Definition of a negative offset (this works)
offset = pd.tseries.frequencies.to_offset('-1h')
# Use it in rolling (this does not work)
start = '2020-01-01 08:00'
end = '2020-01-01 12:00'
intervals = pd.period_range(start = start, end=end, freq = '30T')
values = [i for i in range(0, len(intervals))]
ser = pd.Series(values, index = intervals)
test = ser.rolling(window = offset, closed='left').min()
```
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REF: make Tick entirely a cdef class
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| 3
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MEMBER
| null |
Sits on top of #34167
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DOC: Validation script doesn't report that the docstring is correct
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| 11
| 2020-05-17T22:08:37Z
| 2020-05-22T10:00:22Z
| 2020-05-22T09:41:38Z
|
MEMBER
| null |
When executing for example:
```
./scripts/validate_docstrings.py pandas.DataFrame.head
```
The script, after the docstring, should report:
```
################################################################################
################################## Validation ##################################
################################################################################
Docstring for DataFrame.head correct. :)
```
But for some reason, the sentence `Docstring for DataFrame.head correct. :)` is not being printed.
This is the line that is not being executed: https://github.com/pandas-dev/pandas/blob/master/scripts/validate_docstrings.py#L364
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DOC: Improve docstring of Series/DataFrame.bool
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| 2
| 2020-05-17T22:14:29Z
| 2020-06-22T06:32:50Z
| 2020-06-22T06:32:44Z
|
MEMBER
| null |
Not a big fan of this method, I'd deprecate it, in favor of `bool(df.squeeze())`. But since it's quite tricky, let's at least have the docstring as clear as possible.
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CI: Fix lint error on master
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| 1
| 2020-05-18T04:48:33Z
| 2020-05-18T07:05:58Z
| 2020-05-18T07:00:33Z
|
MEMBER
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH: skip_blank_columns in read_csv
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[] | null | 1
| 2020-05-18T04:52:01Z
| 2020-12-29T10:36:44Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
If the `names` parameter in `read_csv` has more elements than the number of columns in the input file, then the returned DataFrame has `NaN` columns for the extra elements.
#### Describe the solution you'd like
Add a `skip_blank_columns` parameter in `read_csv` (similar to `skip_blank_lines`) to avoid creating the `NaN` columns.
#### API breaking implications
An additional parameter in `read_csv` should not break anything.
#### Describe alternatives you've considered
`read_csv` can be followed by `dropna()` to achieve this, but `skip_blank_columns` would complement the `skip_blank_lines` functionality currently in `read_csv`.
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DOC: `pandas.read_csv`: The word `to_csv` in "See also" is not linked.
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| 3
| 2020-05-18T05:00:30Z
| 2020-05-18T06:58:34Z
| 2020-05-18T06:58:20Z
|
NONE
| null |
#### Location of the documentation
[pandas.read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)
* Document Version: 1.1.0.dev0+1599.g0cb7a1fd4
#### Documentation problem
The word `to_csv` in "See also" is not linked.
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| 34,233
|
REF: do length-checks in boilerplate decorator
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| 2020-05-18T06:10:31Z
| 2021-08-07T06:11:08Z
| 2021-08-07T06:11:07Z
|
MEMBER
| null |
The PR #34081 was reverted due to its impact on length-checks in Index special cases with tuples.
And the refactor is still nice for the cleanup but needs to have appropriate changes to avoid this issue.
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| 34,234
|
BUG: `astype` on index drops name
|
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| 2020-05-18T10:38:08Z
| 2020-05-18T18:59:41Z
| 2020-05-18T18:59:41Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Index name is dropped when using `astype`
#### Code Sample, a copy-pastable example
```python
import pandas as pd
index = pd.Float64Index(range(10), name='my_index')
int_index = index.astype(int)
print(int_index.name) # doesn't print anything
```
#### Problem description
From the docs of `astype`, I wouldn't expect the `name` attribute to be dropped:
> Cast a pandas object to a specified dtype ``dtype``.
#### Expected Output
`map` works.
```python
int_index = index.map(int)
print(int_index.name) # prints original index name
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.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 : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
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 : 5.4.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0
</details>
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BUG: pandas.groupby([A, B]).sum(level='B') gives cython_agg_general() error
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| 2020-05-18T11:32:33Z
| 2020-05-21T18:48:45Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
A = [1, 2, 3]
B = [5, 5, 5]
C = [3, 2, 1]
df = pd.DataFrame(data={'A': A, 'B': B, 'C': C})
print("Method 1:\n", df.groupby(['A', 'C']).agg({'C': 'sum'})) # works
print("Method 2:\n", df.groupby(['A', 'C']).sum(level='C')) # gives error
```
#### Problem description
Last time I ran my code I could use the keyword _level_ to do statistics over a particular multiindex. Now, I get an error. Using agg is a workaround, but I'll have to rewrite quite a lot of code then. I couldn't find anything about the level kwarg being deprecated. Current output:
> Traceback (most recent call last):
>
> File "C:\Users\kahy\OneDrive - Skogforsk\Documents\Projekt\Översikt virkesmätning\Code\bugtest.py", line 19, in <module>
> print("Method 2:\n", df.groupby(['A', 'C']).sum(level='C')) # gives error
>
> File "C:\Program Files\Miniconda3\envs\kari_env\lib\site-packages\pandas\core\groupby\groupby.py", line 1371, in f
> return self._cython_agg_general(alias, alt=npfunc, **kwargs)
>
> TypeError: _cython_agg_general() got an unexpected keyword argument 'level'
> #### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : Swedish_Sweden.1252
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
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 : None
None
</details>
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BUG: Fix validation script not printing docstring is correct (#34228)
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| 3
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| 2020-05-22T09:41:49Z
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|
CONTRIBUTOR
| null |
- [x] closes #34228
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 3
| 2020-05-18T13:57:10Z
| 2020-06-01T01:41:17Z
| 2020-06-01T01:41:17Z
|
NONE
| null |
In the docs it says:
```code
Supports xls, xlsx, xlsm, xlsb, and odf file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets.
```
When I try to read a xlsb file with specifying engine='openpyxl'
I get the next error:
```code
df = pd.read_excel(r"file.xlsb",engine='openpyxl')
```
```code
openpyxl does not support binary format .xlsb, please convert this file to .xlsx format if you want to open it with openpyxl
```
I do have the openpyxl module installed
Thank you
|
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| 34,238
|
API: implement ObjectIndex to take place of Index
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| 2020-05-18T14:51:54Z
| 2020-05-21T01:19:44Z
| 2020-05-21T01:19:44Z
|
MEMBER
| null |
Then we would never actually return an Index object.
We could then make ABCIndex behave like ABCIndexClass, and get rid of the latter.
Briefly discussed in #34159
|
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|
CI: ValueError: Must pass 2-d input in constructor
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| 2020-05-18T19:52:32Z
| 2020-05-18T19:54:44Z
| 2020-05-18T19:54:44Z
|
MEMBER
| null |
seems df construction fail at some special cases, have seen the errors in multiple PRs
```
@pytest.mark.xfail(_is_numpy_dev, reason="Interprets list of frame as 3D")
def test_constructor_list_frames(self):
# see gh-3243
> result = DataFrame([DataFrame()])
pandas/tests/frame/test_constructors.py:153:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/frame.py:514: in __init__
mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
pandas/core/internals/construction.py:190: in init_ndarray
values = _prep_ndarray(values, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
values = array([], shape=(1, 0, 0), dtype=float64), copy = False
def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
if not isinstance(values, (np.ndarray, ABCSeries, Index)):
if len(values) == 0:
return np.empty((0, 0), dtype=object)
elif isinstance(values, range):
arr = np.arange(values.start, values.stop, values.step, dtype="int64")
return arr[..., np.newaxis]
def convert(v):
return maybe_convert_platform(v)
# we could have a 1-dim or 2-dim list here
# this is equiv of np.asarray, but does object conversion
# and platform dtype preservation
try:
if is_list_like(values[0]) or hasattr(values[0], "len"):
values = np.array([convert(v) for v in values])
elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
# GH#21861
values = np.array([convert(v) for v in values])
else:
values = convert(values)
except (ValueError, TypeError):
values = convert(values)
else:
# drop subclass info, do not copy data
values = np.asarray(values)
if copy:
values = values.copy()
if values.ndim == 1:
values = values.reshape((values.shape[0], 1))
elif values.ndim != 2:
> raise ValueError("Must pass 2-d input")
E ValueError: Must pass 2-d input
```
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BUG: PeriodIndex.get_loc should raise KeyError
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| 2020-09-01T23:32:01Z
| 2020-09-01T23:32:01Z
|
CONTRIBUTOR
| null |
Currently PeriodIndex.get_loc raises a ValueError rather than a KeyError.
```python
In [11]: s = pd.Series([1, 2, 3], index=pd.period_range('2000', periods=3, name='A'))
In [12]: s.index.get_loc('A')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-3a472f211c46> in <module>
----> 1 s.index.get_loc('A')
~/sandbox/pandas/pandas/core/indexes/period.py in get_loc(self, key, method, tolerance)
501
502 try:
--> 503 asdt, reso = parse_time_string(key, self.freq)
504 except DateParseError as err:
505 # A string with invalid format
~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_time_string()
~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso()
ValueError: Given date string not likely a datetime.
```
Compare that with DatetimeIndex
```python
In [13]: t = pd.Series([1, 2, 3], index=pd.date_range('2000', periods=3, name='A'))
In [14]: t.index.get_loc("A")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/sandbox/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_str_to_tsobject()
~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string()
ValueError: Given date string not likely a datetime.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
~/sandbox/pandas/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
566 try:
--> 567 key = self._maybe_cast_for_get_loc(key)
568 except ValueError as err:
~/sandbox/pandas/pandas/core/indexes/datetimes.py in _maybe_cast_for_get_loc(self, key)
594 # needed to localize naive datetimes
--> 595 key = Timestamp(key)
596 if key.tzinfo is None:
~/sandbox/pandas/pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
~/sandbox/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_to_tsobject()
~/sandbox/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_str_to_tsobject()
ValueError: could not convert string to Timestamp
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
<ipython-input-14-436c3fe5a8ed> in <module>
----> 1 t.index.get_loc("A")
~/sandbox/pandas/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
567 key = self._maybe_cast_for_get_loc(key)
568 except ValueError as err:
--> 569 raise KeyError(key) from err
570
571 elif isinstance(key, timedelta):
KeyError: 'A'
```
This leads to other issues like `Serie.__getitem__` and `Series.__contains__`:
```python
In [16]: "A" in t
Out[16]: False
In [17]: "A" in s
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-17-6445c229fb58> in <module>
----> 1 "A" in s
~/sandbox/pandas/pandas/core/generic.py in __contains__(self, key)
1739 def __contains__(self, key) -> bool_t:
1740 """True if the key is in the info axis"""
-> 1741 return key in self._info_axis
1742
1743 @property
~/sandbox/pandas/pandas/util/_decorators.py in wrapper(*args, **kwargs)
353 @wraps(func)
354 def wrapper(*args, **kwargs) -> Callable:
--> 355 return func(*args, **kwargs)
356
357 # collecting docstring and docstring templates
~/sandbox/pandas/pandas/core/indexes/period.py in __contains__(self, key)
332 hash(key)
333 try:
--> 334 self.get_loc(key)
335 return True
336 except KeyError:
~/sandbox/pandas/pandas/core/indexes/period.py in get_loc(self, key, method, tolerance)
501
502 try:
--> 503 asdt, reso = parse_time_string(key, self.freq)
504 except DateParseError as err:
505 # A string with invalid format
~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_time_string()
~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso()
ValueError: Given date string not likely a datetime.
```
As part of fixing this, we should revert the change to `pandas/core/groupby/grouper.py` in https://github.com/pandas-dev/pandas/pull/34049.
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MDExOlB1bGxSZXF1ZXN0NDE5ODQ2NzA5
| 34,241
|
CLN: Remove util.is_offset_object
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| 2
| 2020-05-19T02:59:48Z
| 2020-05-20T15:12:58Z
| 2020-05-20T14:53:45Z
|
MEMBER
| null |
Do isinstance checks on _Timestamp instead of ABCTimestamp where possible (less mro traversal)
|
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MDExOlB1bGxSZXF1ZXN0NDE5ODQ5MzYx
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|
BUG: NaT+Period doesnt match Period+NaT
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| 1
| 2020-05-19T03:10:44Z
| 2020-06-06T16:13:52Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Most of the edits in `__add__` and `__sub__` are just an un-indent to get rid of an unreachable branch, but found this incorrect case along the way.
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MDExOlB1bGxSZXF1ZXN0NDE5ODY0ODMw
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|
CLN: remove get_base_alias
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| 2020-05-19T04:10:54Z
| 2020-05-20T15:12:15Z
| 2020-05-20T14:55:55Z
|
MEMBER
| null |
There are way too many really-similar functions like this. This trims one of them and de-duplicates 4 near-identical usages in the plotting code.
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MDU6SXNzdWU2MjA2ODEwODE=
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ENH: Support out-of-band pickling (protocol 5)
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| 6
| 2020-05-19T05:06:47Z
| 2020-10-14T12:27:38Z
| 2020-10-14T12:27:38Z
|
NONE
| null |
#### Is your feature request related to a problem?
It would be nice if Pandas objects supported [pickle's protocol 5 for out-of-band serialization]( https://www.python.org/dev/peps/pep-0574/ ). This would allow the underlying data to be captured in `PickleBuffer`s (specialized `memoryview`). For libraries using pickle's protocol 5 to transmit data over the wire, this would allow for zero-copy data transmission.
#### Describe the solution you'd like
Pandas objects implement `__reduce_ex__` and if the `protocol` argument is `5` or greater, they construct `PickleBuffer`s out of any data arguments.
#### API breaking implications
NA as it should be possible to fallback to existing behavior for older pickle protocols. Users have to actively opt-in at a higher level API (through pickle) to see any effect.
#### Describe alternatives you've considered
NA
#### Additional context
This would be useful in libraries that support distributed dataframes ;)
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DOC, ENH: Clarify ExcelFile's available engine compatibility with file types in...
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| 2020-05-19T07:26:04Z
| 2020-05-19T08:14:24Z
| 2020-05-19T08:14:24Z
|
CONTRIBUTOR
| null |
the docstring. Add check to the ExcelFile class that verifies it. GH34237.
DOC: I edited the available ExcelFile's docstring to add information about the recent pyxlsb engine. I also added some compatibility information between the available engines and file types.
ENH: Passing an incompatible filetype-engine combo to the read_excel method would result in an error at line 824/845 (`self._reader = self._engines[engine](self._io)`), which would be engine-dependent and provide little information to the user about what went wrong. The changes proposed check for compatibility in-class and provide some feedback in case of error.
- [x] closes #34237
- [ ] 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 #34053 on branch 1.0.x (more informative error message with np.min or np.max on unordered Categorical)
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MEMBER
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xref #34053, #33115
regression in 1.0.0
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Backport PR #34049 on branch 1.0.x (Bug in Series.groupby would raise ValueError when grouping by PeriodIndex level)
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MEMBER
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xref #34049, #34010
regression in 1.0.0
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ENH: Add dtype-support for pandas' type-hinting
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| 1
| 2020-05-19T09:11:22Z
| 2020-05-19T10:44:17Z
| null |
NONE
| null |
It would be great if you could annotate a series, for example with -
```python
def process_series(ser: pd.Series[int, str]) -> pd.Series[int, int]:
```
to indicate that the function should accept a string-valued series with an integer index, and output
int-valued series with an integer index.
It could be even better if you would build on that such that the series member's types could be inferred using `TypeVar`s (for example, `pd.Series[int,str].iloc` would accept an integer and return a string), but that's not necessary - just a bonus or a later milestone.
To do that, you could add a new module (maybe "pandas.typing"?) that would contain these type-hints and would require minimal integration (if any) into the pandas' infrastructure. There's a similar package for "numpy" that's external to it called [nptyping](https://github.com/ramonhagenaars/nptyping) that could be used as a reference.
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ENH: to_sql option for progress_bar
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[] | null | 2
| 2020-05-19T09:45:37Z
| 2021-02-05T15:47:34Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
pd.to_sql should definitely have a *progress bar* while dumping *df.to_sql *just like
*pandas.to_gbq*
#### Describe the solution you'd like
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_gbq.html
pd.to_sql should have *progress_bar=True* as a boolean parameter in case the user is sending the data in chunks or if the user wants to see the progress.
#### Describe alternatives you've considered
https://stackoverflow.com/questions/39494056/progress-bar-for-pandas-dataframe-to-sql
```python
import sqlite3
import pandas as pd
from tqdm import tqdm
DB_FILENAME='/tmp/test.sqlite'
def chunker(seq, size):
# from http://stackoverflow.com/a/434328
return (seq[pos:pos + size] for pos in xrange(0, len(seq), size))
def insert_with_progress(df, dbfile):
con = sqlite3.connect(dbfile)
chunksize = int(len(df) / 10) # 10%
with tqdm(total=len(df)) as pbar:
for i, cdf in enumerate(chunker(df, chunksize)):
replace = "replace" if i == 0 else "append"
cdf.to_sql(con=con, name="MLS", if_exists=replace, index=False)
pbar.update(chunksize)
df = pd.DataFrame({'a': range(0,100000)})
insert_with_progress(df, DB_FILENAME)
```
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Backport PR #33968 on branch 1.0.x (CI: Bump numpydev URL)
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NONE
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Backport PR #33968: CI: Bump numpydev URL
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BUG: 'Series' objects are mutable, thus they cannot be hashed
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| 13
| 2020-05-19T10:15:44Z
| 2021-08-16T15:24:53Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
df = pd.DataFrame({"id":[0, 1, 2], "name":["a", None, None]})
df.query("name.isnull()")
# output:
TypeError: 'Series' objects are mutable, thus they cannot be hashed
```
#### Problem description
i'm so sad about this error, cause i do this 'query' method a lot of times.
But today, this error happend suddenly, i really don't know what happend with my mac.
i just installed mars library which relative with pandas, and nothing else.
please help me for this problem, so appreciate！！
#### Expected Output
id name
1 1 None
2 2 None
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
<details>
[TypeError: 'Series' objects are mutable, thus they cannot be hashed]
</details>
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pd.set_option("io.excel.xlsx.reader", "openpyxl") does not work
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| 3
| 2020-05-19T10:24:06Z
| 2021-01-03T16:48:19Z
| 2021-01-03T16:48:19Z
|
NONE
| null |
This works fine for an xlsx file:
`pd.read_excel(path, engine="openpyxl")`
However if i try using set_option then read_excel fails:
```
pd.set_option("io.excel.xlsx.reader", "openpyxl")
pd.read_excel(path)
```
ImportError: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.
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MDExOlB1bGxSZXF1ZXN0NDIwMTAwNDg5
| 34,253
|
Make option_context a ContextDecorator.
|
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| 3
| 2020-05-19T12:54:41Z
| 2020-05-25T18:47:44Z
| 2020-05-25T17:40:35Z
|
CONTRIBUTOR
| null |
This makes it possible to use option_context as a decorator over an
entire function, saving an indent level and making it easy to comment
out (or in) the context while keeping correct indentation.
- [ ] closes #xxxx (N/A)
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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|
ENH: Is possible use rename for flatten MultiIndex?
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[] | null | 2
| 2020-05-19T13:40:00Z
| 2021-08-07T06:24:34Z
| null |
NONE
| null |
From [SO](https://stackoverflow.com/a/61891863/2901002):
df1 = (df.set_index(['Animal', df.groupby('Animal').cumcount().add(1)])
.unstack()
.sort_index(axis=1, level=1))
df1.columns = [f'{a}_{b}' for a, b in df1.columns]
df1 = df1.reset_index()
print (df1)
Animal Age_1 Color_1 Length_1 Age_2 Color_2 Length_2 Age_3 Color_3 \
0 Cat 1 Brown 50cm 2 White 60cm 3 Brown
1 Dog 1 White 99cm 2 White 129cm 3 White
Length_3
0 55cm
1 105cm
If I want chain flatten `MultIindex` for one line solution, is it possible? Now `f-string`s cannot chain with `sort_index` and `reset_index` (or not idea how).
Is possible implemented [`MultiIndex.to_flat_index`](http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.to_flat_index.html) for `DataFrame` ?
df1 = (df.set_index(['Animal', df.groupby('Animal').cumcount().add(1)])
.unstack()
.sort_index(axis=1, level=1)
.to_flat_index(axis=1)
.reset_index())
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CI: Linux py37_locale failing with pyarrow 0.17.1
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|
MEMBER
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=35751&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=add65f64-6c25-5783-8fd6-d9aa1b63d9d4
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MDU6SXNzdWU2MjEwMjI1NzM=
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|
BUG: DataFrame.isin fails when other is a categorical series
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| 8
| 2020-05-19T14:27:23Z
| 2020-07-10T00:08:27Z
| 2020-07-10T00:08:27Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.__version__)
x = pd.DataFrame.from_dict({'a':[1,2,3], 'b':[4,5,6]})
y = pd.DataFrame({'a':[1,2,3]}, dtype='category')
print(x.isin(y))
y = pd.Series([1,2,3]).astype('category')
print(x.isin(y))
1.0.3
a b
0 True False
1 True False
2 True False
Traceback (most recent call last):
File "/home/brmiller/repro.py", line 9, in <module>
print(x.isin(y))
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/frame.py", line 8423, in isin
return self.eq(values.reindex_like(self), axis="index")
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/ops/__init__.py", line 814, in f
self, other, op, fill_value=None, axis=axis, level=level
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/ops/__init__.py", line 618, in _combine_series_frame
new_data = left._combine_match_index(right, func)
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/frame.py", line 5317, in _combine_match_index
new_data = func(self.values.T, other.values).T
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/ops/common.py", line 64, in new_method
return method(self, other)
File "/home/brmiller/anaconda3/envs/pandas1/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 72, in func
raise ValueError("Lengths must match.")
ValueError: Lengths must match.
```
#### Problem description
This operation previously worked in pandas 0.25.3 and gave the same result as the case when other is a single column DataFrame.
#### Expected Output
```
a b
0 True False
1 True False
2 True False
a b
0 True False
1 True False
2 True False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.14.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
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 : 0.15.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.1
</details>
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MDU6SXNzdWU2MjEwNTc3NzA=
| 34,257
|
ENH: DataFrame.replace does not work with dictionary subclasses
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[] | null | 8
| 2020-05-19T15:08:56Z
| 2020-05-20T15:18:28Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I want to use `DataFrame.replace` with a dictionary that has a custom `__getitem__` logic. The current behavior is easy to demonstrate with a `defaultdict` (see code below).
#### Describe the solution you'd like
I would like `DataFrame.replace` to honor the `__getitem__` method of dict-like arguments.
#### API breaking implications
I don't know.
#### Code
```python
>>> from collections import defaultdict
>>> import pandas as pd
>>> df = pd.DataFrame([1, 2, 3])
>>> d = defaultdict(int, {1: -1, 2: -2})
>>> df.replace(d)
0
0 -1
1 -2
2 3
>>> d[3]
0
```
Expected behavior: the `3` inside the DataFrame should be replaced with `0`, because `d[3]` returns `0`.
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MDExOlB1bGxSZXF1ZXN0NDIwMTkyNjUx
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CI: temporarily disable interval in feather tests (pyarrow 0.17.1 regression)
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/34255
Temporary solution to get green CI.
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Allow merge_asof on first column of like-levelled MultiIndexes
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| 2
| 2020-05-19T15:31:35Z
| 2020-06-20T16:02:21Z
| 2020-06-20T16:02:14Z
|
NONE
| null |
This change essentially allows you to merge_asof on the first level of a MultiIndex, treating the levels after the first as additional 'by' columns. It is possible to do the reset_index/set_index oneself but I think this is a nice syntax and a fairly natural thing to do. Would you be interested in incorporating something like this into pandas?
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: One-hot decoding
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| 2020-05-19T15:57:09Z
| 2020-05-19T16:09:20Z
| 2020-05-19T16:09:20Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
`pd.get_dummies` provides a way to turn a sequence of category-like data into a one-hot encoded data frame. However, there is no easy way (to my knowledge) of going in the other direction: given a boolean dataframe where the row sums are all 1, produce a categorical series. This task is particularly valuable for serialisation.
#### Describe the solution you'd like
Some way of constructing a `Categorical` array from a one-hot encoded dataframe (view). To avoid piling extra functionality into the existing constructor, a class method could be used.
Scratch implementation:
```python
import numpy as np
import pandas as pd
class Categorical:
...
@classmethod
def from_dummies(cls, df: pd.DataFrame, **kwargs):
onehot = df.astype(bool)
if (onehot.sum(axis=1) > 1).any():
raise ValueError("Some rows belong to >1 category")
index_into = pd.Series([np.nan] + list(onehot.columns))
mult_by = np.arange(1, len(index_into))
indexes = (onehot.astype(int) * mult_by).sum(axis=1)
values = index_into[indexes]
return cls(values, df.columns, **kwargs)
```
#### Describe alternatives you've considered
- A free function (less discoverable, less self-documenting)
- Importing scikit-learn
#### Additional context
[sklearn.preprocessing.OneHotEncoder.inverse_transform](https://github.com/scikit-learn/scikit-learn/blob/fd237278e/sklearn/preprocessing/_encoders.py#L473)
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DOC: Clarify ExcelFile's available engine compatibility with file types in...
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| 2020-06-01T01:41:29Z
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CONTRIBUTOR
| null |
information about engine compatibility and also reflect the support of the pyxlsb engine. GH34237.
DOC: I edited the available ExcelFile's docstring to add information about the recent pyxlsb engine. I also added some compatibility information between the available engines and file types.
- [x] closes #34237
- [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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REF: FY5253Mixin to share methods
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REF: Implement RelativeDeltaOffset
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MEMBER
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RelativeDeltaOffset (basically the same as the existing DateOffset class) is a bit of an odd duck _attributes-wise, this is just a move without yet cdef-ing it.
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MDExOlB1bGxSZXF1ZXN0NDIwMzM0NzI5
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ENH: add fsspec support
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| 39
| 2020-05-19T19:59:53Z
| 2020-06-23T13:57:28Z
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CONTRIBUTOR
| null |
Supersedes https://github.com/pandas-dev/pandas/pull/33549
closes #33452
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- [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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MDExOlB1bGxSZXF1ZXN0NDIwNDE5MzA1
| 34,267
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Fix .isin Considers "1" and 1 equal
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CONTRIBUTOR
| null |
- [X ] ref #34125
- [X ] tests added / passed
- [X ] passes `black pandas`
- [X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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TST: GH28813 test .diff() on Sparse dtype
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| 2
| 2020-05-20T01:46:51Z
| 2020-05-22T11:59:55Z
| 2020-05-22T09:58:31Z
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CONTRIBUTOR
| null |
- [x] closes #28813
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Add a quick test to confirm that .diff() function handles sparse dataframes the same way as regular dataframes.
First time making a pandas PR so comments, advice, critiques are encouraged.
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| 1
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added some docstrings as well
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|
Problem with obtaining a view of a DataFrame using loc
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[] | null | 3
| 2020-05-20T07:30:49Z
| 2021-08-07T06:31:44Z
| 2021-08-07T06:31:39Z
|
NONE
| null |
Hello, I am trying to obtain a view of a pandas dataframe using the `loc` method but it is not working as expected when I am modifying the original DataFrame.
I want to extract a row/slice of a DataFrame using the `loc` method so that when a modification is done to the DataFrame, the slice reflects the change.
Let's have a look at this example:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'ID':np.arange(0,5,2), 'a':np.arange(3), 'b':np.arange(3)}).set_index('ID')
df
a b
ID
0 0 0
2 1 1
4 2 2
```
Now I create a slice using loc:
```python
slice1 = df.loc[[2],]
slice1
a b
ID
2 1 1
```
Then I modify the original DataFrame:
```python
df.loc[2, 'b'] = 9
df
a b
ID
0 0 0
2 1 9
4 2 2
```
But unfortunately our slice does not reflect this modification as I would be expecting for a view:
```python
slice1
a b
ID
2 1 1
```
My expectation:
```python
a b
ID
2 1 9
```
Am I doing something wrong or is this a bug?
Thank you for your help.
Pandas version: 1.0.3
Stackoverflow: https://stackoverflow.com/questions/61890845/obtain-a-view-of-a-dataframe-using-the-loc-method
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BUG: Same function calls on the same DataFrameGroupBy object give different results
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| 5
| 2020-05-20T08:53:34Z
| 2020-08-07T16:56:13Z
| 2020-08-07T16:56:13Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
# Source codes
```python
import pandas as pd
inp = pd.DataFrame([[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 10, 11, 12],
[5, 14, 15, 16],
[1, 18, 19, 20],
[21, 22, 23, 24],
[5, 26, 27, 28]],
columns=['group', 'fa', 'fb', 'fc'])
print('pandas version:', pd.__version__)
grps = inp.groupby('group', as_index=True)
print('Column number in all groups:',
grps.apply(lambda x: x.shape[1]).unique())
print('Column number in all groups:',
grps.apply(lambda x: x.shape[1]).unique())
print('ID:', id(grps))
print('Shape of first dataframe in the group:', grps.first().shape)
print('ID:', id(grps))
print('Column number in all groups:',
grps.apply(lambda x: x.shape[1]).unique())
```
# Problem description
In above codes, same function calls `grps.apply(lambda x: x.shape[1]).unique()` give different results:
In the first 2 times before `grps.first().shape` is called, it returns 4.
While after `grps.first().shape` is called, it returns 3.
## Running output
```
Column number in all groups: [4]
Column number in all groups: [4]
ID: 140686222651664
Shape of first dataframe in the group: (3, 3)
ID: 140686222651664
Column number in all groups: [3]
```
## Expected Output
```
Column number in all groups: [4]
Column number in all groups: [4]
ID: 140686222651664
Shape of first dataframe in the group: (3, 3)
ID: 140686222651664
Column number in all groups: [4]
```
# Environments
Python 3.7.7, Ubuntu 18.04.
Output of ``pd.show_versions()``:
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-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 : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 41.2.0
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 : 7.13.0
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
```
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ENH: specify missing keys in KeyError when passing list-like to .loc
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| 6
| 2020-05-20T10:04:10Z
| 2020-06-26T20:21:17Z
| 2020-06-26T20:21:17Z
|
CONTRIBUTOR
| null |
I'm a big fan of the strong limitation when passing list-likes to `.loc[]`. However, when the a key is missing and the KeyError is raised, it'd be helpful to know _which_ key/s is/are missing.
I understand this could have performance implications since each key of the list-like object should be searched, but also think the UX would be improved.
At least, the first missing key could be reported, without hindering performance. I'm happy to provide a PR for this if provided with some guidelines (I suppose this affects both Series and DataFrames, as well as maybe some other parts of the code I'm not aware of).
```python
pd.__version__
# '1.0.3'
s = pd.Series({'a': 1, 'b': 2, 'c': 3})
s.loc['d'] # Raises KeyError but specifies 'd' is missing
s.loc[['a', 'b', 'c', 'd']] # Raises KeyError without specifying which key is missing
```
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QST:Different results of pd.merge if using on='key' vs left_on/right_on!
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[] | null | 4
| 2020-05-20T11:00:13Z
| 2021-08-07T06:33:30Z
| 2021-08-07T06:33:25Z
|
NONE
| null |
Hello,
I see a mysterious behaviour of merge function in pandas and i don't know if its a bug or because i missed something.
If i use the parameters left_on and right on an how='outer' then a key is missing and relpaced with a NA. If i use the same name for both key columns and use on='key' all the keys will be available. Is this a normal behaviour? Is there any difference between on and left_on/right_on?
Please see the examples below:
```python
# Example 1: NA for key=2
a = pd.DataFrame({'aa': [1,3,4,5], 'bb':[1,2,3,4]})
b = pd.DataFrame({'ab': [1,2,3,4,5], 'cc':[1,2,3,4,5]})
pd.merge(a, b, left_on='aa', right_on='ab', how='outer')
# Example 2: no NA
a = pd.DataFrame({'aa': [1,3,4,5], 'bb':[1,2,3,4]})
b = pd.DataFrame({'aa': [1,2,3,4,5], 'cc':[1,2,3,4,5]})
pd.merge(a, b, on='aa', how='outer')
```
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| 621,720,948
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| 34,274
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BUG: small error in pandas/io/pytables.py
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| 6
| 2020-05-20T12:26:47Z
| 2021-01-05T02:22:33Z
| 2021-01-05T02:22:33Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
df.to_hdf('file.h5','name', format='table')
~/miniconda3/lib/python3.6/site-packages/pandas/io/pytables.py in _maybe_convert_for_string_atom(name, block, existing_col, min_itemsize, nan_rep, encoding, errors)
4798 # we cannot serialize this data, so report an exception on a column
4799 # by column basis
-> 4800 for i in range(len(block.shape[0])):
4801
4802 col = block.iget(i)
TypeError: object of type 'int' has no len()
```
#### Problem description
I don't know why exactly pandas cannot serialize my data frame with a multi-index.
But the above error prevents the real error message to be printed.
#### Expected Output
Error message.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3.post20200325
Cython : 0.29.18
pytest : 5.4.1
hypothesis : 5.15.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 3.8.0
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 3.8.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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ENH: fix arrow roundtrip for ExtensionDtypes in absence of pandas metadata
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| 2
| 2020-05-20T13:28:20Z
| 2020-05-22T14:40:44Z
| 2020-05-22T14:40:40Z
|
MEMBER
| null |
An oversight in our Arrow Extension Types for period and interval to also define the conversion to the equivalent pandas dtype. This ensures that the arrow->pandas conversion also works if there is no "pandas_metadata" in the schema's metadata, eg when this metadata was lost during some operations (up to now, the pandas ExtensionDtype was reconstructed based on the string name in the stored metadata).
See https://arrow.apache.org/docs/python/extending_types.html#conversion-to-pandas
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MDU6SXNzdWU2MjE4NjM0MTM=
| 34,276
|
QST: How "safe" is it to pickle a DataFrame with v1.x for long(er) term storage?
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| 2020-05-20T15:38:11Z
| 2020-08-21T17:05:40Z
| 2020-05-20T16:12:28Z
|
NONE
| null |
#### Question about pandas
How "safe" is pickling `DataFrame` objects with v1.x for long term? Pickling in past versions has been a sort of gamble, as expected, but I'm wondering how the long term storability is for `pandas` objects (in particular, `DataFrame`) now that the API has solidified in v1.x.
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MDExOlB1bGxSZXF1ZXN0NDIwODQ4Mjkz
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BUG: flex op with DataFrame, Series and ea vs ndarray
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| 10
| 2020-05-20T16:04:18Z
| 2020-05-25T17:45:16Z
| 2020-05-25T17:43:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
<s>I have a couple ideas in mind that might make the edit in blocks here unnecessary, but want to profile them first, so will do that cleanup in a refactor following the bugfix.</s> Updated with a much cleaner implementation, it also de-special-cases one of our timedelta64 tests.
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| 621,909,431
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MDExOlB1bGxSZXF1ZXN0NDIwODcyMDc3
| 34,278
|
REF: Make Period arith mirror PeriodArray arith
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| 0
| 2020-05-20T16:33:50Z
| 2020-05-22T20:30:35Z
| 2020-05-22T19:47:50Z
|
MEMBER
| null |
In follow-ups can use is_any_tdlike_scalar elsewhere
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| 34,279
|
Reopened: "TypeError: searchsorted requires compatible dtype or scalar, not ndarray "
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| 2020-05-20T19:10:55Z
| 2020-05-21T13:53:43Z
| 2020-05-21T13:53:43Z
|
NONE
| null |
"TypeError: searchsorted requires compatible dtype or scalar, not Series"
appears again when applying timeindex data:
```
#sample data
data = ['1998-01-07 13:17:00', '1998-01-07 13:18:00', '1998-01-07 13:19:00', '1998-01-07 13:20:00',
'1998-01-07 13:21:00', '1998-01-07 13:22:00', '1998-01-07 13:23:00']
df = pd.DataFrame(x)
df.set_index(df.iloc[:, 0], inplace=True)
df.index = pd.DatetimeIndex(df.index.values)
# this is the line
df.index.searchsorted(pd.Series(np.array([df.index[0], df.index.max()])))
```
How to solve this?
|
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BUG: can't set up dev environment -- Import Error: C extensions: ...
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[] | null | 11
| 2020-05-20T20:54:29Z
| 2020-12-01T01:31:39Z
| 2020-05-25T19:50:07Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
pulled out from #33821 (the main symptom was that `import pandas` failed (see problem description):
> Yea looks like the extensions need to be rebuilt. @charlesdong1991 instructions above should work; maybe not even need the second line if you launch Python from the pandas repo
> […](#)
> On May 19, 2020, at 1:17 PM, Kaiqi Dong ***@***.***> wrote: @charlesdong1991 commented on this pull request. In pandas/tests/plotting/test_frame.py <[#33821 (comment)](https://github.com/pandas-dev/pandas/pull/33821#discussion_r427573721)>: > + def test_color_and_marker(self): + # GH21003 - code sample from 2018-05-10 is equivalent to this test + df = DataFrame(np.random.random((7, 4))) + # combining a color string and a marker letter should be allowed + df.plot(color="green", style="d") # d for diamond + + def test_color_list_and_marker(self): + # GH21003 - code sample from 2020-04-23 is equivalent to this test + df = DataFrame(np.random.random((7, 4))) + color_list = ["yellow", "red", "green", "blue"] + ax = df.plot(color=color_list, style="d") + # Before this patch was introduced, the previous line of code resulted + # in a plot where each individual line was assigned a list of colors: + # ax.lines[i].get_color() == ['yellow', 'red', 'green', 'blue'] + # which resulted in a ValueError when plt.draw() was called. + assert [line.get_color() for line in ax.lines] == color_list emm, have no idea, i really think python setup.py build_ext --inplace -j 4 python -m pip install -e . --no-build-isolation --no-use-pep517 would solve the issue @WillAyd <https://github.com/WillAyd> could you help with the environment issue? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <[#33821 (comment)](https://github.com/pandas-dev/pandas/pull/33821#discussion_r427573721)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAEU4UNW7DTC32C2KAWVF2LRSLSNZANCNFSM4MR622DA>.
I just re-built the pandas-dev environment altogether:
I ran:
```conda env remove --name pandas-dev```
The `pandas-joooeey` folder was still there so I deleted it.
Then I ran in Git Bash:
```
Joey@zenbook MINGW64 ~
$ git clone https://github.com//github.com/joooeey/pandas.git pandas-joooeey
Cloning into 'pandas-joooeey'...
remote: Not Found
fatal: repository 'https://github.com//github.com/joooeey/pandas.git/' not found
Joey@zenbook MINGW64 ~
$ cd pandas-joooeey
bash: cd: pandas-joooeey: No such file or directory
Joey@zenbook MINGW64 ~
$ git clone https://github.com/joooeey/pandas.git pandas-joooeey
Cloning into 'pandas-joooeey'...
remote: Enumerating objects: 226160, done.
remote: Total 226160 (delta 0), reused 0 (delta 0), pack-reused 226160
Receiving objects: 100% (226160/226160), 183.30 MiB | 903.00 KiB/s, done.
Resolving deltas: 100% (188107/188107), done.
Updating files: 100% (1889/1889), done.
Joey@zenbook MINGW64 ~
$ cd pandas-joooeey
Joey@zenbook MINGW64 ~/pandas-joooeey (master)
$ git remote add upstream https://github.com/pandas-dev/pandas.git
```
And then the following commands in Anaconda Prompt (as given by `doskey /history`:
```
(pandas-dev) c:\Users\Joey\pandas-joooeey>doskey /history
cd c:\\users\joey\pandas-joooeey
conda update conda
conda env create -f environment.yml
conda activate pandas-dev
python setup.py build_ext --inplace -j 4
python -m pip install -e . --no-build-isolation --no-use-pep517
python setup.py build_ext --inplace --force
cython --version
doskey /history
```
None of these threw errors. However, I still get an error when I `import pandas` in Spyder (I made sure to open a fresh console in Spyder):
```
import pandas
Traceback (most recent call last):
File "C:\Users\Joey\pandas-joooeey\pandas\__init__.py", line 32, in <module>
from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
File "C:\Users\Joey\pandas-joooeey\pandas\_libs\__init__.py", line 13, in <module>
from pandas._libs.interval import Interval
ModuleNotFoundError: No module named 'pandas._libs.interval'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<ipython-input-1-38d4b0363d82>", line 1, in <module>
import pandas
File "C:\Users\Joey\pandas-joooeey\pandas\__init__.py", line 40, in <module>
) from e
ImportError: C extension: No module named 'pandas._libs.interval' not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first.
```
I also tried re-basing master from upstream in Git Bash:
```
git fetch upstream
git merge upstream/master
git push
```
Console output:
<details>
```
Joey@zenbook MINGW64 ~/pandas-joooeey (master)
$ git fetch upstream
remote: Enumerating objects: 382, done.
remote: Counting objects: 100% (382/382), done.
remote: Compressing objects: 100% (57/57), done.
remote: Total 507 (delta 348), reused 336 (delta 325), pack-reused 125
Receiving objects: 100% (507/507), 239.93 KiB | 813.00 KiB/s, done.
Resolving deltas: 100% (408/408), completed with 168 local objects.
From https://github.com/pandas-dev/pandas
* [new branch] 0.19.x -> upstream/0.19.x
* [new branch] 0.20.x -> upstream/0.20.x
* [new branch] 0.21.x -> upstream/0.21.x
* [new branch] 0.22.x -> upstream/0.22.x
* [new branch] 0.23.x -> upstream/0.23.x
* [new branch] 0.24.x -> upstream/0.24.x
* [new branch] 0.25.x -> upstream/0.25.x
* [new branch] 1.0.x -> upstream/1.0.x
* [new branch] gh-pages -> upstream/gh-pages
* [new branch] master -> upstream/master
* [new branch] revert-25761-patch-1 -> upstream/revert-25761-patch-1
* [new branch] revert-29447-orc-reader -> upstream/revert-29447-orc-re
* [new branch] v0.25.2.x -> upstream/v0.25.2.x
Joey@zenbook MINGW64 ~/pandas-joooeey (master)
$ git merge upstream/master
Updating 837daf18d..23c7e85d8
Fast-forward
.travis.yml | 24 +-
README.md | 1 +
asv_bench/benchmarks/algorithms.py | 17 +-
asv_bench/benchmarks/arithmetic.py | 63 +-
asv_bench/benchmarks/groupby.py | 58 +
asv_bench/benchmarks/io/parsers.py | 4 +-
asv_bench/benchmarks/rolling.py | 25 +-
asv_bench/benchmarks/stat_ops.py | 4 +-
azure-pipelines.yml | 3 +
ci/build39.sh | 21 +
ci/code_checks.sh | 4 +-
ci/deps/azure-36-minimum_versions.yaml | 5 +-
ci/deps/azure-37-numpydev.yaml | 5 +-
ci/deps/azure-macos-36.yaml | 2 +-
ci/deps/travis-37-arm64.yaml | 21 +
ci/run_tests.sh | 2 +-
ci/setup_env.sh | 19 +-
conda.recipe/meta.yaml | 4 +-
doc/source/conf.py | 2 +-
doc/source/development/contributing.rst | 6 +-
doc/source/development/extending.rst | 2 +-
doc/source/ecosystem.rst | 18 +-
doc/source/getting_started/install.rst | 2 +-
.../intro_tutorials/02_read_write.rst | 6 +-
.../intro_tutorials/03_subset_data.rst | 2 +-
.../intro_tutorials/06_calculate_statistics.rst | 8 +-
.../intro_tutorials/07_reshape_table_layout.rst | 16 +-
.../intro_tutorials/08_combine_dataframes.rst | 2 +-
.../intro_tutorials/09_timeseries.rst | 6 +-
.../intro_tutorials/10_text_data.rst | 20 +-
doc/source/reference/extensions.rst | 1 +
doc/source/reference/general_utility_functions.rst | 4 +
doc/source/reference/groupby.rst | 6 +-
doc/source/reference/offset_frequency.rst | 90 +-
doc/source/user_guide/10min.rst | 2 +-
doc/source/user_guide/basics.rst | 75 +-
doc/source/user_guide/computation.rst | 38 +-
doc/source/user_guide/dsintro.rst | 6 +-
doc/source/user_guide/enhancingperf.rst | 16 +-
doc/source/user_guide/gotchas.rst | 2 +-
doc/source/user_guide/groupby.rst | 94 +
doc/source/user_guide/indexing.rst | 2 +-
doc/source/user_guide/reshaping.rst | 14 +-
doc/source/user_guide/sparse.rst | 2 +-
doc/source/user_guide/timeseries.rst | 79 +-
doc/source/whatsnew/v0.18.0.rst | 10 +-
doc/source/whatsnew/v0.18.1.rst | 34 +-
doc/source/whatsnew/v0.19.0.rst | 46 +-
doc/source/whatsnew/v0.19.1.rst | 4 +-
doc/source/whatsnew/v0.19.2.rst | 4 +-
doc/source/whatsnew/v0.20.0.rst | 32 +-
doc/source/whatsnew/v0.20.2.rst | 10 +-
doc/source/whatsnew/v0.20.3.rst | 8 +-
doc/source/whatsnew/v0.21.0.rst | 26 +-
doc/source/whatsnew/v0.21.1.rst | 10 +-
doc/source/whatsnew/v1.1.0.rst | 259 ++-
environment.yml | 4 +-
pandas/__init__.py | 2 -
pandas/_libs/groupby.pyx | 4 +-
pandas/_libs/hashtable.pyx | 5 +-
pandas/_libs/hashtable_class_helper.pxi.in | 44 +-
pandas/_libs/hashtable_func_helper.pxi.in | 2 +-
pandas/_libs/index.pyx | 48 +-
pandas/_libs/internals.pyx | 5 +-
pandas/_libs/interval.pyx | 62 +-
pandas/_libs/lib.pxd | 2 +-
pandas/_libs/lib.pyx | 67 +-
pandas/_libs/missing.pxd | 2 +-
pandas/_libs/missing.pyx | 18 +-
pandas/_libs/parsers.pyx | 2 +-
pandas/_libs/reduction.pyx | 4 +-
pandas/_libs/reshape.pyx | 5 +-
pandas/_libs/src/parser/tokenizer.h | 4 +-
pandas/_libs/src/ujson/lib/ultrajson.h | 2 +-
pandas/_libs/src/ujson/python/objToJSON.c | 17 +-
pandas/_libs/testing.pyx | 11 +-
pandas/_libs/tslib.pyx | 32 +-
pandas/_libs/tslibs/__init__.py | 10 +-
pandas/_libs/tslibs/base.pxd | 8 +
pandas/_libs/tslibs/base.pyx | 16 +
pandas/_libs/tslibs/c_timestamp.pxd | 17 -
pandas/_libs/tslibs/c_timestamp.pyx | 434 ----
pandas/_libs/tslibs/ccalendar.pxd | 4 +
pandas/_libs/tslibs/ccalendar.pyx | 4 +
pandas/_libs/tslibs/conversion.pyx | 77 +-
pandas/_libs/tslibs/fields.pyx | 6 +-
pandas/_libs/tslibs/frequencies.pxd | 4 +-
pandas/_libs/tslibs/frequencies.pyx | 93 +-
pandas/_libs/tslibs/nattype.pxd | 2 +-
pandas/_libs/tslibs/nattype.pyx | 20 +-
pandas/_libs/tslibs/np_datetime.pxd | 2 -
pandas/_libs/tslibs/np_datetime.pyx | 9 -
pandas/_libs/tslibs/offsets.pxd | 2 +
pandas/_libs/tslibs/offsets.pyx | 779 ++++++-
pandas/_libs/tslibs/parsing.pyx | 23 +-
pandas/_libs/tslibs/period.pxd | 1 +
pandas/_libs/tslibs/period.pyx | 163 +-
pandas/_libs/tslibs/resolution.pyx | 70 +-
pandas/_libs/tslibs/strptime.pyx | 13 +-
pandas/_libs/tslibs/timedeltas.pyx | 247 +--
pandas/_libs/tslibs/timestamps.pxd | 20 +
pandas/_libs/tslibs/timestamps.pyx | 426 +++-
pandas/_libs/tslibs/timezones.pxd | 9 +-
pandas/_libs/tslibs/timezones.pyx | 21 +-
pandas/_libs/tslibs/tzconversion.pxd | 1 -
pandas/_libs/tslibs/tzconversion.pyx | 14 +-
pandas/_libs/tslibs/util.pxd | 42 +-
pandas/_libs/window/aggregations.pyx | 16 +-
pandas/_testing.py | 64 +-
pandas/_typing.py | 16 +
pandas/compat/__init__.py | 1 +
pandas/compat/_optional.py | 2 +-
pandas/compat/numpy/__init__.py | 10 +-
pandas/compat/numpy/function.py | 12 +-
pandas/compat/pickle_compat.py | 38 +
pandas/conftest.py | 28 +-
pandas/core/accessor.py | 2 +-
pandas/core/aggregation.py | 4 +-
pandas/core/algorithms.py | 100 +-
pandas/core/array_algos/transforms.py | 5 +-
pandas/core/arrays/_mixins.py | 122 ++
pandas/core/arrays/_ranges.py | 107 +-
pandas/core/arrays/base.py | 67 +-
pandas/core/arrays/boolean.py | 33 +-
pandas/core/arrays/categorical.py | 166 +-
pandas/core/arrays/datetimelike.py | 537 +++--
pandas/core/arrays/datetimes.py | 138 +-
pandas/core/arrays/integer.py | 38 +-
pandas/core/arrays/interval.py | 26 +-
pandas/core/arrays/masked.py | 35 +-
pandas/core/arrays/numpy_.py | 85 +-
pandas/core/arrays/period.py | 81 +-
pandas/core/arrays/sparse/array.py | 55 +-
pandas/core/arrays/sparse/dtype.py | 26 +-
pandas/core/arrays/string_.py | 9 +
pandas/core/arrays/timedeltas.py | 143 +-
pandas/core/base.py | 7 +-
pandas/core/common.py | 43 +-
pandas/core/computation/expr.py | 2 +-
pandas/core/computation/expressions.py | 4 +-
pandas/core/config_init.py | 5 +-
pandas/core/construction.py | 69 +-
pandas/core/dtypes/base.py | 32 +-
pandas/core/dtypes/cast.py | 92 +-
pandas/core/dtypes/common.py | 2 -
pandas/core/dtypes/concat.py | 150 +-
pandas/core/dtypes/dtypes.py | 37 +-
pandas/core/dtypes/generic.py | 2 -
pandas/core/dtypes/missing.py | 123 +-
pandas/core/frame.py | 344 ++-
pandas/core/generic.py | 277 ++-
pandas/core/groupby/generic.py | 178 +-
pandas/core/groupby/groupby.py | 366 +++-
pandas/core/groupby/grouper.py | 156 +-
pandas/core/groupby/ops.py | 129 +-
pandas/core/indexers.py | 2 +-
pandas/core/indexes/accessors.py | 6 +-
pandas/core/indexes/api.py | 3 +-
pandas/core/indexes/base.py | 107 +-
pandas/core/indexes/category.py | 16 +-
pandas/core/indexes/datetimelike.py | 279 +--
pandas/core/indexes/datetimes.py | 28 +-
pandas/core/indexes/extension.py | 46 +-
pandas/core/indexes/interval.py | 2 +-
pandas/core/indexes/multi.py | 10 +-
pandas/core/indexes/numeric.py | 4 -
pandas/core/indexes/period.py | 61 +-
pandas/core/indexes/range.py | 14 +-
pandas/core/indexes/timedeltas.py | 35 +-
pandas/core/indexing.py | 45 +-
pandas/core/internals/__init__.py | 10 +-
pandas/core/internals/blocks.py | 40 +-
pandas/core/internals/concat.py | 15 +-
pandas/core/internals/construction.py | 69 +-
pandas/core/internals/managers.py | 126 +-
pandas/core/missing.py | 103 +-
pandas/core/nanops.py | 118 +-
pandas/core/ops/__init__.py | 68 +-
pandas/core/ops/array_ops.py | 21 +-
pandas/core/ops/blockwise.py | 102 +
pandas/core/ops/dispatch.py | 14 +-
pandas/core/ops/docstrings.py | 4 +-
pandas/core/ops/methods.py | 10 +-
pandas/core/resample.py | 213 +-
pandas/core/reshape/concat.py | 12 +-
pandas/core/reshape/melt.py | 13 +-
pandas/core/reshape/merge.py | 42 +-
pandas/core/reshape/pivot.py | 40 +-
pandas/core/reshape/reshape.py | 6 +-
pandas/core/reshape/tile.py | 46 +-
pandas/core/series.py | 204 +-
pandas/core/sorting.py | 144 +-
pandas/core/strings.py | 71 +-
pandas/core/tools/datetimes.py | 192 +-
pandas/core/tools/numeric.py | 7 +-
pandas/core/tools/timedeltas.py | 4 +-
pandas/core/tools/times.py | 141 ++
pandas/core/util/numba_.py | 52 +-
pandas/core/window/common.py | 27 +-
pandas/core/window/ewm.py | 10 +-
pandas/core/window/expanding.py | 11 +-
pandas/core/window/rolling.py | 159 +-
pandas/errors/__init__.py | 18 +-
pandas/io/clipboard/__init__.py | 2 +-
pandas/io/common.py | 27 +
pandas/io/feather_format.py | 12 +-
pandas/io/formats/excel.py | 18 +-
pandas/io/formats/format.py | 28 +-
pandas/io/formats/html.py | 10 +-
pandas/io/formats/info.py | 70 +-
pandas/io/formats/style.py | 4 +-
pandas/io/gcs.py | 6 +-
pandas/io/json/_json.py | 2 +-
pandas/io/json/_normalize.py | 2 +-
pandas/io/json/_table_schema.py | 26 +-
pandas/io/parquet.py | 35 +-
pandas/io/parsers.py | 6 +-
pandas/io/pickle.py | 3 +-
pandas/io/pytables.py | 52 +-
pandas/io/s3.py | 8 +-
pandas/io/sas/sas7bdat.py | 3 +-
pandas/io/sas/sas_xport.py | 3 +-
pandas/io/sas/sasreader.py | 19 +
pandas/io/sql.py | 4 +-
pandas/io/stata.py | 125 +-
pandas/plotting/_core.py | 40 +-
pandas/plotting/_matplotlib/boxplot.py | 3 +-
pandas/plotting/_matplotlib/converter.py | 59 +-
pandas/plotting/_matplotlib/core.py | 10 +-
pandas/plotting/_matplotlib/hist.py | 4 +-
pandas/plotting/_matplotlib/misc.py | 2 +-
pandas/plotting/_matplotlib/timeseries.py | 37 +-
pandas/plotting/_matplotlib/tools.py | 2 +-
pandas/plotting/_misc.py | 117 +-
pandas/tests/api/test_api.py | 2 -
pandas/tests/arithmetic/common.py | 9 +-
pandas/tests/arithmetic/conftest.py | 28 +-
pandas/tests/arithmetic/test_datetime64.py | 147 +-
pandas/tests/arithmetic/test_interval.py | 17 +-
pandas/tests/arithmetic/test_numeric.py | 10 +-
pandas/tests/arithmetic/test_period.py | 74 +-
pandas/tests/arithmetic/test_timedelta64.py | 152 +-
pandas/tests/arrays/boolean/test_logical.py | 1 +
pandas/tests/arrays/categorical/test_analytics.py | 32 +
.../tests/arrays/categorical/test_constructors.py | 2 +
pandas/tests/arrays/categorical/test_missing.py | 53 +-
pandas/tests/arrays/integer/test_comparison.py | 10 +
pandas/tests/arrays/integer/test_concat.py | 26 +
pandas/tests/arrays/integer/test_function.py | 20 +
pandas/tests/arrays/sparse/test_array.py | 19 +
pandas/tests/arrays/string_/test_string.py | 55 +
pandas/tests/arrays/test_datetimelike.py | 126 +-
pandas/tests/arrays/test_datetimes.py | 34 +
pandas/tests/base/test_conversion.py | 58 +-
pandas/tests/base/test_fillna.py | 4 +-
pandas/tests/base/test_misc.py | 6 +-
pandas/tests/base/test_unique.py | 10 +-
pandas/tests/base/test_value_counts.py | 4 +-
pandas/tests/dtypes/cast/test_promote.py | 15 +-
pandas/tests/dtypes/test_common.py | 4 +
pandas/tests/dtypes/test_dtypes.py | 4 +
pandas/tests/dtypes/test_generic.py | 6 +-
pandas/tests/dtypes/test_inference.py | 117 +-
pandas/tests/extension/arrow/test_bool.py | 18 +
pandas/tests/extension/base/constructors.py | 33 +
pandas/tests/extension/base/dtype.py | 9 +-
pandas/tests/extension/base/getitem.py | 3 +-
pandas/tests/extension/base/methods.py | 60 +-
pandas/tests/extension/base/missing.py | 7 +
pandas/tests/extension/base/ops.py | 18 +-
pandas/tests/extension/decimal/test_decimal.py | 4 +
pandas/tests/extension/json/array.py | 6 +
pandas/tests/extension/json/test_json.py | 35 +-
pandas/tests/extension/test_boolean.py | 39 +-
pandas/tests/extension/test_categorical.py | 47 +-
pandas/tests/extension/test_datetime.py | 9 +
pandas/tests/extension/test_numpy.py | 35 +-
pandas/tests/extension/test_period.py | 11 +-
pandas/tests/extension/test_sparse.py | 12 +
pandas/tests/extension/test_string.py | 11 +-
pandas/tests/frame/indexing/test_indexing.py | 2 +-
pandas/tests/frame/methods/test_astype.py | 559 +++++
pandas/tests/frame/methods/test_at_time.py | 4 +
pandas/tests/frame/methods/test_cov_corr.py | 27 +
pandas/tests/frame/methods/test_drop_duplicates.py | 7 +
pandas/tests/frame/methods/test_interpolate.py | 12 +
pandas/tests/frame/methods/test_replace.py | 8 +
pandas/tests/frame/methods/test_round.py | 5 -
pandas/tests/frame/methods/test_set_index.py | 532 +++++
pandas/tests/frame/methods/test_shift.py | 6 +-
pandas/tests/frame/methods/test_sort_index.py | 418 +++-
pandas/tests/frame/methods/test_sort_values.py | 162 ++
pandas/tests/frame/methods/test_truncate.py | 17 +
pandas/tests/frame/methods/test_tz_convert.py | 6 +
pandas/tests/frame/test_alter_axes.py | 523 +----
pandas/tests/frame/test_analytics.py | 60 +-
pandas/tests/frame/test_api.py | 2 +-
pandas/tests/frame/test_arithmetic.py | 19 +-
pandas/tests/frame/test_axis_select_reindex.py | 3 +-
pandas/tests/frame/test_cumulative.py | 13 +-
pandas/tests/frame/test_dtypes.py | 546 +----
pandas/tests/frame/test_query_eval.py | 36 +
pandas/tests/frame/test_subclass.py | 133 +-
pandas/tests/frame/test_to_csv.py | 5 +
pandas/tests/groupby/aggregate/test_aggregate.py | 8 +
pandas/tests/groupby/aggregate/test_numba.py | 115 +
pandas/tests/groupby/test_apply.py | 20 +
pandas/tests/groupby/test_categorical.py | 19 +-
pandas/tests/groupby/test_counting.py | 135 +-
pandas/tests/groupby/test_function.py | 666 +-----
pandas/tests/groupby/test_groupby_dropna.py | 244 +++
pandas/tests/groupby/test_groupby_subclass.py | 77 +
pandas/tests/groupby/test_nth.py | 12 +
pandas/tests/groupby/test_nunique.py | 165 ++
pandas/tests/groupby/test_pipe.py | 78 +
pandas/tests/groupby/test_quantile.py | 217 ++
pandas/tests/groupby/test_size.py | 46 +
pandas/tests/groupby/test_timegrouper.py | 27 +-
pandas/tests/groupby/transform/test_numba.py | 9 +-
pandas/tests/indexes/categorical/test_astype.py | 66 +
pandas/tests/indexes/categorical/test_category.py | 63 +-
pandas/tests/indexes/categorical/test_indexing.py | 37 +
pandas/tests/indexes/common.py | 10 +-
pandas/tests/indexes/datetimelike.py | 11 +-
pandas/tests/indexes/datetimes/test_astype.py | 47 +-
.../tests/indexes/datetimes/test_constructors.py | 15 +-
pandas/tests/indexes/datetimes/test_date_range.py | 46 +-
pandas/tests/indexes/datetimes/test_datetime.py | 73 +-
pandas/tests/indexes/datetimes/test_indexing.py | 42 +-
pandas/tests/indexes/datetimes/test_insert.py | 23 +
pandas/tests/indexes/datetimes/test_map.py | 41 +
pandas/tests/indexes/datetimes/test_ops.py | 42 +-
.../indexes/datetimes/test_partial_slicing.py | 104 +-
pandas/tests/indexes/datetimes/test_pickle.py | 41 +
pandas/tests/indexes/datetimes/test_setops.py | 14 +-
pandas/tests/indexes/datetimes/test_timezones.py | 36 +-
pandas/tests/indexes/interval/test_indexing.py | 9 +
pandas/tests/indexes/interval/test_interval.py | 30 +-
pandas/tests/indexes/multi/test_analytics.py | 2 +-
pandas/tests/indexes/multi/test_indexing.py | 5 +-
pandas/tests/indexes/multi/test_join.py | 12 +-
.../tests/indexes/multi/test_partial_indexing.py | 61 +-
pandas/tests/indexes/multi/test_take.py | 3 -
pandas/tests/indexes/numeric/test_astype.py | 83 +
pandas/tests/indexes/period/test_astype.py | 14 +-
pandas/tests/indexes/period/test_factorize.py | 37 +
pandas/tests/indexes/period/test_indexing.py | 62 +-
.../tests/indexes/period/test_partial_slicing.py | 183 +-
pandas/tests/indexes/period/test_period.py | 52 -
pandas/tests/indexes/period/test_period_range.py | 8 +
pandas/tests/indexes/period/test_to_timestamp.py | 3 +-
pandas/tests/indexes/test_base.py | 17 +-
pandas/tests/indexes/test_common.py | 4 +-
pandas/tests/indexes/test_index_new.py | 5 +-
pandas/tests/indexes/test_numeric.py | 76 +-
pandas/tests/indexes/test_numpy_compat.py | 4 +-
.../tests/indexes/timedeltas/test_constructors.py | 11 +
pandas/tests/indexes/timedeltas/test_indexing.py | 14 +-
pandas/tests/indexes/timedeltas/test_insert.py | 11 +
pandas/tests/indexes/timedeltas/test_ops.py | 25 +-
.../indexes/timedeltas/test_partial_slicing.py | 42 +-
.../tests/indexes/timedeltas/test_searchsorted.py | 26 +
pandas/tests/indexes/timedeltas/test_setops.py | 2 +-
pandas/tests/indexes/timedeltas/test_timedelta.py | 32 +-
.../indexes/timedeltas/test_timedelta_range.py | 20 +-
pandas/tests/indexing/multiindex/test_loc.py | 20 +-
pandas/tests/indexing/multiindex/test_partial.py | 3 +-
pandas/tests/indexing/multiindex/test_slice.py | 10 +-
pandas/tests/indexing/multiindex/test_sorted.py | 30 +-
pandas/tests/indexing/test_categorical.py | 22 +-
pandas/tests/indexing/test_chaining_and_caching.py | 3 -
pandas/tests/indexing/test_coercion.py | 150 +-
pandas/tests/indexing/test_datetime.py | 13 +-
pandas/tests/indexing/test_indexing.py | 3 +-
pandas/tests/indexing/test_loc.py | 13 +
pandas/tests/indexing/test_partial.py | 123 +-
pandas/tests/internals/test_internals.py | 2 +-
pandas/tests/io/conftest.py | 10 +-
pandas/tests/io/data/stata/stata-compat-105.dta | Bin 0 -> 771 bytes
pandas/tests/io/data/stata/stata-compat-108.dta | Bin 0 -> 1128 bytes
pandas/tests/io/data/stata/stata-compat-111.dta | Bin 0 -> 1514 bytes
pandas/tests/io/data/stata/stata-compat-113.dta | Bin 0 -> 1514 bytes
pandas/tests/io/data/stata/stata-compat-114.dta | Bin 0 -> 1810 bytes
pandas/tests/io/data/stata/stata-compat-118.dta | Bin 0 -> 5798 bytes
pandas/tests/io/excel/test_openpyxl.py | 1 -
pandas/tests/io/excel/test_style.py | 8 +-
pandas/tests/io/excel/test_writers.py | 14 +
pandas/tests/io/formats/test_css.py | 2 -
pandas/tests/io/formats/test_format.py | 57 +
pandas/tests/io/json/test_normalize.py | 20 +
pandas/tests/io/json/test_pandas.py | 25 +-
pandas/tests/io/json/test_ujson.py | 3 +-
pandas/tests/io/parser/test_common.py | 9 +
pandas/tests/io/parser/test_dtypes.py | 3 +-
pandas/tests/io/parser/test_network.py | 32 +-
pandas/tests/io/parser/test_parse_dates.py | 25 +-
pandas/tests/io/parser/test_usecols.py | 10 +-
pandas/tests/io/pytables/test_store.py | 64 +-
pandas/tests/io/pytables/test_timezones.py | 28 +-
pandas/tests/io/test_common.py | 5 +
pandas/tests/io/test_feather.py | 29 +-
pandas/tests/io/test_parquet.py | 50 +-
pandas/tests/io/test_sql.py | 4 +-
pandas/tests/io/test_stata.py | 74 +-
pandas/tests/plotting/test_datetimelike.py | 4 +-
pandas/tests/plotting/test_hist_method.py | 24 +
pandas/tests/reductions/test_reductions.py | 6 +-
pandas/tests/resample/test_base.py | 36 +-
pandas/tests/resample/test_datetime_index.py | 327 ++-
pandas/tests/resample/test_deprecated.py | 263 +++
pandas/tests/resample/test_period_index.py | 95 +-
pandas/tests/resample/test_resample_api.py | 8 +-
pandas/tests/resample/test_resampler_grouper.py | 42 +
pandas/tests/resample/test_time_grouper.py | 26 +-
pandas/tests/resample/test_timedelta.py | 39 +-
pandas/tests/reshape/merge/test_merge.py | 32 +-
pandas/tests/reshape/merge/test_multi.py | 6 +-
pandas/tests/reshape/test_concat.py | 48 +-
pandas/tests/reshape/test_cut.py | 39 +
pandas/tests/reshape/test_pivot.py | 38 +-
pandas/tests/scalar/interval/test_arithmetic.py | 47 +
pandas/tests/scalar/interval/test_interval.py | 5 +-
pandas/tests/scalar/period/test_period.py | 22 +-
pandas/tests/scalar/test_nat.py | 9 +-
pandas/tests/scalar/timedelta/test_arithmetic.py | 42 +-
pandas/tests/scalar/timestamp/test_arithmetic.py | 2 +-
pandas/tests/scalar/timestamp/test_comparisons.py | 4 +-
pandas/tests/series/indexing/test_alter_index.py | 2 +
pandas/tests/series/indexing/test_datetime.py | 6 +-
pandas/tests/series/indexing/test_getitem.py | 13 +
pandas/tests/series/indexing/test_indexing.py | 69 +-
pandas/tests/series/methods/test_asof.py | 4 +-
pandas/tests/series/methods/test_astype.py | 25 +
pandas/tests/series/methods/test_at_time.py | 5 +
pandas/tests/series/methods/test_droplevel.py | 2 +-
pandas/tests/series/methods/test_interpolate.py | 31 +-
pandas/tests/series/methods/test_rank.py | 2 +-
pandas/tests/series/methods/test_shift.py | 5 +-
pandas/tests/series/methods/test_sort_index.py | 154 +-
pandas/tests/series/methods/test_sort_values.py | 28 +
pandas/tests/series/methods/test_to_dict.py | 8 +-
pandas/tests/series/methods/test_to_period.py | 10 +
pandas/tests/series/methods/test_to_timestamp.py | 13 +-
pandas/tests/series/methods/test_truncate.py | 17 +
pandas/tests/series/methods/test_tz_localize.py | 6 +-
pandas/tests/series/methods/test_update.py | 54 +-
pandas/tests/series/test_apply.py | 17 +-
pandas/tests/series/test_arithmetic.py | 35 +-
pandas/tests/series/test_constructors.py | 41 +-
pandas/tests/series/test_cumulative.py | 13 +-
pandas/tests/series/test_datetime_values.py | 7 +-
pandas/tests/series/test_dtypes.py | 6 +-
pandas/tests/series/test_io.py | 2 +
pandas/tests/series/test_missing.py | 9 +-
pandas/tests/series/test_operators.py | 54 +-
pandas/tests/series/test_subclass.py | 16 +
pandas/tests/series/test_timeseries.py | 1 +
pandas/tests/test_algos.py | 72 +
pandas/tests/test_downstream.py | 11 +-
pandas/tests/test_errors.py | 1 +
pandas/tests/test_expressions.py | 2 -
pandas/tests/test_multilevel.py | 328 +--
pandas/tests/test_strings.py | 58 +-
pandas/tests/test_take.py | 2 +-
pandas/tests/tools/test_to_time.py | 15 +-
pandas/tests/tseries/frequencies/test_freq_code.py | 61 +-
pandas/tests/tseries/offsets/test_offsets.py | 51 +-
.../tseries/offsets/test_offsets_properties.py | 5 +-
pandas/tests/tseries/offsets/test_ticks.py | 27 +-
pandas/tests/tseries/offsets/test_yqm_offsets.py | 1 +
pandas/tests/tslibs/test_api.py | 5 +-
pandas/tests/tslibs/test_conversion.py | 7 +-
pandas/tests/tslibs/test_normalize_date.py | 41 -
pandas/tests/tslibs/test_period_asfreq.py | 13 +-
pandas/tests/util/test_assert_series_equal.py | 34 +-
pandas/tests/window/common.py | 506 ++---
pandas/tests/window/conftest.py | 215 ++
pandas/tests/window/moments/conftest.py | 58 +
.../window/moments/test_moments_consistency_ewm.py | 279 +++
.../moments/test_moments_consistency_expanding.py | 497 +++++
.../moments/test_moments_consistency_rolling.py | 695 ++++++
pandas/tests/window/moments/test_moments_ewm.py | 661 ++----
.../tests/window/moments/test_moments_expanding.py | 420 ----
.../tests/window/moments/test_moments_rolling.py | 2254 ++++++++------------
pandas/tests/window/test_api.py | 565 ++---
pandas/tests/window/test_apply.py | 30 +-
pandas/tests/window/test_base_indexer.py | 76 +-
pandas/tests/window/test_ewm.py | 92 +-
pandas/tests/window/test_expanding.py | 264 ++-
pandas/tests/window/test_rolling.py | 892 +++++---
pandas/tests/window/test_window.py | 126 +-
pandas/tseries/frequencies.py | 16 +-
pandas/tseries/offsets.py | 1025 ++-------
pandas/util/_decorators.py | 12 +-
pandas/util/_doctools.py | 2 +-
pandas/util/_validators.py | 2 +-
pyproject.toml | 4 +-
requirements-dev.txt | 4 +-
scripts/validate_rst_title_capitalization.py | 17 +
setup.cfg | 7 +-
setup.py | 8 +-
web/pandas/community/blog/extension-arrays.md | 2 +-
web/pandas/getting_started.md | 16 +-
web/pandas/static/css/pandas.css | 3 +
504 files changed, 19269 insertions(+), 12424 deletions(-)
create mode 100755 ci/build39.sh
create mode 100644 ci/deps/travis-37-arm64.yaml
create mode 100644 pandas/_libs/tslibs/base.pxd
create mode 100644 pandas/_libs/tslibs/base.pyx
delete mode 100644 pandas/_libs/tslibs/c_timestamp.pxd
delete mode 100644 pandas/_libs/tslibs/c_timestamp.pyx
create mode 100644 pandas/_libs/tslibs/period.pxd
create mode 100644 pandas/core/arrays/_mixins.py
create mode 100644 pandas/core/ops/blockwise.py
create mode 100644 pandas/core/tools/times.py
create mode 100644 pandas/tests/arrays/integer/test_concat.py
create mode 100644 pandas/tests/frame/methods/test_astype.py
create mode 100644 pandas/tests/frame/methods/test_set_index.py
create mode 100644 pandas/tests/groupby/aggregate/test_numba.py
create mode 100644 pandas/tests/groupby/test_groupby_dropna.py
create mode 100644 pandas/tests/groupby/test_groupby_subclass.py
create mode 100644 pandas/tests/groupby/test_nunique.py
create mode 100644 pandas/tests/groupby/test_pipe.py
create mode 100644 pandas/tests/groupby/test_quantile.py
create mode 100644 pandas/tests/groupby/test_size.py
create mode 100644 pandas/tests/indexes/categorical/test_astype.py
create mode 100644 pandas/tests/indexes/datetimes/test_map.py
create mode 100644 pandas/tests/indexes/datetimes/test_pickle.py
create mode 100644 pandas/tests/indexes/numeric/test_astype.py
create mode 100644 pandas/tests/indexes/period/test_factorize.py
create mode 100644 pandas/tests/indexes/timedeltas/test_searchsorted.py
create mode 100644 pandas/tests/io/data/stata/stata-compat-105.dta
create mode 100644 pandas/tests/io/data/stata/stata-compat-108.dta
create mode 100644 pandas/tests/io/data/stata/stata-compat-111.dta
create mode 100644 pandas/tests/io/data/stata/stata-compat-113.dta
create mode 100644 pandas/tests/io/data/stata/stata-compat-114.dta
create mode 100644 pandas/tests/io/data/stata/stata-compat-118.dta
create mode 100644 pandas/tests/resample/test_deprecated.py
create mode 100644 pandas/tests/scalar/interval/test_arithmetic.py
create mode 100644 pandas/tests/series/methods/test_astype.py
delete mode 100644 pandas/tests/tslibs/test_normalize_date.py
create mode 100644 pandas/tests/window/moments/test_moments_consistency_ewm.py
create mode 100644 pandas/tests/window/moments/test_moments_consistency_expanding.py
create mode 100644 pandas/tests/window/moments/test_moments_consistency_rolling.py
delete mode 100644 pandas/tests/window/moments/test_moments_expanding.py
Joey@zenbook MINGW64 ~/pandas-joooeey (master)
$ git push
Total 0 (delta 0), reused 0 (delta 0), pack-reused 0
To https://github.com/joooeey/pandas.git
837daf18d..23c7e85d8 master -> master
```
</details>
After that, `import pandas` still results in the same C extension error as given above.
The only thing I left untouched was the Visual Studio Build Tools 2019.
I have the following build tools installed/missing:
Installation details
MSBuild Tools
Provides the tools required to build MSBuild-based applications.
C++ build tools
**Included**
- [x] C++ Build Tools core features
- [x] C++ 2019 Redistributable Update
**Optional**
- [x] MSVC v142 - VS 2019 C++ x64/x86 build tools (v14.25)
- [x] Windows 10 SDK (10.0.18362.0)
- [x] C++ CMake tools for Windows
- [x] Testing tools core features - Build Tools
- [x] C++ AddressSanitizer (Experimental)
- [ ] C++ ATL for latest v142 build tools (x86 & x64)
- [ ] C++ MFC for latest v142 build tools (x86 & x64)
- [ ] C++/CLI support for v142 build tools (14.25)
- [ ] C++ Modules for v142 build tools (x64/x86 – experimental)
- [ ] C++ Clang tools for Windows (9.0.0 - x64/x86)
- [ ] Windows 10 SDK (10.0.17763.0)
- [ ] Windows 10 SDK (10.0.17134.0)
- [ ] Windows 10 SDK (10.0.16299.0)
- [ ] MSVC v141 - VS 2017 C++ x64/x86 build tools (v14.16)
- [ ] MSVC v140 - VS 2015 C++ build tools (v14.00)
#### Problem description
`import pandas` fails on my `master` branch:
```
import pandas
Traceback (most recent call last):
File "C:\Users\Joey\pandas-joooeey\pandas\__init__.py", line 32, in <module>
from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
File "C:\Users\Joey\pandas-joooeey\pandas\_libs\__init__.py", line 13, in <module>
from pandas._libs.interval import Interval
ModuleNotFoundError: No module named 'pandas._libs.interval'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<ipython-input-1-38d4b0363d82>", line 1, in <module>
import pandas
File "C:\Users\Joey\pandas-joooeey\pandas\__init__.py", line 40, in <module>
) from e
ImportError: C extension: No module named 'pandas._libs.interval' not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first.
```
#### Output of ``pd.show_versions()``
N/A -- cannot even import Pandas.
The fork from pandas-dev was made in April 2020.
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MDExOlB1bGxSZXF1ZXN0NDIxMDIzMzEw
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REF: make operate_blockwise into a BlockManager method
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| 2020-05-20T21:14:46Z
| 2020-05-22T20:27:56Z
| 2020-05-22T20:18:04Z
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MEMBER
| null |
More appropriate abstraction.
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REF: Make QuarterOffset a cdef class
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MDU6SXNzdWU2MjIxMzIzNjU=
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|
BUG: querying AWS athena via pyathena connection produces error
|
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| 2020-05-20T22:50:38Z
| 2021-08-07T06:35:24Z
| 2021-08-07T06:35:23Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
from sqlalchemy.engine import create_engine
import pandas as pd
# %%
connection_params = {
's3_staging_dir': PUT_AN_S3_PATH_HERE,
'aws_region': 'us-west-2',
'aws_key_id': OMITTED_KEY,
'aws_secret': OMITTED_SECRET,
'dbname': PUT_ANY_ATHENA_DB_NAME}
url_string = 'awsathena+rest://{aws_key_id}:{aws_secret}@athena.{aws_region}.amazonaws.com:443/{dbname}?s3_staging_dir={s3_staging_dir}'
# %%
engine = create_engine( url_string.format(**connection_params))
conn = engine.connect()
# %%
conn.execute(""" drop table if exists tmp1""")
conn.execute("""
create external table tmp1( a int, b string )
stored as parquet
location 's3://ml-lahaus/tmp1'
""")
conn.execute("""
insert into tmp1 values (4, 'badad')
""")
# %% A query using regular conn.execute method
result = conn.execute("""select * from tmp1 where b='badad'""")
for row in result:
print(row) # shows expected result
# %% Last cell: same query passed to pd.read_sql
df = pd.read_sql( """select * from tmp1 where b='badad'""", conn)
print( df ) #also shows expected result but at the same time scary error trace shows in console
# %%
```
#### Problem description
The code works perfectly fine up to the line marked with `# %% Last cell`. The code after that produces an exception saying 'Failed to execute query', and the corresponding stack trace which I paste below. However, this exception seems to be happening in a different thread or something like that as the code actually finishes successfully, and the "df" variable is actually bound to a DataFrame with the right data in it, which is at the very least puzzling and disorienting for the data analyst.
Exception produced by last cell (in a different thread?)
```
Failed to execute query.
Traceback (most recent call last):
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/pyathena/common.py", line 236, in _execute
**request
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/pyathena/util.py", line 344, in retry_api_call
return retry(func, *args, **kwargs)
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/tenacity/__init__.py", line 409, in call
do = self.iter(retry_state=retry_state)
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/tenacity/__init__.py", line 356, in iter
return fut.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 428, in result
return self.__get_result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/tenacity/__init__.py", line 412, in call
result = fn(*args, **kwargs)
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/teo/git/ml-services/venv-tmp/lib/python3.7/site-packages/botocore/client.py", line 635, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.errorfactory.InvalidRequestException: An error occurred (InvalidRequestException) when calling the StartQueryExecution operation: line 12:63: mismatched input 'badad' expecting {<EOF>, '.', '[', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'UNION', 'EXCEPT', 'INTERSECT', '+', '-', '*', '/', '%', '||'}
```
The exception shows up when query contains literal string enclosed in single quotes. It doesn't show otherwise.
#### Expected Output
Expected no puzzling exception to be shown.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : sv_SE.UTF-8
LOCALE : sv_SE.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
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 : 2.8.5 (dt dec pq3 ext lo64)
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 : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
I also paste other possible relevant package versions below:
```
$ pip freeze
boto3==1.13.14
botocore==1.16.14
docutils==0.15.2
future==0.18.2
jmespath==0.10.0
joblib==0.15.1
lahaus-ml-shared==0.1
numpy==1.18.4
pandas==1.0.3
psycopg2-binary==2.8.5
PyAthena==1.10.7
python-dateutil==2.8.1
python-dotenv==0.13.0
pytz==2020.1
s3transfer==0.3.3
scikit-learn==0.23.1
scipy==1.4.1
six==1.14.0
SQLAlchemy==1.3.17
tenacity==6.2.0
threadpoolctl==2.0.0
urllib3==1.25.9
```
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MDExOlB1bGxSZXF1ZXN0NDIxMTAwNDU3
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REF: move get_rule_month to libparsing to simplify dep structure
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MEMBER
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REF: make BusinessMixin a cdef class
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MEMBER
| null |
Getting the pickle working on this one was a PITA, hopefully the worst is behind us.
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MDExOlB1bGxSZXF1ZXN0NDIxMTI5ODMx
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CLN: always dispatch-to-series
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|
MEMBER
| null |
A couple more cleanups follow this.
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REF: put cast_from_unit in conversion to de-circularize cimports
|
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MDU6SXNzdWU2MjIzNjI4ODQ=
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|
DOC: improve pie chart example
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[] | null | 6
| 2020-05-21T09:10:02Z
| 2020-05-23T04:22:05Z
| 2020-05-23T04:22:05Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.plot.pie.html
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
This example doesn't look great:
The legend covers the labels, and the two pies are so close that "mercury" is hard to read.
#### Suggested fix for documentation
Make a more readable plot
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BUG: No version exists that can be used with Python 3.5 and Python 3.8
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| 3
| 2020-05-21T09:38:57Z
| 2020-05-21T17:39:08Z
| 2020-05-21T10:56:12Z
|
NONE
| null |
Since the codebase started using f-strings around version 1.0.0, the only latest version that can be used with Python 3.5 is 0.25.3.
On the other hand, if another package specified dependency as `pandas==0.25.3`, PyPI will not have a pre-compiled Wheel for that (only source distribution). However, compiling those sources fails with modern GCC / CLang and Python 3.8.
Our code must support Python 3.5 because that's what in most currently supported Linux distributions. (eg. Ubuntu 16 LTE). And will have to, at least for a few years.
---
As an aside, I believe f-strings to be a horrible idea, but I cannot force my opinions on you... I'd still wish there was a way to at least remove them from `setup.py`, so that I could install the package using old version of Python (and, hope that I won't hit the code path that uses f-strings when using the library).
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dt.total_seconds() stores float with appending 0.00000000000001
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| 2020-05-21T10:28:16Z
| 2021-03-26T05:57:36Z
| null |
NONE
| null |
Simple test to show that if I have two datetime columns and use dt.total_seconds() to calc the difference, values are stored with an offset of 0.00000000000001.
```
print(pd.__version__)
print(pd.show_versions())
1.0.3
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.18.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
iPython code:
```
import pandas as pd
import datetime
data = {'start':datetime.datetime(2020,1,1,12),
'end':datetime.datetime(2020,1,1,12,2)
}
df = pd.DataFrame(data,index=[0])
#try to parse to pd.to_datetime
df['end'] = pd.to_datetime(df['end'])
df['start'] = pd.to_datetime(df['start'])
print('print calc differences')
print((df['end'] - df['start']).dt.total_seconds())
print((df['end'] - df['start']).dt.total_seconds().values[0])
print('')
print('testing on normal float')
print(float(120))
print('')
```
Output:
```
print calc differences
0 120.0
dtype: float64
120.00000000000001
testing on normal float
120.0
```
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TYP: Add type hints to pd.read_html
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| 14
| 2020-05-21T13:22:05Z
| 2020-08-08T08:30:06Z
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CONTRIBUTOR
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MDU6SXNzdWU2MjI1NjI2NDY=
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|
BUG: left join between df with a single index and df with a multiindex produces an inner join
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[] | null | 0
| 2020-05-21T15:02:28Z
| 2020-09-04T13:31:11Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
A = pd.DataFrame([1,2], columns=["i"]).set_index(["i"])
B = pd.DataFrame([(1,4),(3,0),(1,5)], columns=["i", "ii"]).set_index(["i", "ii"])
A.join(B, how="left")
#Empty DataFrame
#Columns: []
#Index: [(1, 4), (1, 5)]
```
#### Problem description
Joining a df with 1 index and a df with a multiindex always generates an inner join, no matter the value of "how". In this case index-value 2 from the left df is missing, while it should be kept.
The same happens when just joining the indexes. This problem does not happen when joining 2 multi-indexes.
It seems something in the implementation in file pandas\core\indexes\base.py , class Index, method _join_level goes wrong. The generated new codes are either calculated wrong or don't take into account the join-method. But that's as far as I've gotten.
#### Expected Output
Empty DataFrame
Columns: []
Index: [(1, 4), (1, 5), (2, nan)]
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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|
ENH: categorical scatter plot
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| 25
| 2020-05-21T15:16:01Z
| 2021-01-27T16:04:56Z
| 2021-01-27T15:47:43Z
|
MEMBER
| null |
- [ ] closes #12380 (this idea was mentioned as a comment here). Also, closes #31357 (I hadn't noticed found that issue when I first opened this)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Here's what this would look like:
Was just hoping to get initial feedback on whether this would be welcome
Somethings I still need to do:
- [ ] add docstring / annotations to `_plot_colorbar`
- [x] `test_fash.sh` passes locally but there are some CI failures, need to reproduce / figure out why (those tests pass now, failure was probably unrelated)
- [ ] add an example of this functionality to the docs
- [ ] if ints are passed, treat same way as categorical
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fix to is_string
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| null |
- [X] closes #34295
- [x] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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|
BUG: `is_string_dtype` incorrectly identifies categorical data
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| 0
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| 2020-05-21T20:53:54Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
s = pd.Series(['a', 'b', 'c'], dtype='category')
pdt.is_string_dtype(s)
>>> True
```
#### Problem description
The current implementation of `is_string_dtype` incorrectly evaluates to True for categorical series.
#### Expected Output
```python
s = pd.Series(['a', 'b', 'c'], dtype='category')
pdt.is_string_dtype(s)
>>> False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.2
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 : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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DOC:updated pie plot legend overlap issue, closes #34288
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| 2020-05-23T04:22:16Z
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|
CONTRIBUTOR
| null |
- [X] closes #34288
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: Subsequent calls to df.sub() are much faster than the first call
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| 8
| 2020-05-21T16:12:37Z
| 2020-05-27T13:11:12Z
| 2020-05-26T22:52:51Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
# Building some general structure
my_date_range = pd.date_range('20200101 00:00', '20200102 0:00', freq='S')
level_0_names = list(str(i) for i in range(30))
#level_0_names = list(range(30))
index = pd.MultiIndex.from_product([level_0_names, my_date_range])
column_names = ['col_1', 'col_2']
# Building a df that represents some value over time (think sensors)
# Indexed by sensor and time
value_df = pd.DataFrame(np.random.rand(len(index),2), index=index, columns=column_names)
# Build a reference df for the reference value the sensor can take (like its max)
# Indexed by sensor
ref_df = pd.DataFrame(np.random.randint(1, 10, (len(level_0_names), 2)),
index = level_0_names,
columns=column_names)
# We now want to consider for each time index in value_df what is the deviation of the value observed wrt to the ref value
# In a notebook, this first execution will be slow: 8-10s on my machine
# %%time
value_df.sub(ref_df, level=0)
# This second execution will be fast: 100-150ms
# %%time
value_df.sub(ref_df, level=0)
# For reference, this is NOT the problem, the following lines would produce the same output
# On my machine it takes ~2s
# %%time
same_w_merge = pd.merge(left = value_df.reset_index(level=1), right = ref_df, right_index=True, left_index=True)
same_w_merge['col_1_x'] -= same_w_merge['col_1_y']
same_w_merge['col_2_x'] -= same_w_merge['col_2_y']
same_w_merge = same_w_merge.drop(columns = ['col_1_y', 'col_2_y'])
same_w_merge = same_w_merge.rename({'col_1_x':'col_1', 'col_2_x': 'col_2'})
same_w_merge = same_w_merge.set_index('level_1', append=True).sort_index()
```
#### Problem description
There is a significant difference in speed between the first and second call to `sub` (which are the same instruction) in the code above. I don't understand where this is coming from. In particular why this is notably slower than merge (whose performance remains consistent).
Upon investigation, I noticed that the difference between runs is much smaller if value_df.index.level[0] is of type int (80ms for the first run 60ms for the subsequent)
#### Expected Output
Current output is correct, speed of first call is the issue here
#### Output of ``pd.show_versions()``
<details>
Bug reproduced here on a conda/ OS X install for simplicity but can confirm it exists as well in a Ubuntu Based Docker
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
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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MDU6SXNzdWU2MjI2NDMxMjA=
| 34,298
|
sum() after groupby returns different value compared to regular sum()
|
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| 2020-05-21T17:00:59Z
| 2020-08-27T02:57:54Z
| 2020-05-24T17:52:00Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
np.random.seed(0)
df = pd.DataFrame(np.random.rand(253, 2) * 254, columns=['a', 'b'])
df['type'] = 'test'
sum_mult_v1 = df.assign(mult=(lambda x: x.a * x.b)).groupby('type')['mult'].sum()[0]
sum_mult_v2 = (df['a'] * df['b']).sum()
print(sum_mult_v1)
print(sum_mult_v2)
print(sum_mult_v1 == sum_mult_v2)
```
#### Problem description
The output of the code is:
```
4010049.3807103755
4010049.3807103736
False
```
For some reason, the summation of values after groupby is different from the same operation done without a groupby. I understand that there is no point in grouping by a column that only has one value, but I wonder if there is something off with the summation function after groupby?
#### Expected Output
The expected output would be having the same numbers.
```
4010049.3807103736
4010049.3807103736
True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
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 : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : 0.49.1
</details>
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|
BUG: TestMergeDtypes.test_merge_on_ints_floats_warning fails (Linux/openSUSE/Factory)
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|
NONE
| null |
- [X] I have checked that this issue has not already been reported (cannot find any ``test_merge`` issue)
- [X] I have confirmed this bug exists on the latest version of pandas (testing on 1.0.3, which is the latest released).
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas. (This happened while building the package for openSUSE, so we care to use only officially released versions).
#### Problem description
While running the test suite, this one test fails consistently:
```
[ 363s] + export PYTHONPATH=/home/abuild/rpmbuild/BUILDROOT/python-pandas-1.0.3-0.i386/usr/lib/python3.8/site-packages
[ 363s] + PYTHONPATH=/home/abuild/rpmbuild/BUILDROOT/python-pandas-1.0.3-0.i386/usr/lib/python3.8/site-packages
[ 363s] + xvfb-run py.test-3.8 -n auto -v /home/abuild/rpmbuild/BUILDROOT/python-pandas-1.0.3-0.i386/usr/lib/python3.8/site-packages/pandas/tests -k 'not test_oo_optimizable and not test_encode_non_c_locale and not test_maybe_promote_int_with_int'
[ 368s] ============================= test session starts ==============================
[ 368s] platform linux -- Python 3.8.2, pytest-5.3.5, py-1.8.1, pluggy-0.13.1 -- /usr/bin/python3
[ 368s] cachedir: .pytest_cache
[ 368s] hypothesis profile 'ci' -> deadline=timedelta(milliseconds=500), suppress_health_check=[HealthCheck.too_slow], database=DirectoryBasedExampleDatabase('/home/abuild/rpmbuild/BUILD/pandas-1.0.3/.hypothesis/examples')
[ 368s] rootdir: /home/abuild/rpmbuild
[ 368s] plugins: forked-1.1.3, hypothesis-5.11.0, xdist-1.32.0, mock-3.1.0
[ 1070s] =================================== FAILURES ===================================
[ 1070s] ______________ TestMergeDtypes.test_merge_on_ints_floats_warning _______________
[ 1070s] [gw0] linux -- Python 3.8.2 /usr/bin/python3
[ 1070s]
[ 1070s] self = <pandas.tests.reshape.merge.test_merge.TestMergeDtypes object at 0x9419e988>
[ 1070s]
[ 1070s] def test_merge_on_ints_floats_warning(self):
[ 1070s] # GH 16572
[ 1070s] # merge will produce a warning when merging on int and
[ 1070s] # float columns where the float values are not exactly
[ 1070s] # equal to their int representation
[ 1070s] A = DataFrame({"X": [1, 2, 3]})
[ 1070s] B = DataFrame({"Y": [1.1, 2.5, 3.0]})
[ 1070s] expected = DataFrame({"X": [3], "Y": [3.0]})
[ 1070s]
[ 1070s] with tm.assert_produces_warning(UserWarning):
[ 1070s] result = A.merge(B, left_on="X", right_on="Y")
[ 1070s] tm.assert_frame_equal(result, expected)
[ 1070s]
[ 1070s] with tm.assert_produces_warning(UserWarning):
[ 1070s] result = B.merge(A, left_on="Y", right_on="X")
[ 1070s] tm.assert_frame_equal(result, expected[["Y", "X"]])
[ 1070s]
[ 1070s] # test no warning if float has NaNs
[ 1070s] B = DataFrame({"Y": [np.nan, np.nan, 3.0]})
[ 1070s]
[ 1070s] with tm.assert_produces_warning(None):
[ 1070s] result = B.merge(A, left_on="Y", right_on="X")
[ 1070s] > tm.assert_frame_equal(result, expected[["Y", "X"]])
[ 1070s]
[ 1070s] ../../BUILDROOT/python-pandas-1.0.3-0.i386/usr/lib/python3.8/site-packages/pandas/tests/reshape/merge/test_merge.py:1484:
[ 1070s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1070s]
[ 1070s] self = <contextlib._GeneratorContextManager object at 0x9419e340>, type = None
[ 1070s] value = None, traceback = None
[ 1070s]
[ 1070s] def __exit__(self, type, value, traceback):
[ 1070s] if type is None:
[ 1070s] try:
[ 1070s] > next(self.gen)
[ 1070s] E AssertionError: Caused unexpected warning(s): [('RuntimeWarning', RuntimeWarning('invalid value encountered in equal'), '/home/abuild/rpmbuild/BUILDROOT/python-pandas-1.0.3-0.i386/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py', 1091)]
[ 1070s]
[ 1070s] /usr/lib/python3.8/contextlib.py:120: AssertionError
```
#### Expected Output
All tests pass.
#### Output of ``pd.show_versions()``
<details>
[ 403s] + python3 -c 'import pandas; print(pandas.show_versions())'
[ 404s] /usr/lib/python3.8/site-packages/setuptools/version.py:1: UserWarning: Module pandah
[ 404s] import pkg_resources
[ 404s]
[ 404s] INSTALLED VERSIONS
[ 404s] ------------------
[ 404s] commit : None
[ 404s] python : 3.8.2.final.0
[ 404s] python-bits : 32
[ 404s] OS : Linux
[ 404s] OS-release : 5.6.12-1-default
[ 404s] machine : i686
[ 404s] processor : i686
[ 404s] byteorder : little
[ 404s] LC_ALL : en_US.UTF-8
[ 404s] LANG : en_US.UTF-8
[ 404s] LOCALE : en_US.UTF-8
[ 404s]
[ 404s] pandas : 1.0.3
[ 404s] numpy : 1.18.4
[ 404s] pytz : 2020.1
[ 404s] dateutil : 2.8.1
[ 404s] pip : None
[ 404s] setuptools : 44.0.0
[ 404s] Cython : 0.29.17
[ 404s] pytest : 5.3.5
[ 404s] hypothesis : 5.11.0
[ 404s] sphinx : None
[ 404s] blosc : None
[ 404s] feather : None
[ 404s] xlsxwriter : 1.2.7
[ 404s] lxml.etree : 4.5.0
[ 404s] html5lib : None
[ 404s] pymysql : None
[ 404s] psycopg2 : None
[ 404s] jinja2 : 2.11.2
[ 404s] IPython : None
[ 404s] pandas_datareader: None
[ 404s] bs4 : 4.9.0
[ 404s] bottleneck : None
[ 404s] fastparquet : None
[ 404s] gcsfs : None
[ 404s] lxml.etree : 4.5.0
[ 404s] matplotlib : None
[ 404s] numexpr : None
[ 404s] odfpy : None
[ 404s] openpyxl : 3.0.3
[ 404s] pandas_gbq : None
[ 404s] pyarrow : None
[ 404s] pytables : None
[ 404s] pytest : 5.3.5
[ 404s] pyxlsb : None
[ 404s] s3fs : None
[ 404s] scipy : None
[ 404s] sqlalchemy : 1.3.16
[ 404s] tables : None
[ 404s] tabulate : None
[ 404s] xarray : None
[ 404s] xlrd : 1.2.0
[ 404s] xlwt : 1.3.0
[ 404s] xlsxwriter : 1.2.7
[ 404s] numba : None
[ 404s] None
</details>
[Complete build log with all details of the process](https://github.com/pandas-dev/pandas/files/4664063/_log.txt.zip)
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ENH: add an option to not install the tests
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| 2020-05-21T19:07:30Z
| 2020-05-21T19:26:12Z
| 2020-05-21T19:26:12Z
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NONE
| null |
We've been using pandas as part of a project running on AWS lambda, which means we need it installed as a layer, and layers need to have a total size (when zipped) of 50MB. This is tricky with libraries like pandas which are pretty big (installed size for me is 41 MB by itself, although it compresses to less).
There are existing layers for pandas which we've been using, but recently I looked into getting all of our dependencies into a single layer that we generated ourselves. When I looked into it I was surprised to find a lot of space in pandas is from the tests: 17 MB, so nearly half, is in `pandas/tests`.
#### Describe the solution you'd like
The feature request is to add some logic to `setup.py` to make the tests an optional installation, which will save a lot of space for applications that need it.
#### API breaking implications
This shouldn't break the API unless tests are being imported/used in the main code (I certainly hope they aren't)
#### Describe alternatives you've considered
I can manually remove the tests when I package up the layer, as can anyone else who wants to save the space, but it'd be nice if this was a simple option.
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TST: GH24798 df.replace() with duplicate columns
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|
CONTRIBUTOR
| null |
- [x] xref #24798
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: read_csv() encoding latin1 gives KeyError on 1st column in new version 1.0.3
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[] | null | 11
| 2020-05-22T07:56:05Z
| 2021-08-07T22:14:59Z
| 2021-08-07T22:14:56Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
#data = pd.read_csv("dataset.csv", encoding="latin1").fillna(method="ffill")
data = pd.DataFrame({'Sentence #': ['Sentence 1','',''],
'Word': ['Thousands','of','demonstrators'],
'POS': ['NNS', 'IN', 'NNS']}) # this should be in the csv file to reproduce the error
gk = data.groupby('Sentence #')
print(gk.first())
```
#### Problem description
read_csv() encoding 'latin1' gives KeyError on 1st column in new version 1.0.3
This works perfectly in old version 0.25.3 but gives KeyError when run in new version 1.0.3
Works correctly when changed to 'utf-8'
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
Word POS
Sentence #
Sentence 1 Thousands NNS
#### 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 : AMD64 Family 22 Model 48 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.0
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 40.8.0
Cython : 0.29.13
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 : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 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 : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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BUG: Setting the index changes dtype
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| 2
| 2020-05-22T08:48:22Z
| 2020-05-23T15:01:27Z
| 2020-05-23T15:01:19Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1,2,3], 'B': [1,10,100]}).astype(np.uint16)
print(df.dtypes)
# A uint16
# B uint16
# dtype: object
df = df \
.set_index('B') \
.reset_index()
print(df.dtypes)
# B uint64
# A uint16
# dtype: object
```
#### Problem description
In the example above the type of column `B` changed from `uint16` to `uint64`. It may cause the memory issue, so expected behavior is to not change the types implicitly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 46.3.0
Cython : 0.29.18
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.8.1
bs4 : 4.6.3
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.48.0
</details>
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| 2020-05-22T09:50:44Z
| 2020-06-07T21:23:10Z
| 2020-06-07T21:23:10Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/user_guide/visualization.html#using-layout-and-targeting-multiple-axes
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
This is hard to read:
Same with the plot immediately below it:
#### Suggested fix for documentation
Could the plots be made clearer, so the labels don't cover each other?
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MDExOlB1bGxSZXF1ZXN0NDIxODYzOTA3
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ENH: nullable Float32/64 ExtensionArray
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| 23
| 2020-05-22T11:18:22Z
| 2020-11-27T14:18:27Z
| 2020-09-30T12:31:52Z
|
MEMBER
| null |
As proposed at https://github.com/pandas-dev/pandas/issues/32265#issuecomment-627004139, this is adding a float extension dtype and array (for now avoiding/ignoring the NaN-discussion), using the masked array approach following our other nullable dtypes.
Brief overview:
- This PR adds a `FloatingArray` and `Float32Dtype` / `Float64Dtype` (similar as for the nullable integer dtypes)
- I am using the "Float64" pattern with capital "F" as name for the dtype to distinguish the string name of the "float64" dtype from numpy (consistent with Int64 etc)
- For now I only added Float32 and Float64 (we can see later if we want to add others like float16)
- Upon conversion of input (both in construction as in operations such as setitem), any `np.nan` is coerced to NA (we can see later to add an option / config for this)
- The added tests are for now mostly copied from the existing integer/boolean tests, and adapted for Float
- It directly uses some of the masked ops I implemented earlier for IntegerArray, which means that eg `sum` already shows a similar perf benefit:
```python
a = np.random.randn(1_000_000)
a[np.random.randint(0, 1_000_000, 100)] = np.nan
s1 = pd.Series(a)
s2 = pd.Series(a, dtype="Float64")
In [6]: np.allclose(s1.sum(), s2.sum())
Out[6]: True
# numpy float series
In [7]: %timeit s1.sum()
2.73 ms ± 104 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# nullable float series
In [8]: %timeit s2.sum()
875 µs ± 18.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
- Still to do:
- [x] Add whatsnew + docs
- [ ] Division with Int64 dtype can now result in nullable Float Dtype instead of numpy float array
- Other todo's but that can probably be left for follow-up PRs:
- Update the `convert_dtypes` method to also return nullable floats (one question here: what with floats that are all "integer"-like? I think we want to keep returning nullable int for that, but we might want to add a parameter controlling that behaviour?)
- Some more arithmetical operations with IntegerArray/BooleanArray can now return a FloatingArray instead of numpy float array
- Ufuncs on IntegerArray/BooleanArray can return FloatingArray, ufuncs on mixture of IntegerArray/FloatArray
- Consolidate some tests that are common for integer/boolean/float
- Deduplicate more things between the Array classes and move to BaseMaskedArray (eg the arithmetic / comparison ops can probably be moved with a bit of refactoring)
- `__from_arrow__`
- ufuncs
- `coerce_to_array`, but this will probably require some refactoring
- formatting options
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CLN: consolidate arrow roundtrip tests for nullable dtypes in base masked tests
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| 4
| 2020-05-22T12:33:00Z
| 2020-05-23T06:48:21Z
| 2020-05-22T23:45:35Z
|
MEMBER
| null |
We recently splitted the test_boolean and test_integer in multiple files in each a directory, but I think a next step can be to deduplicate some of the common tests for the different nullable/masked dtypes.
Certainly given that we are going to add float tests as well (cfr https://github.com/pandas-dev/pandas/pull/34307)
Starting here with the arrow-compat/roundtrip tests for both integer/boolean.
I didn't yet write a full blown conftest.py and just defined a `data` fixture inline, since here it's just a single file. But if adding more later, we probably want to move this to a `conftest.py` (or a base test class), although this duplicates a bit the `tests/arrays/integer/conftest.py` and `tests/arrays/boolean/conftest.py`
cc @dsaxton @jbrockmendel
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BUG: StringArray non-extensible due to inconsisent assertion
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[] | null | 13
| 2020-05-22T13:04:10Z
| 2021-08-07T22:15:59Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from pandas import StringDtype
from pandas.core.arrays import StringArray
from pandas.core.dtypes.dtypes import register_extension_dtype
@register_extension_dtype
class MyExtensionDtype(StringDtype):
name = 'my_extension'
def __repr__(self) -> str:
return "MyExtensionDtype"
@classmethod
def construct_array_type(cls) -> "Type[MyExtensionStringArray]":
return MyExtensionStringArray
class MyExtensionStringArray(StringArray):
def __init__(self, values, copy=False):
super().__init__(values, copy)
self._dtype = MyExtensionDtype()
series = pd.Series(["test", "test2"], dtype="my_extension")
assert series.dtype == 'my_extension'
```
Results in
`assert dtype == "string" AssertionError`
#### Problem description
It should be possible to extend the StringDtype/StringArray for users to design efficient subtypes. I believe that the the AssertionError is a bug and not intended, as pandas wants to have extensible dtypes, because there is the ExtensionDtype.
#### Expected Output
The code above should pass without errors.
PR with fix on it's way.
#### Output of ``pd.show_versions()``
<details>
pandas v1.0.3
</details>
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Fix: StringArray non-extensible due to inconsisent assertion
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| 2020-12-03T13:34:57Z
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NONE
| null |
Fix for #34309
- [X] closes #34309
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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BUG: op(frame, series) raising NotImplementedError with extension dtypes
|
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| 3
| 2020-05-22T13:28:58Z
| 2020-05-25T17:59:20Z
| 2020-05-25T17:59:20Z
|
MEMBER
| null |
```
In [1]: df = pd.DataFrame(np.random.randint(0, 100, (10, 3)), dtype="Int64", columns=['a', 'b', 'c'])
In [2]: s = pd.Series([1, 2, 3], dtype="int64", index=["a", "b", "c"])
In [3]: df + s
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-3-c26e56e5136b> in <module>
----> 1 df + s
~/scipy/pandas/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
715 axis = self._get_axis_number(axis) if axis is not None else 1
716 new_data = _combine_series_frame(
--> 717 self, other, pass_op, axis=axis, str_rep=str_rep
718 )
719 else:
~/scipy/pandas/pandas/core/ops/__init__.py in _combine_series_frame(left, right, func, axis, str_rep)
516
517 array_op = get_array_op(func, str_rep=str_rep)
--> 518 bm = left._mgr.apply(array_op, right=rvalues.T, align_keys=["right"])
519 return type(left)(bm)
520
~/scipy/pandas/pandas/core/internals/managers.py in apply(self, f, align_keys, **kwargs)
393
394 if callable(f):
--> 395 applied = b.apply(f, **kwargs)
396 else:
397 applied = getattr(b, f)(**kwargs)
~/scipy/pandas/pandas/core/internals/blocks.py in apply(self, func, **kwargs)
334 """
335 with np.errstate(all="ignore"):
--> 336 result = func(self.values, **kwargs)
337
338 return self._split_op_result(result)
~/scipy/pandas/pandas/core/ops/array_ops.py in arithmetic_op(left, right, op, str_rep)
198 if should_extension_dispatch(lvalues, rvalues) or isinstance(rvalues, Timedelta):
199 # Timedelta is included because numexpr will fail on it, see GH#31457
--> 200 res_values = op(lvalues, rvalues)
201
202 else:
~/scipy/pandas/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~/scipy/pandas/pandas/core/arrays/integer.py in integer_arithmetic_method(self, other)
578
579 if getattr(other, "ndim", 0) > 1:
--> 580 raise NotImplementedError("can only perform ops with 1-d structures")
581
582 if isinstance(other, IntegerArray):
NotImplementedError: can only perform ops with 1-d structures
In [4]: df.astype(int) + s
Out[4]:
a b c
0 86 53 66
1 77 26 56
2 11 5 45
3 97 21 84
4 20 72 38
5 62 45 7
6 91 34 20
7 26 5 98
8 18 55 66
9 86 33 80
```
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REGR: fix op(frame, series) with extension dtypes
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| 5
| 2020-05-22T13:51:01Z
| 2020-05-25T17:58:47Z
| 2020-05-25T17:43:35Z
|
MEMBER
| null |
Closes #34311
cc @jbrockmendel
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DEPR: pd.lreshape
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| 2
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| 2020-05-22T17:52:41Z
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/33418#pullrequestreview-390789474
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DEPR: pd.wide_to_long
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/33418#pullrequestreview-390789474
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EHN: Implement closed=left|right|neither in DataFrame.rolling for fixed windows
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| 3
| 2020-05-22T14:32:06Z
| 2020-10-22T00:21:11Z
| 2020-10-22T00:21:11Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = [0, 1, 1, 0, 0, 1, 0, 1]
df = pd.DataFrame({'binary_col': data}, index=pd.date_range(start='2020-01-01', freq="min", periods=len(data)))
rolling = df.rolling(window=len(df), closed='left', min_periods=1)
rolling.mean()
```
#### Problem description
The rolling window ignores the closed='left' parameter. Since it was supposed to be closed only on the left side, the current right side of the window should not be used on the calculations. But it turns out it is.
#### Original dataframe
| index | binary_col |
|---------------------|------------|
| 2020-01-01 00:00:00 | 0 |
| 2020-01-01 00:01:00 | 1 |
| 2020-01-01 00:02:00 | 1 |
| 2020-01-01 00:03:00 | 0 |
| 2020-01-01 00:04:00 | 0 |
| 2020-01-01 00:05:00 | 1 |
| 2020-01-01 00:06:00 | 0 |
| 2020-01-01 00:07:00 | 1 |
#### Actual output
| index | binary_col |
|---------------------|------------|
| 2020-01-01 00:00:00 | 0.000000 |
| 2020-01-01 00:01:00 | 0.500000 |
| 2020-01-01 00:02:00 | 0.666667 |
| 2020-01-01 00:03:00 | 0.500000 |
| 2020-01-01 00:04:00 | 0.400000 |
| 2020-01-01 00:05:00 | 0.500000 |
| 2020-01-01 00:06:00 | 0.428571 |
| 2020-01-01 00:07:00 | 0.500000 |
#### Expected Output
| index | binary_col |
|---------------------|------------|
| 2020-01-01 00:00:00 | NaN |
| 2020-01-01 00:01:00 | 0.000000 |
| 2020-01-01 00:02:00 | 0.500000 |
| 2020-01-01 00:03:00 | 0.666667 |
| 2020-01-01 00:04:00 | 0.500000 |
| 2020-01-01 00:05:00 | 0.400000 |
| 2020-01-01 00:06:00 | 0.500000 |
| 2020-01-01 00:07:00 | 0.428571 |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-70-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.3
</details>
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| 623,277,600
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MDU6SXNzdWU2MjMyNzc2MDA=
| 34,316
|
BUG: don't plot colorbar if c is column containing colors
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| 0
| 2020-05-22T15:11:32Z
| 2020-06-14T14:46:35Z
| 2020-06-14T14:46:34Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import matplotlib as mpl
import numpy as np
import pandas as pd
CMAP = "viridis"
df = pd.DataFrame(
[[5.1, 3.5], [4.9, 3.0], [7.0, 3.2], [6.4, 3.2], [5.9, 3.0]],
columns=["length", "width"],
)
df["species"] = ['r', 'r', 'g', 'g', 'b']
df.plot.scatter(
x=0, y=1, c="species", cmap=CMAP,
)
```
#### Problem description
This produces
The colorbar to the right of the plot doesn't make any sense here
#### Expected Output
Same plot, but without the colorbar
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 6ad157e937624854312934a3e2df8437083cf957
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
Version : #44~18.04.2-Ubuntu SMP Thu Apr 23 14:27:18 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+1623.g6ad157e93
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 3.0.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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