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MDExOlB1bGxSZXF1ZXN0NDM1MDI2MjI3
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DOC: move 'Other API changes' under correct section
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| 1
| 2020-06-16T07:27:09Z
| 2020-06-16T13:00:35Z
| 2020-06-16T12:53:05Z
|
MEMBER
| null |
The "Other API changes" bullet points were split in two (one part under "Other API changes", and one part directly under "Backwards incompatible API changes"), and in previous whatsnew files, the "Other API changes" is also a subsection of "Backwards incompatible API changes".
So moved a few things around to make this consistent.
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MDExOlB1bGxSZXF1ZXN0NDM1MDM2NTg5
| 34,818
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CLN: remove unused args/kwargs in BlockManager.reduce
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| 2
| 2020-06-16T07:48:52Z
| 2020-06-16T15:33:28Z
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|
MEMBER
| null |
Small clean-up broken off from https://github.com/pandas-dev/pandas/pull/32867 cc @jbrockmendel
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MDExOlB1bGxSZXF1ZXN0NDM1MDk5MDU3
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Backport PR #34733 on branch 1.0.x (BUG: Fixed Series.replace for EA with casting)
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MEMBER
| null |
xref #34733
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MDU6SXNzdWU2Mzk1MjY2MTU=
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BUG: Slicing on non-monotonic DatetimeIndex inconsistencies
|
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| 2020-06-16T09:46:09Z
| 2020-07-02T14:39:50Z
| 2020-07-02T14:25:50Z
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NONE
| null |
Looked for this bug in the opened issues, but couldn't find it. I work with v.0.25.3, but checked that this also occurs in v.1.0.4.
```python
ser = pd.Series([1, 2, 3], pd.to_datetime(['2010-01-01', '2009-01-01', '2011-01-01']))
# Case 1:
ser['2009-01-01':]
# Out:
# 2009-01-01 2
# 2011-01-01 3
# dtype: int64
# Case 2:
ser['2008-01-01':]
# Out:
# 2010-01-01 1
# 2009-01-01 2
# 2011-01-01 3
# dtype: int64
# Case 3:
ser[pd.Timestamp('2008-01-01'):]
# Out:
# KeyError: Timestamp('2008-01-01 00:00:00')
```
#### Problem description
As you can see in the above examples, there are multiple inconsistencies and errors here:
1. The inconsistency in output between cases 2 & 3 (that's perhaps the worst).
2. The irrelevant (and so-very frustrating) error-message in case 3
3. The strange behavior in case 1 (it should've also included the first item).
#### Expected Output
For all three cases:
```python
# 2010-01-01 1
# 2009-01-01 2
# 2011-01-01 3
# dtype: int64
```
If not, at the very least you should do the following:
1. Treat string-timestamps and `pd.Timestamp`s in the same way (perhaps by overriding the slicing in the `DatetimeIndex` to cast strings to `pd.Timestamp`s right away).
2. Give an indicative error message. Could be something like ``"KeyError: Can not perform inexact slicing on an unordered `DatetimeIndex`"``.
3. Show a warning upon slicing that results may be inconsistent due to lack of monotonicity
I also want to add that you could implement an unordered `DatetimeIndex` that will return the expected output I wrote above by holding and ordered index in the background that holds the relative indices (e.g. in the above example ``OrderedDict({'2009-01-01': 1, '2010-01-01': 0, '2011-01-01': 2})``), and all timestamp-related-slicing will be performed on it.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-53-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.2
pytz : 2018.3
dateutil : 2.6.1
pip : 20.1.1
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU2Mzk1NDk0OTc=
| 34,821
|
BUG: read_excel(engine='openpyxl') with "unsized" XLSX issue
|
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[] | null | 6
| 2020-06-16T10:21:39Z
| 2021-02-02T23:16:02Z
| 2021-02-02T23:16:02Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
Consider code:
```python
import pandas as pd
pd.read_excel('test.xlsx', index_col=[0, 1], engine='openpyxl')
```
with some special `test.xlsx` (see below).
According to [openpyxl issue #1483](https://bitbucket.org/openpyxl/openpyxl/issues/1483) and [openpyxl documentation](https://openpyxl.readthedocs.io/en/stable/optimized.html#worksheet-dimensions), **user (i.e. Pandas) [have to](https://bitbucket.org/openpyxl/openpyxl/issues/1483#comment-57780692) call [`sheet.calculate_dimension(force=True)`](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-133) if any workbook's worksheet is "unsized"**.
(What is "worksheet is unsized"? It means: the worksheet doesn't have `DIMENSION_TAG`, see [WorkSheetParser.parse_dimensions()](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_reader.py#lines-158). When it checked? On `ReadOnlyWorksheet` object construction.)
**But Pandas doesn't do it.**
So, if the worksheet is "unsized", on reading it by `read_excel(engine='openpyxl')` we try to [get sheet data](https://github.com/pandas-dev/pandas/blob/v1.0.3/pandas/io/excel/_base.py#L443). We [iterate rows](https://github.com/pandas-dev/pandas/blob/v1.0.3/pandas/io/excel/_openpyxl.py#L539) falling down into openpyxl: [1](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-29) -> [2](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/worksheet.py#lines-454) -> [3](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-27) -> [4](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/worksheet.py#lines-401) (with `max_col == None`) -> [5](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/worksheet.py#lines-433) -> [6](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-185) (it returns `self._max_column == None`) -> [7](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/worksheet.py#lines-436) -> [8](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-57) (with `max_col == None`) -> [9](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-89) -> [10](https://bitbucket.org/openpyxl/openpyxl/src/3.0.3/openpyxl/worksheet/_read_only.py#lines-10) (with `max_col == None`). And in [these lines](https://bitbucket.org/openpyxl/openpyxl/src/ca7b1baf75f2fc6b270320ea91d82404f5039e1e/openpyxl/worksheet/_read_only.py#lines-107:108):
```python
max_col = max_col or row[-1]['column']
row_width = max_col + 1 - min_col
```
we have: **row's cells number (`row_width`) is individual for each row (`row`)**.
Now consider an _unsized_ Excel table (I have a real file but it's private; after saving in LibreOffice it's become sized):
We get [sheet data](https://github.com/pandas-dev/pandas/blob/v1.0.3/pandas/io/excel/_base.py#L443):
```python
[ [`A1`],
[ 'B1', 'B2' ] ]
```
But `index_col=[0, 1]` **suppose to have >=2 cols in each row**:
```
Traceback (most recent call last):
File "test.py", line 6, in <module>
pd.read_excel('test.xlsx', index_col=[0, 1], engine='openpyxl')
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 480, in parse
last = data[offset][col]
IndexError: list index out of range
```
**IMHO, patch could be:**
```diff
diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py
index c4327316d..3efbf4abc 100644
--- a/pandas/io/excel/_openpyxl.py
+++ b/pandas/io/excel/_openpyxl.py
@@ -536,6 +536,9 @@ class _OpenpyxlReader(_BaseExcelReader):
def get_sheet_data(self, sheet, convert_float: bool) -> List[List[Scalar]]:
data: List[List[Scalar]] = []
+
+ sheet.calculate_dimension(force=True)
+
for row in sheet.rows:
data.append([self._convert_cell(cell, convert_float) for cell in row])
```
(But it may provide [some drawbacks](https://bitbucket.org/openpyxl/openpyxl/issues/1483#comment-57780692)).
With this patch, sheet data would be:
```python
[ [`A1`, ''],
[ 'B1', 'B2' ] ]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.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 : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
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 : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
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 : None
</details>
Thanks!
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MDU6SXNzdWU2Mzk2MjE5MTg=
| 34,822
|
BUG: setting raw=True in the Dataframe.apply function causes a ValueError
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[] | null | 7
| 2020-06-16T12:23:50Z
| 2021-02-05T17:10:13Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (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
df = pd.DataFrame({"a": [100, 300], "b": [200, 400]})
def parse_node_row(row, index):
def unpack_bitmask(val):
return [np.bitwise_and(np.right_shift(val, i * 16), 0xFFFF) for i in range(4)]
arr = np.concatenate(np.apply_along_axis(unpack_bitmask, 0, row)).ravel()
return pd.Series(arr, index=index)
iterables = [df.columns, ["gpu{}".format(i) for i in range(4)]]
midx = pd.MultiIndex.from_product(iterables, names=['node', 'gpu'])
ndf = df.apply(parse_node_row, axis="columns", result_type="expand", args=(midx,))
```
#### Problem description
I am working on a dataset where values are bitmaskes, each 64bit integer is actually 4x16 (one per GPU per node). The format of the dataset is out of my control.
So, I wrote the code above to create a new dataframe that created a multi index with the data split.
It works fine and all, but as I am solely using numpy functions in `parse_node_row`, I added `parse=True` in the `apply` call. However, this causes my script to crash! The minimal reproducible example above shows
```
ValueError: Shape of passed values is (2, 8), indices imply (2, 2)
```
But runs just fine when `raw=False`. What gives?
There is no mention in [the docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html) of any side-effects of using raw=True besides you getting a numpy array in your apply function, which is completely fine for me.
#### Expected Output
More performance!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1
Cython : 0.29.20
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
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
</details>
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ENH: Pass arguments to `to_pandas` in `read_csv(engine="pyarrow")
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| 2020-06-16T13:07:52Z
| 2020-09-04T14:04:25Z
| null |
CONTRIBUTOR
| null |
As a follow-up for #31817, we should enable the user to pass arguments to the `to_pandas` call when converting the result from `pyarrow.Table` to `pandas.DataFrame`. This would enable e.g. the user to directly construct `ExtensionArray` instances using the `types_mapper` argument.
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DOC: to_sql accepting Engine vs. Connectable
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| 2020-06-25T05:10:27Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html
#### Documentation problem
[Documentation](https://github.com/pandas-dev/pandas/blob/1ce1c3c1ef9894bf1ba79805f37514291f52a9da/pandas/core/generic.py#L2530) says that `con` is to be a "sqlalchemy.engine.Engine or ..." while [source](https://github.com/pandas-dev/pandas/blob/f34fd588cd9facfa82a792fb570fb8a56f11f6c7/pandas/io/sql.py#L539) says that `con` can be a "SQLAlchemy connectable(engine/connection) or ..."
#### Suggested fix for documentation
Documentation should mention that a SQLAlchemy `Connection` is acceptable because some operations like [an upsert via a temporary table in SQL Server](https://stackoverflow.com/q/62388767/2144390) need to ensure that the same DBAPI connection is used for the entire operation. (SQL Server local temp tables are only visible to the connection/session that created them).
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MDU6SXNzdWU2Mzk3MzQ1MDg=
| 34,825
|
BUG: pd.concat(..., copy=False) still causes copy on block consolidation.
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| false
| null |
[] | null | 1
| 2020-06-16T14:54:27Z
| 2020-06-16T21:36:23Z
| null |
NONE
| null |
#### Problem description
When concatenating columns of the same dtype, even with `copy=False` option, the columns are consolidated together which involves a copy and a costly `vstack`. The performance is actually worse for `copy=False` than the default `copy=True` which is misleading.
There are use cases where consolidated data is not required for an application, so this unneeded performance penalty is undesired.
#### Sample Program
```python
import time
import pandas as pd
template_series = pd.Series(list(range(10000)))
series_ls = []
for i in range(1000):
series_ls.append(template_series.copy())
start_time = time.time()
df_no_copy = pd.concat(series_ls, copy=False)
print("No copy elapsed", time.time() - start_time)
start_time = time.time()
df_copy = pd.concat(series_ls, copy=True) # The default setting
print("Copy elapsed", time.time() - start_time)
```
#### Execution Time
```
No copy elapsed 0.07740044593811035
Copy elapsed 0.05434751510620117
```
Execution time is in seconds.
#### Problem Trace
The consolidation occurs as a result of:
```python
if not self.copy:
new_data._consolidate_inplace()
```
located near "pandas/core/reshape/concat.py:499".
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MDU6SXNzdWU2Mzk4MjIxODM=
| 34,826
|
BUG: pandas.read_parquet no longer accepting a file-like object
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[] | null | 1
| 2020-06-16T16:47:12Z
| 2020-08-21T17:05:17Z
| 2020-06-16T18:34:27Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
from io import BytesIO
import numpy as np
import pandas as pd
import pytest
def test_read_parquet_on_stream():
stream = BytesIO()
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.to_parquet(stream)
result = pd.read_parquet(stream)
assert np.array_equal(result.values, df.values)
```
#### Problem description
The documentation of `pandas.read_parquet()` (see [1]) says
> Parameters: path: str, path object or file-like object
It is quite handy to be able to use a stream as parameter. It used to work with pandas version 1.0.3, but causes the error below for pandas version 1.0.4.
#### Expected Output
I expect the test to pass silently, which it does when using `pandas == 1.0.3`.
#### Output
But for `pandas == 1.0.4`, the test case fails due to `stream` not being a path-like object:
>
> tests/util/test_parquet_util.py:388 (test_read_parquet_on_stream)
> def test_read_parquet_on_stream():
> stream = BytesIO()
> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
> df.to_parquet(stream)
> > result = pd.read_parquet(stream)
>
> tests/util/test_parquet_util.py:393:
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> /usr/local/lib/python3.7/dist-packages/pandas/io/parquet.py:315: in read_parquet
> return impl.read(path, columns=columns, **kwargs)
> /usr/local/lib/python3.7/dist-packages/pandas/io/parquet.py:131: in read
> path, filesystem=get_fs_for_path(path), **kwargs
> /usr/local/lib/python3.7/dist-packages/pyarrow/parquet.py:1019: in __init__
> self.paths = _parse_uri(path_or_paths)
> /usr/local/lib/python3.7/dist-packages/pyarrow/parquet.py:49: in _parse_uri
> path = _stringify_path(path)
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
>
> path = <_io.BytesIO object at 0x7f07d0e77530>
>
> def _stringify_path(path):
> """
> Convert *path* to a string or unicode path if possible.
> """
> if isinstance(path, six.string_types):
> return path
>
> # checking whether path implements the filesystem protocol
> try:
> return path.__fspath__() # new in python 3.6
> except AttributeError:
> # fallback pathlib ckeck for earlier python versions than 3.6
> if _has_pathlib and isinstance(path, pathlib.Path):
> return str(path)
>
> > raise TypeError("not a path-like object")
> E TypeError: not a path-like object
>
> /usr/local/lib/python3.7/dist-packages/pyarrow/util.py:84: TypeError
>
#### Details
Besides pandas, my `requirements.txt` contains the following relevant dependencies:
```
pyarrow ~= 0.15.1
pytest ~= 5.3.5
pyyaml ~= 5.1.2
```
My Python versions are 3.6.10 and 3.7.7.
[1]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html
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MDExOlB1bGxSZXF1ZXN0NDM1MzQ1ODk5
| 34,827
|
BUG fix _Unstacker int32 limit in dataframe sizes (pandas-dev#26314)
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[] | null | 5
| 2020-06-16T16:52:04Z
| 2020-10-12T16:07:34Z
| 2020-09-15T08:41:35Z
|
NONE
| null |
- [x] closes #26314
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I have tested it with :
`df = pd.DataFrame(np.random.randint(low=0, high=1500000, size=(90000, 2)), columns=['a', 'b'])`
`df.set_index(['a', 'b']).unstack()`
I am not sure if I should add it as a test, it requires quite some memory. I am also hesitant about where the test should be located in case it's added.
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REF: remove libfrequencies
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| 2020-06-16T17:30:02Z
| 2020-06-16T20:51:28Z
| 2020-06-16T20:47:02Z
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NDM1NDAyMjEy
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|
Add support for gitpod
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[] | null | 3
| 2020-06-16T18:43:09Z
| 2020-07-19T19:41:20Z
| 2020-07-19T19:41:20Z
|
NONE
| null |
This PR proposes to add support for Gitpod, which can provide (new) contributors with one-click environment to use for working on the pandas code base.
The PR includes two main additions:
- .gitpod.yml - Config file for gitpod that specifies which image/Dockerfile to use for gitpod and which init command(s) to run when booting the pod.
- .gitpod.Dockerfile - Dockerfile used to build a base image for the pod. Partially adapted from the existing Dockerfile in the repo.
Some things maybe still to consider:
- Gitpod has some options for providing pre-built workspaces (https://www.gitpod.io/docs/prebuilds/). This may be interesting to enable.
- There is some duplication between the two dockerfiles, but unfortunately the VS Code Dockerfile is not entirely compatible with the gitpod one. As such, the duplication may not be a bad thing.
- [x] closes #xxxx (N/A)
- [x] tests added / passed (N/A)
- [x] passes `black pandas` (N/A)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (N/A)
- [x] whatsnew entry (N/A)
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| 639,972,902
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MDExOlB1bGxSZXF1ZXN0NDM1NDY3MzYw
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REF: move registry, Registry to dtypes.base
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| 8
| 2020-06-16T21:07:39Z
| 2020-07-11T00:27:07Z
| 2020-07-10T23:54:35Z
|
MEMBER
| null |
These are more closely related to ExtensionDtype than to our internal EADtypes. More concretely, they are low-dependency, while dtypes.dtypes has some unfortunate circular dependencies.
I'd also like to move pandas_dtype and is_dtype_equal from dtypes.common so that those can be stricty "above" dtypes.dtypes in the dependency structure. Saving that for another pass.
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MDExOlB1bGxSZXF1ZXN0NDM1NDY4MzA4
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CLN: GH29547 change string formatting with f-strings (6 files changed)
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| 1
| 2020-06-16T21:09:49Z
| 2020-06-20T09:01:29Z
| 2020-06-18T13:59:00Z
|
CONTRIBUTOR
| null |
Replace old string formatting syntax with f-strings #29547
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Changed 6 files:
- pandas/tests/io/parser/test_header.py
- pandas/tests/io/test_sql.py
- pandas/tests/io/test_html.py
- pandas/tests/reductions/test_reductions.py
- pandas/tests/reshape/test_melt.py
- pandas/tests/scalar/timedelta/test_timedelta.py
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| 34,832
|
BUG: DataFrame.__setitem__ creates object-dtype array for extension type scalars
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| 6
| 2020-06-16T21:14:35Z
| 2020-09-10T13:34:43Z
| 2020-09-10T13:34:42Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({
'a': [1, 2]
})
df['b'] = pd.Period('2020-01')
df['c'] = df['b'] + 0
df['d'] = df['a'] + df['b']
df.dtypes
```
#### Problem description
Not sure if this is best classified as a bug, but wasn't sure how to classify it since it certainly defies my expectations of how the Pandas package would work and would like to understand what's happening.
Essentially the issue is that, in the code snippet above, `df['b']`'s is considered an object rather than a `period[M]`. Stranger to me still is that both `df['c'] and df['d']` are correctly set to `period[M]` columns. This seems very odd to me, that when you combine an `object` column with a `int`, you get a `period[M]` column.
Can you help me understand what the logic is here? Is it possible to update Pandas so that when someone does `df['b'] = pd.Period('2020-01')` the resulting column is a `period[M]` type?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : 2.4.2
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.4.1
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 : None
</details>
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MDU6SXNzdWU2NDAwMTk5NDM=
| 34,833
|
BUG: resample seems to convert hours to 00:00
|
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"name": "Resample",
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[] | null | 8
| 2020-06-16T22:47:30Z
| 2020-06-22T14:35:51Z
| 2020-06-18T00:49:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported. (As far as I can see by using the search)
- [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
import datetime as dt
import pandas as pd
start_time = dt.datetime(year=2018, month=6, day=7, hour=18)
end_time = dt.datetime(year=2018, month=6, day=27, hour=18)
time_index_df = pd.date_range(
start_time,
end_time,
freq="12H",
name="datetime",
).to_frame(index=False)
time_index_df["test"] = 1
time_index_df["name"] = "Name"
time_index_df = time_index_df.resample("2d", convention="end", on="datetime").mean().reset_index()
print(time_index_df["datetime"].values)
time_index_df.to_csv(f"test_resample_{pd.__version__}")
```
#### Problem description
On pandas 0.23 the behaviour of resample will keep the correct datetime (18:00 in this case) after resample.
But starting from 0.24, after resample, the datetime is now converted to (00:00).
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
On. 0.23
['2018-06-07T18:00:00.000000000' '2018-06-09T18:00:00.000000000'
'2018-06-11T18:00:00.000000000' '2018-06-13T18:00:00.000000000'
'2018-06-15T18:00:00.000000000' '2018-06-17T18:00:00.000000000'
'2018-06-19T18:00:00.000000000' '2018-06-21T18:00:00.000000000'
'2018-06-23T18:00:00.000000000' '2018-06-25T18:00:00.000000000'
'2018-06-27T18:00:00.000000000']
But Starting from 0.24 it is giving me
['2018-06-07T00:00:00.000000000' '2018-06-09T00:00:00.000000000'
'2018-06-11T00:00:00.000000000' '2018-06-13T00:00:00.000000000'
'2018-06-15T00:00:00.000000000' '2018-06-17T00:00:00.000000000'
'2018-06-19T00:00:00.000000000' '2018-06-21T00:00:00.000000000'
'2018-06-23T00:00:00.000000000' '2018-06-25T00:00:00.000000000'
'2018-06-27T00:00:00.000000000']
#### Output of ``pd.show_versions()``
<details>
0.23
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 5.3.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.23.0
pytest: None
pip: 20.0.2
setuptools: 46.0.0
Cython: None
numpy: 1.18.5
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.8
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: 2.8.5 (dt dec pq3 ext lo64)
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None
0.24
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 5.3.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.24.2
pytest: None
pip: 20.0.2
setuptools: 46.0.0
Cython: None
numpy: 1.18.5
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.8
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: 2.8.5 (dt dec pq3 ext lo64)
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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MDU6SXNzdWU2NDAwMzExOTU=
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ENH: remove restrictions to numexpr to allow `where` etc.
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[] | null | 6
| 2020-06-16T23:18:33Z
| 2021-03-21T13:06:20Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
the evaluation of a query is currently limited to the list `_mathops` while numexpr would support more, most notably a where (that would also solve [other issues simple](https://github.com/pandas-dev/pandas/issues/12945)).
I do not see any reason for this restriction. In fact, simply adding the `where` runs (at least for my use case). Why is this restriction in place? Why can't we enlarge it/directly pass it through to numexpr?
#### Describe the solution you'd like
Allow the full operator set that numexpr supports in the `pd.eval`
#### API breaking implications
Nothing
#### Alternatives
Using `.where` is an option _if you can access the dataframe directly_ (although suboptimal). However, if your selection of data is based on passing a selection string around instead of the df (several reasons for this), the latter is not feasible.
The following doesn't work:
```python
import pandas as pd
data = {'a': [1, 2, 3]}
df = pd.DataFrame({'a': [1, 2, 3]})
df.eval('where(a>2, 42, 0)')
```
whereas in `numexpr` it does
```
numexpr.evaluate('where(a>2, 42, 0)', local_dict=data)
```
we expect this to return `[0, 0, 42]`
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MDExOlB1bGxSZXF1ZXN0NDM1NTIzNTU2
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Replaced np.bool refs; fix CI failure
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| 2020-06-16T23:48:06Z
| 2020-06-30T16:05:52Z
| 2020-06-17T17:26:57Z
|
MEMBER
| null |
I mostly just replace `np.bool` with `np.bool_` though there are some other cases where I just used bool. I don't know if it matters...so happy to conform one way or another
For now just seeing if this fixes CI
Closes https://github.com/pandas-dev/pandas/issues/34848.
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|
ENH: Splitting datetime events in a df based on a datetime interval
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[] | null | 2
| 2020-06-17T00:53:04Z
| 2021-08-08T00:55:10Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I have transaction data, where events start at a given point in time and then end at a given point in time. In order to see what events are ongoing over the course of time as well as allocate time to a specified time interval, multiple rows need to be made indicating that an event is still ongoing if it spans greater than the time interval specified. (i.e. if a row starts on Monday and ends on Wednesday and my time interval is 1 day, there needs to be 3 rows, 1 for when the event began on Monday then ending at just before midnight, 1 for Tuesday at midnight ending just before the following midnight, and 1 for Wednesday at midnight to the event end time.
It would be nice if given a dataframe and indicating start column, end column, and time interval, a method returned a dataframe that made those necessary cuts and filled in the gaps with extra rows.
#### Describe the solution you'd like
```
def split_events(self, start, end, freq, show_event_id=False):
"""
Takes properly formatted pandas.DataFrame of events and adds rows for
events that cross into multiple intervals so each row falls under one interval
Args:
start (str): name of start datetime.datetime formatted column
stop (str): name of end datetime.datetime formatted column
freq (str): frequency string
show_event_id (bool): adding id column for reverse mapping
Returns:
pandas.DataFrame: formatted where events that cross over into multiple
intervals are separated into separate events
"""
>>> df = pd.DataFrame([['2020-06-01 08:06:00', '2020-06-01 09:14:00', 'bar'],
... columns=['start', 'end', 'foo'])
>>> df
start stop foo
0 '2020-06-01 08:06:00' '2020-06-01 09:14:00' bar
>>> df.split_events(start='start', end='end', freq='30m')
start stop foo
0 '2020-06-01 08:06:00' '2020-06-01 08:30:00' bar
1 '2020-06-01 08:30:00' '2020-06-01 09:00:00' bar
2 '2020-06-01 09:00:00' '2020-06-01 09:14:00' bar
>>> df.split_events(start='start', end='end', freq='30m', show_event_id=True)
id start stop foo
0 0 '2020-06-01 08:06:00' '2020-06-01 08:30:00' bar
1 0 '2020-06-01 08:30:00' '2020-06-01 09:00:00' bar
2 0 '2020-06-01 09:00:00' '2020-06-01 09:14:00' bar
```
#### API breaking implications
I see this as a standalone method so I don't believe it would affect the API
#### Describe alternatives you've considered
Searched exhaustively for similar methods that will accomplish this task and have been unable to find an existing solution. Interpolate comes close but depends on a reference column, in this instance our reference is a specified time interval.
The following link also seems to have functionality that can be leveraged to accomplish the problem but I haven't been able to find anything straightforward ([link](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases))
#### Additional context
Probably more computational intensive than it needs to be but this is a function I have been using to split events by day. One of the reasons it is done in this fashion is to make sure any additional columns passed in the df are also replicated for each row.
```python
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
def split_events_by_day(df, start='DateTimeStart', stop='DateTimeEnd'):
"""
Takes properly formatted pandas.DataFrame of events and adds rows for
events that cross into multiple days so each row falls under same day
Args:
df (pandas.DataFrame): formatted with start and stop columns formatted
as datetime.datetime
start (str): name of start datetime.datetime formatted column
stop (str): name of stop datetime.datetime formatted column
Returns:
pandas.DataFrame: formatted where events that cross over into multiple
days are separated into separate events, an
'event_id' is added for reverse mapping
"""
df['event_id'] = np.arange(len(df)) # add indexing col for events
for index, row in df.iterrows():
row = row.copy() # making copy of row for editing
# testing if event that starts and stops in the same day
if (row[start].date()) == (row[stop].date()):
pass # leave row as is
# testing if event that stops on a different day than it started
else:
# number of calendar days in timespan
days = (
row[stop].replace(hour=0,minute=0,second=0
- row[start].replace(hour=0,minute=0,second=0)
).days
next_day = (
row[start].replace(hour=0,minute=0,second=0)
+ timedelta(days=1)
)
stop_time = row[stop]
# assigning start calendar day cut to current row being evaluated
df.at[index, stop] = next_day
# adding rows for each calendar day in timespan not on start/stop
# calendar day
while days > 1:
row = row.copy()
row[start] = next_day
next_day += timedelta(days=1)
row[stop] = next_day
df = df.append(row)
days -= 1
row[start] = next_day
row[stop] = stop_time
# adding row for stop calendar day cut to current row being
# evaluated
df = df.append(row)
return df
```
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MDExOlB1bGxSZXF1ZXN0NDM1NTYxODg3
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REF: implement _shared_docs to de-circularize dependencies
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| 2020-06-24T23:51:38Z
| 2020-06-24T23:36:11Z
|
MEMBER
| null |
A side effect of this is that a bunch of pd.concat imports can be done at import-time instead of at runtime, will do this in a separate pass.
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MDU6SXNzdWU2NDAwOTUzMjU=
| 34,838
|
BUG: cut() ignores labels, include_lowest and other parameters when bins are given by IntervalIndex
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| 2020-06-17T02:38:54Z
| 2020-08-21T17:05:17Z
| 2020-06-17T03:38:43Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'A': range(1, 10, 1)})
bins_s = [1, 5, 8]
bins_i = pd.IntervalIndex.from_tuples([(1, 5), (5, 8)])
labels = ['low', 'high']
df['A_s'] = pd.cut(df['A'], bins_s, labels=labels, include_lowest=True, right=False)
df['A_i'] = pd.cut(df['A'], bins_i, labels=labels, include_lowest=True, right=False)
print(df)
```
actual output
```
A A_s A_i
0 1 low NaN
1 2 low (1.0, 5.0]
2 3 low (1.0, 5.0]
3 4 low (1.0, 5.0]
4 5 high (1.0, 5.0]
5 6 high (5.0, 8.0]
6 7 high (5.0, 8.0]
7 8 NaN (5.0, 8.0]
8 9 NaN NaN
```
#### Problem description
The cut() implementation redirects to `Categorical.from_codes` when the bins are given by an IntervalIndex ignoring labels and producing results inconsistent with an equivalent call to cut with bins given by a sequence.
#### Expected Output
One would expect `A_s` anb `A_i` to be identical given the equivalent bin specifications.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDM1NjA5ODMz
| 34,839
|
ENH: Add support for calculating EWMA with a time component
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| 3
| 2020-06-17T05:21:43Z
| 2020-07-07T00:31:46Z
| 2020-07-06T22:37:57Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This enhancement allows EWMA to be calculated relative to the timestamp in which an observation occurs instead of assuming each observation is equally spaced.
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MDU6SXNzdWU2NDAxNTc2Mzc=
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ENH: Use of NamedTuple instead of Tuple for MultiIndex
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| 2020-06-17T05:41:22Z
| 2020-08-21T17:05:18Z
| 2020-06-18T19:14:04Z
|
NONE
| null |
#### Is your feature request related to a problem?
Simply from an ergonomic point of view, when using MultiIndex, it would be worth using a list of NamedTuple instead of Tuple, so that level name can be directly used to call index values.
#### Describe the solution you'd like
Currently,
```python
import pandas as pd
midx = pd.MultiIndex.from_product([['value1','value2'],[1]], names=['level1','level2'])
df = pd.DataFrame(0,index=[1,2],columns=midx)
df.columns[0]['level1']
```
returns:
```python
TypeError: tuple indices must be integers or slices, not str
```
Would this be a NamedTuple, this would return the correct result: 'value1'
Bests,
PS: and thanks for your awesome work!
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BUG: to_parquet write partitioned DataFrame to local filesystem for other filesystems (e.g. S3)
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| 2020-06-17T06:49:42Z
| 2020-08-21T15:59:13Z
| 2020-08-21T15:59:13Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
remote = "s3://some-pandas-test-bucket/table"
pd.DataFrame([[1, 'a'], [1, 'b']], columns=['col_one', 'col_two']).to_parquet(remote, partition_cols=['col_two'])
```
#### Problem description
When a DataFrame is to be saved in object file system such as S3 as partitioned Parquet files, it saves the files in local file system instead, only a 0 byte object of is created, whereas the files are stored in the working directory as `s3:`
```
(pandas-dev) ➜ pandas git:(fix/parquet-write-dataset-s3) ✗ ls -R s3:
some-pandas-test-bucket
s3:/some-pandas-test-bucket:
table
s3:/some-pandas-test-bucket/table:
col_two=a col_two=b
s3:/some-pandas-test-bucket/table/col_two=a:
2a386111fd44400b8de1901254a7c485.parquet
s3:/some-pandas-test-bucket/table/col_two=b:
aefb0d5b907a415d943b6bcf59f2521d.parquet
```
On S3
```
(pandas-dev) ➜ pandas git:(fix/parquet-write-dataset-s3) ✗ aws s3 ls s3://some-pandas-test-bucket
2020-06-17 14:44:00 0 table
```
#### Expected Output
```
(pandas-dev) ➜ pandas git:(fix/parquet-write-dataset-s3) ✗ aws s3 ls s3://some-pandas-test-bucket --recursive --human-readable --summarize
2020-06-17 14:46:30 0 Bytes table
2020-06-17 14:46:30 1.3 KiB table/col_two=a/291b3f0d1d1f4eab9d6358e39ac47631.parquet
2020-06-17 14:46:30 1.3 KiB table/col_two=b/834e872441224885a5089554524d3b12.parquet
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5fdd6f50ac5ac2de939ef5b849e4c41e27e623a3
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.UTF-8
pandas : 1.1.0.dev0+1887.g5fdd6f50a.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.16.1
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDM1NjU1MDU1
| 34,842
|
Infer filesystem from path when writing a partitioned DataFrame to remote file systems using pyarrow
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NONE
| null |
Infer the file system to be passed to pyarrow based on the path provided.
- [x] closes #34841
- [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: Series(..) coerces datetime64[non-ns] array differently depending on writable flag
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MEMBER
| null |
Normally, if we create a Series or DataFrame from a numpy array of non-ns datetime64, and the values are within the ns-range, we convert to datetime64[ns] (in `sanitize_array`).
However, this does not seem to happen if the array is not writeable:
```
In [9]: arr = np.array(['2020-01-01T00:00:00.000000'], dtype="datetime64[us]")
In [10]: pd.Series(arr)
Out[10]:
0 2020-01-01
dtype: datetime64[ns]
In [11]: arr.flags.writeable = False
In [12]: pd.Series(arr)
Out[12]:
0 2020-01-01 00:00:00
dtype: object
```
I *suppose* this is a bug? (@jbrockmendel ?)
It seems this is due to `tslibs.conversion.ensure_datetime64ns` failing on a read-only buffer, and it started "failing" since pands 0.25
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BUG: fix construction from read-only non-ns datetime64 numpy array
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MEMBER
| null |
Closes #34843
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DOC: 1.0.5 release date
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Backport PR #34845 on branch 1.0.x (DOC: 1.0.5 release date)
|
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| 0
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NONE
| null |
Backport PR #34845: DOC: 1.0.5 release date
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Remove redundant lists in array.py
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CONTRIBUTOR
| null |
As far as I know these lists do nothing but taking up memory.
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|
CI: Failing np.bool on numpy master
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| 1
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| 2020-06-17T17:26:57Z
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CONTRIBUTOR
| null |
```
=================================== FAILURES ===================================
_________________________ test_dataframe_div_silenced __________________________
[gw0] linux -- Python 3.9.0 /home/travis/virtualenv/python3.9-dev/bin/python
def test_dataframe_div_silenced():
# GH#26793
pdf1 = pd.DataFrame(
{
"A": np.arange(10),
"B": [np.nan, 1, 2, 3, 4] * 2,
"C": [np.nan] * 10,
"D": np.arange(10),
},
index=list("abcdefghij"),
columns=list("ABCD"),
)
pdf2 = pd.DataFrame(
np.random.randn(10, 4), index=list("abcdefghjk"), columns=list("ABCX")
)
with tm.assert_produces_warning(None):
> pdf1.div(pdf2, fill_value=0)
pandas/tests/arithmetic/test_numeric.py:1311:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f00d965da90>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning('`np.bool` is a deprecated alias for the builtin `bool`. Use `bool` by itself, which is identical in behavior, to silence this warning. If you specifically wanted the numpy scalar type, use `np.bool_` here.'), '/home/travis/build/pandas-dev/pandas/pandas/core/indexes/base.py', 377), ('DeprecationWarning', DeprecationWarning('`np.bool` is a deprecated alias for the builtin `bool`. Use `bool` by itself, which is identical in behavior, to silence this warning. If you specifically wanted the numpy scalar type, use `np.bool_` here.'), '/home/travis/build/pandas-dev/pandas/pandas/core/indexes/base.py', 377), ('DeprecationWarning', DeprecationWarning('`np.bool` is a deprecated alias for the builtin `bool`. Use `bool` by itself, which is identical in behavior, to silence this warning. If you specifically wanted the numpy scalar type, use `np.bool_` here.'), '/home/travis/build/pandas-dev/pandas/pandas/core/indexes/base.py', 377), ('DeprecationWarning', DeprecationWarning('`np.bool` is a deprecated alias for the builtin `bool`. Use `bool` by itself, which is identical in behavior, to silence this warning. If you specifically wanted the numpy scalar type, use `np.bool_` here.'), '/home/travis/build/pandas-dev/pandas/pandas/core/indexes/base.py', 377)]
```
https://travis-ci.org/github/pandas-dev/pandas/jobs/699262221
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WIP: TST: split up tests/plotting/test_frame.py into subdir & modules #34769
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| 10
| 2020-06-17T12:33:32Z
| 2020-10-31T19:17:09Z
| 2020-10-31T19:17:08Z
|
NONE
| null |
Moved `tests/plotting/test_frame.py` to `tests/plotting/frame/test_frame.py`.
Worked towards #34769 but not closing it as this has not split the test suite.
- [ ] xref #34769
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2NDA0MTUxNzg=
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BUG: epoch2num matplotlib deprecation warning
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| 14
| 2020-06-17T12:47:58Z
| 2020-07-23T22:06:03Z
| 2020-07-23T22:06:03Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
Using the current matplotlib master branch, pandas raises the following deprecation warning:
```
/Users/dstansby/miniconda3/envs/dev/lib/python3.8/site-packages/pandas/plotting/_matplotlib/converter.py:256: MatplotlibDeprecationWarning:
The epoch2num function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.
```
It looks like this line is present on pandas master, so should probably be changed (I'm not sure how) to avoid raising this deprecation warning.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1.post2858+gc3bfeb9c3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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base/test_unique.py: regression test for bad unicode string
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| 3
| 2020-06-17T13:27:02Z
| 2020-06-18T23:08:46Z
| 2020-06-18T23:08:41Z
|
CONTRIBUTOR
| null |
- [x] closes #34550
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I ran the tests with pandas 1.0.4, where it segfaults as expected (shown below), and `master` passes the test.
tests/test_mytest.py::test_unique2[idx_or_series_w_bad_unicode0] Fatal Python error: Segmentation fault
Current thread 0x00007f09d2dc4740 (most recent call first):
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pandas/core/algorithms.py", line 382 in unique
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pandas/core/base.py", line 1246 in unique
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 1974 in unique
File "/home/suvali/tmp/pandas-tests/tests/test_mytest.py", line 16 in test_unique2
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/python.py", line 182 in pytest_pyfunc_call
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File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/manager.py", line 93 in _hookexec
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/hooks.py", line 286 in __call__
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/python.py", line 1477 in runtest
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 135 in pytest_runtest_call
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File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 217 in <lambda>
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 244 in from_call
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 216 in call_runtest_hook
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 186 in call_and_report
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 100 in runtestprotocol
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/runner.py", line 85 in pytest_runtest_protocol
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/callers.py", line 187 in _multicall
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/manager.py", line 84 in <lambda>
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File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/manager.py", line 93 in _hookexec
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/hooks.py", line 286 in __call__
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/main.py", line 247 in _main
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/main.py", line 191 in wrap_session
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/main.py", line 240 in pytest_cmdline_main
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/callers.py", line 187 in _multicall
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/manager.py", line 84 in <lambda>
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/manager.py", line 93 in _hookexec
File "/opt/conda/envs/py38/lib/python3.8/site-packages/pluggy/hooks.py", line 286 in __call__
File "/opt/conda/envs/py38/lib/python3.8/site-packages/_pytest/config/__init__.py", line 124 in main
File "/opt/conda/envs/py38/bin/pytest", line 11 in <module>
Segmentation fault (core dumped)
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| 3
| 2020-06-17T14:17:14Z
| 2020-07-06T15:32:05Z
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CONTRIBUTOR
| null |
This should fail till https://github.com/pandas-dev/pandas/pull/34835 is in.
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DEPR: to_perioddelta
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| 2
| 2020-06-17T15:19:24Z
| 2020-06-18T16:47:15Z
| 2020-06-18T16:33:53Z
|
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
The only usage was in liboffsets and that has now been removed.
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|
QST: Events generated when dataframes are changed
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| 2020-06-17T21:42:36Z
| 2020-06-18T14:39:33Z
| 2020-06-18T14:39:33Z
|
NONE
| null |
- [x ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
here is a link to this question (similar)
https://stackoverflow.com/questions/48209811/how-to-notify-pandas-dataframe-modification
---
#### Question about pandas
What events are generated when a dataframe is updated in pandas? for example if a column is dropped or a value is changed in a cell? I am not interested in accesses, but I'd like to know if a dataframe in my environment goes away or if it is altered.
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DOC: Page Invalid 404 for Pandas 1.0.5 whatsnew
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| 6
| 2020-06-18T04:18:17Z
| 2020-06-18T10:40:15Z
| 2020-06-18T07:18:48Z
|
NONE
| null |
#### Location of the documentation
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
Here is the page location https://github.com/pandas-dev/pandas/releases/tag/v1.0.5
```
404 Not Found
nginx/1.10.3 (Ubuntu)
```
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
[this should provide a description of what documentation you believe needs to be fixed/improved]
#### Suggested fix for documentation
[this should explain the suggested fix and **why** it's better than the existing documentation]
|
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DOC: Fix syntax in 1.1.0 whatsnew
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| 0
| 2020-06-18T05:37:34Z
| 2020-06-20T11:08:40Z
| 2020-06-18T07:22:44Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: read-only buffer failures in datetime parsing
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| 1
| 2020-06-18T06:49:43Z
| 2020-11-04T13:43:46Z
| 2020-11-04T13:43:46Z
|
MEMBER
| null |
PR https://github.com/pandas-dev/pandas/pull/22147 caused several "read-only buffer" errors (latest: https://github.com/pandas-dev/pandas/issues/34843). I quickly skimmed that PR, and most changes there have been fixed in the mean-time (either converted back to use ndarray or added `const`), a few are remaining, like:
`array_strptime` (and also `array_to_timedelta64` -> `to_timedelta`)
```
In [4]: arr = np.array(['15/10/2020'], dtype=object)
In [5]: arr.flags.writeable = False
In [6]: pd.to_datetime(arr, format="%d/%m/%Y")
...
ValueError: buffer source array is read-only
```
and then a few in parsing.pyx, but I suppose those are generally not a problem, if the arrays are coming from out parsing code, they will never be read-only.
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/34843#issuecomment-645259205_
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1.0 as boolean dtype cause ValueError in pd.read_csv()
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| 2020-06-18T07:43:32Z
| 2020-06-18T08:35:24Z
| 2020-06-18T08:35:24Z
|
NONE
| null |
When I read the following "example_csv" using pd.read_csv() without assigning dtypes, no error is raised:
```python
import io
import pandas as pd
example_csv = """
boolean_column, string_column
1.0, a
0.0, b
,c"""
csv = io.StringIO(example_csv)
df = pd.read_csv(csv)
df.info()
>>> <class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 boolean_column 2 non-null float64
1 string_column 3 non-null object
dtypes: float64(1), object(1)
memory usage: 176.0+ bytes
```
However, I want "boolean_column" to have a nullable boolean dtype instead of float64. Therefore, I assign a dtype when reading the data which casues an error.
```python
csv = io.StringIO(example_csv)
df = pd.read_csv(csv, dtype={"boolean_column": "boolean"})
>>> ValueError: 1.0 cannot be cast to bool
```
Is this intended?
I am using Windows 10, Python 3.7.6 and pandas 1.0.4
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BUG: ValueError: 1.0 and 0.0 cannot be cast to bool when using pd.read_csv()
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| 5
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| 2020-07-02T15:01:40Z
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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.
---
#### Code Sample
```python
import io
import pandas as pd
example_csv = """
boolean_column, string_column
1.0, a
0.0, b
,c"""
csv = io.StringIO(example_csv)
df = pd.read_csv(csv, dtype={"boolean_column": "boolean"})
```
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-c698a4bcbc2d> in <module>
1 csv = io.StringIO(example_csv)
----> 2 df = pd.read_csv(csv, dtype={"boolean_column": "boolean"})
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\core\arrays\boolean.py in _from_sequence_of_strings(cls, strings, dtype, copy)
302 raise ValueError(f"{s} cannot be cast to bool")
303
--> 304 scalars = [map_string(x) for x in strings]
305 return cls._from_sequence(scalars, dtype, copy)
306
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\core\arrays\boolean.py in <listcomp>(.0)
302 raise ValueError(f"{s} cannot be cast to bool")
303
--> 304 scalars = [map_string(x) for x in strings]
305 return cls._from_sequence(scalars, dtype, copy)
306
c:\users\abbooda\appdata\local\continuum\miniconda3\envs\divi\lib\site-packages\pandas\core\arrays\boolean.py in map_string(s)
300 return False
301 else:
--> 302 raise ValueError(f"{s} cannot be cast to bool")
303
304 scalars = [map_string(x) for x in strings]
ValueError: 1.0 cannot be cast to bool
```
#### Problem description
I would expect that 1.0, 0.0 and None can be cast to a nullable boolean. Using ``pd.read_csv()`` without dtype assignment assigns float64 dtype. Why does "boolean" not work?
#### Expected Output
```python
print(df)
>>> boolean_column string_column
0 True a
1 False b
2 <NA> c
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.4
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.0.post20200616
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 : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 4
| 2020-06-18T10:50:14Z
| 2020-06-24T16:00:50Z
| 2020-06-24T16:00:50Z
|
CONTRIBUTOR
| null |
Sample code:
```python
import pandas as pd
idx = pd.date_range('2008', '2009')
idx.to_series()['2008']
```
in 0.25.3 we get
````
2008-01-01 2008-01-01
2008-01-02 2008-01-02
2008-01-03 2008-01-03
2008-01-04 2008-01-04
2008-01-05 2008-01-05
...
2008-12-27 2008-12-27
2008-12-28 2008-12-28
2008-12-29 2008-12-29
2008-12-30 2008-12-30
2008-12-31 2008-12-31
Freq: D, Length: 366, dtype: datetime64[ns]
````
in >= 1.0.0 (up to 1.0.4 at least) we get
```
2008 2008-01-01
2008 2008-01-02
2008 2008-01-03
2008 2008-01-04
2008 2008-01-05
...
2008 2008-12-27
2008 2008-12-28
2008 2008-12-29
2008 2008-12-30
2008 2008-12-31
Length: 366, dtype: datetime64[ns]
```
The index has dtype obj
Variantations like
```
idx.to_series().astype(str)['2008']
idx.to_series().loc['2008']
```
look unaffected
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BUG: read_parquet doesn't accept anymore a file route starting with 'file://'
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| 2020-08-21T17:05:18Z
| 2020-06-18T11:35:40Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
# Your code here
import pandas as pd
pd.read_parquet('file://<route_to_parquet_file>')
```
#### Problem description
When passed a path with the file protocol (e.g., `file:///home/Desktop/my_parquet.pq`) it throws the following error:
OSError: Passed non-file path: file:///home/Desktop/my_parquet.pq
#### Expected Output
Should read things fine.
With Pandas 1.0.3 it does work perfectly, so there's something wrong when moving from 1.0.3 to 1.0.4.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-101-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.0.post20200616
Cython : 0.29.20
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : 1.7.0
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 : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.49.1
</details>
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DEPR: Merging on different number of levels
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| 7
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| 2021-04-20T22:47:54Z
| 2021-04-20T22:47:54Z
|
NONE
| null |
Two dataframes with MultiIndex in columns (shapes are different).
```python
df1_columns = pd.MultiIndex.from_tuples([("A0", "B0", "C0"), ("A1", "B1", "C1")])
df1 = pd.DataFrame([[1, 2], [10, 20]], columns=df1_columns)
df2_columns = pd.MultiIndex.from_tuples([("X0", "Y0"), ("X1", "Y1")])
df2 = pd.DataFrame([[1, 200], [10, 200]], columns=df2_columns)
```
**Case 1**
```python
pd.merge(df1, df2, left_on=[("A0", "B0","C0")], right_on=[("X0", "Y0")])
```
returns
```
(A0, B0, C0) (A1, B1, C1) (X0, Y0) (X1, Y1)
0 1 2 1 200
1 10 20 10 200
```
It's ok. Columns saved its structure from the both dataframes. They are tuples, but it can be fixed (for example with something like [this](https://gist.github.com/dinya/06e374721993fd387db19ee7f83065a5)).
**Case 2**
```python
pd.merge(df2, df1, left_on=[("X0", "Y0")], right_on=[("A0", "B0","C0")])
```
returns
```
X0 X1 A0 A1
Y0 Y1 B0 B1
0 1 200 1 2
1 10 200 10 20
```
When the left DataFrame is a DataFrame with fewer column levels the merge result is questionable: lower levels are dropped.
Is this a bug or a feature (result levels shape must be the same as for the left DataFrame)?
Notes:
1. In both cases the warning is the same ``UserWarning: merging between different levels can give an unintended result ...``.
2. Checked with pandas 0.25.1 and 1.0.4.
3. See [notebook](https://gist.github.com/dinya/7e97a66ba00694fa752e7074baa39861).
---
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com) (see [62490244](https://stackoverflow.com/questions/62490244/merge-drops-lower-levels-in-result-columns-when-the-left-df-has-fewer-multiindex)).
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PERF: to speed up rendering of styler
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| 29
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| 2020-07-09T23:36:05Z
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|
CONTRIBUTOR
| null |
see https://github.com/pandas-dev/pandas/issues/19917#issuecomment-646209895
- [x] closes #19917
- [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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Series and Index have inconsistent repr
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|
MEMBER
| null |
Series and Index format differently when printed to the console (e.g., `dtype: object` vs. `dtype='object'`), but likely they should look the same (Index seems better IMO since it matches the constructor and agrees with numpy).
```python
In [11]: pd.Series([], dtype=object, name="abc")
Out[11]: Series([], Name: abc, dtype: object)
In [12]: pd.Index([], dtype=object, name="abc")
Out[12]: Index([], dtype='object', name='abc')
```
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DOC: Move API breaking to appropriate sections
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| 3
| 2020-06-18T20:52:33Z
| 2020-07-07T11:50:51Z
| 2020-07-07T11:50:51Z
|
CONTRIBUTOR
| null |
As discussed in https://github.com/pandas-dev/pandas/issues/34801, this moves some items from our "API breaking changes" section to either bug fixes or enhancements. I've only moved the ones we can hopefully get consensus on as bug fix / enhancement. But let me know if any of these need further discussion on whether they're an API breaking change.
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|
[WIP]REGR: Fixed reading from public S3 buckets with credentials
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| 2020-06-23T14:01:32Z
| 2020-06-23T14:01:26Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34626
This works in 1.0.4 I think, so no whatsnew.
I'd like to wait for https://github.com/pandas-dev/pandas/pull/34266 to get in first. I'll need to update things.
And in the future, I'd like to deprecate this behavior in favor of something like
```python
pd.read_csv("s3://path/to/key", storage_options={"anon": True})
```
It should be easy to detect the cases when we need to do that. We just don't have the `storage_options` kwarg yet.
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DOC: pandas/pandas/core/window/ewm.py
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| 5
| 2020-06-18T21:46:47Z
| 2020-06-20T22:20:59Z
| 2020-06-20T22:20:59Z
|
NONE
| null |
#### Location of the documentation
Docstring for `class EWM(_Rolling)` in ewm.py.
#### Documentation problem
The purposes of the `EWM` class is unclear because there's no documentation on what 'ewm' is abbreviating. One reason this is confusing is that `ewm` can easily be interpreted as either "exponentially weighted mean" or "exponentially weighted moving". The `EWM` class docstring actually muddies the water further by stating that the class provides "exponential weighted **(EW)** functions".
#### Suggested fix for documentation
If it's absolutely necessary to use an abbreviation like `ewm` then the docstring needs to clearly state what it's abbreviating.
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PERF: Timestamp dont create ndarray
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| 2020-06-19T00:59:41Z
| 2021-11-20T23:23:09Z
| 2020-06-19T18:12:51Z
|
MEMBER
| null |
```
In [1]: import pandas as pd
In [2]: ts = pd.Timestamp.now()
In [3]: %timeit ts.day_name()
6.46 µs ± 158 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
8.39 µs ± 107 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
```
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ENH: Vendor typing_extensions
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| 8
| 2020-06-19T01:48:57Z
| 2020-09-01T23:37:09Z
| 2020-09-01T23:37:09Z
|
MEMBER
| null |
I’ve discussed this with @simonjayhawkins here and there but never had a formal issue opened, hence this :-)
I think we need to vendor typing_extensions to realistically be able to unpin mypy from where it is now. The main thing I think we need is to unlock Protocols; these aren’t added until Python 3.8 but as of mypy 0.75 seem to be the documented solution to a lot of the Mixin problems that we face:
https://mypy-lang.blogspot.com/2019/11/mypy-0.html
There are some other useful things like Literal, Final and TypedDict that this would unlock as well, to at least hold us over until 3.8 is the minimum supported version
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BUG: AssertionError when slicing MultiIndex and setting value of pandas.Series
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| 1
| 2020-06-19T10:12:56Z
| 2020-10-31T17:19:06Z
| 2020-10-31T17:19:06Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
>>> import numpy as np
... import pandas as pd
...
... arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
... ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
... tuples = list(zip(*arrays))
... index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
...
... s = pd.Series(np.random.randn(8), index=index)
>>>s
first second
bar one -0.592576
two -0.054856
baz one -0.169856
two -0.712601
foo one 2.180688
two -0.032646
qux one -0.729950
two 0.883029
dtype: float64
>>> s[('baz', 'one'):('foo', 'two')] = 4 # This works!
>>> s
first second
bar one -0.592576
two -0.054856
baz one 4.000000
two 4.000000
foo one 4.000000
two 4.000000
qux one -0.729950
two 0.883029
dtype: float64
>>> s.loc[('baz', 'one'):('foo', 'two')] = 4 # This gives the error!
Traceback (most recent call last):
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2646, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: ('baz', 'one')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 2890, in _get_level_indexer
start = level_index.get_loc(key.start)
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2648, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: ('baz', 'one')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexing.py", line 670, in __setitem__
indexer = self._get_setitem_indexer(key)
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexing.py", line 641, in _get_setitem_indexer
return ax.get_loc(key)
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 2662, in get_loc
loc = self._get_level_indexer(key, level=0)
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 2903, in _get_level_indexer
key.start, key.stop, key.step, kind="loc"
File "/Users/edoardo/miniconda3/envs/my37/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 4717, in slice_indexer
raise AssertionError("Start slice bound is non-scalar")
AssertionError: Start slice bound is non-scalar
```
#### Problem description
I'm trying to slice and set values of a pandas Series but using the loc function does not work. I can do it by simply using [] and using loc if the Series is first converted into a DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.0.post20200616
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
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 : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: Inconsistent behavior for df.replace over pd.Period columns
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| 6
| 2020-06-19T13:33:41Z
| 2020-11-17T01:17:29Z
| 2020-11-17T01:17:29Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({
'Int': [1, 2, 3],
'Flo': [1.0, 6.0, 1.0],
'Per': [pd.Period('2020-01')]*3,
'Tim': [pd.Timestamp('2020-01')]*3
})
# TypeError: 'value' should be a 'Period', 'NaT', or array of those. Got 'float' instead.
df.replace(1.0, 0.0)
# Works
df[['Int', 'Flo', 'Tim']].replace(1.0, 0.0)
# Works
df['Per'].replace(1.0, pd.Period('2020-02'))
```
#### Problem description
`df.replace(1.0, 0.0)` fails if any columns of `df` are Period types. The error is saying that 0.0 is an invalid value to put in a Pandas Period column. This should never be an issue though, 1.0 will never exist in a Pandas Period column, so nothing will ever be replaced for 0.0.
#### Expected Output
The expected behavior would be consistent with the `df[['Int', 'Flo', 'Tim']].replace(1.0, 0.0)` in the example above. This does work, the 'Flo' column has all of it's 1.0s replaced with 0.0s and the 'Int' and 'Tim' columns (which are not Float types) remain unaffected. I would expect that a Pandas Period column would also be skipped in the same way.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.0
pip : 20.1.1
setuptools : 47.3.0.post20200616
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.9
numba : None
</details>
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API: Honor copy for dict-input in DataFrame
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| 2021-01-03T22:36:54Z
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|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/32960.
~Still need a whatsnew. The tl/dr is~
1. We can't honor no-copy for a dict with multiple values of the same dtype: `DataFrame({"A": np.array([1, 2]), "B": np.array([1, 2])})` as long as we have consolidation.
~2. Currently, this is an API breaking change for something like `pd.DataFrame({"A": np.array([1, 2])})`. To resolve that, I think we'll need something like a default of `copy=None`, and then require `copy=False` to enable no-copy for dict inputs (but keep no copy by default for 2d ndarrays and data frames).~
Unsure how we're going to reconcile that with the behavior of EAs.
EDIT: I've changed the default `copy` to `None`, which means
1. no copy for ndarray / dataframe input (`copy=True` to copy)
2. copy for dict input (`copy=False` to not copy).
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Should IntegerArray provide data / mask through an API?
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CONTRIBUTOR
| null |
I'm currently implementing some algorithms on top of the `IntegerArray` class with `numba`. Therefore I would need to pass in the two separate backing NumPy arrays instead of the `pandas` class. Using `series.to_numpy()` isn't helpful in this case as this returns a `object`-typed array. For now, I'll keep using `series.values._data` and `series.values._mask` but I'm aware that using private variables is not a long-term solution.
Given that `series.values._data` may be undefined where the mask is `True`, I know that returning the raw `data` may be a bit controversial but still need. Thus naming the accessor for it should be done carefully.
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Doc: Dates being plotted wrong_x-compact parameter #20688
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NONE
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- [x] closes #xxxx
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DF.__setitem__ creates extension column when given extension scalar
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| 2020-07-13T13:01:58Z
| 2020-07-11T02:02:55Z
|
CONTRIBUTOR
| null |
This PR is in response to [Issue 34832]( https://github.com/pandas-dev/pandas/issues/34832).
These changes follow the suggestions from reviewers on the PR to fix an issue where `df['a']=pd.Period('2020-01')` would create an object column instead of a `period[M]` column.
One potential issue that I see with these changes is that this code requires the `shape` parameter passed into `cast_scalar_to_array` to be an int, even though the functions docstring claims that it accepts a tuple.
I'm happy to work on resolving that issue, but first wanted to clarify that it is necessary. Based on my perspective, there would be no reason to call this function to create a multi-dimensional array, but I surely don't understand the full interconnectivity of this package.
- [x] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0NDM3MzA2Njc1
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Cln 31942/replace appender with doc 6
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CONTRIBUTOR
| null |
- [x] ref #31942
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- fixes issues with the introduction of the doc decorator
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BUG/TST: Read from Public s3 Bucket Without Creds
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| 2020-07-15T19:07:56Z
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|
CONTRIBUTOR
| null |
- [x] Closes #34626
We should potentially merge this before https://github.com/pandas-dev/pandas/pull/34266 to confirm reading from s3 without credentials works.
Ref discussion here: https://github.com/pandas-dev/pandas/pull/34793#issuecomment-645023794
cc. @jorisvandenbossche
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BUG: Fix replace for different dtype equal value
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MEMBER
| null |
Bug noticed when looking at a different issue https://github.com/pandas-dev/pandas/issues/34871#issuecomment-646664842
- [x] closes https://github.com/pandas-dev/pandas/issues/35376
- [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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TST: Add test to verify 'dropna' behaviour on SparseArray
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CONTRIBUTOR
| null |
verify that calling `dropna` on a pd.SparseArray does not inadvertently drop non-na records on both DataFrames and the SparseArray itself.
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (not applicable?)
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| 2020-07-06T23:27:32Z
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|
CONTRIBUTOR
| null |
verify that aligning two dataframes with a `CategoricalIndex` does not change the type of the index.
- [x] closes #28397
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (not applicable?)
|
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CI/COMPAT: PyUnicode_FromUnicode is deprecated failure on 3.9 CI
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[] | null | 7
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| 2021-10-30T04:44:55Z
| null |
MEMBER
| null |
The 3.9-dev build started failing (eg https://travis-ci.org/github/pandas-dev/pandas/jobs/700233890):
```
pandas/_libs/parsers.c: In function ‘__Pyx_decode_c_string’:
pandas/_libs/parsers.c:56645:9: error: ‘PyUnicode_FromUnicode’ is deprecated [-Werror=deprecated-declarations]
return PyUnicode_FromUnicode(NULL, 0);
^~~~~~
In file included from /opt/python/3.9-dev/include/python3.9/unicodeobject.h:1026:0,
from /opt/python/3.9-dev/include/python3.9/Python.h:97,
from pandas/_libs/parsers.c:57:
/opt/python/3.9-dev/include/python3.9/cpython/unicodeobject.h:551:42: note: declared here
Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(
^~~~~~~~~~~~~~~~~~~~~
cc1: all warnings being treated as errors
```
See https://docs.python.org/3.9/c-api/unicode.html#c.PyUnicode_FromUnicode
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DOC: Fix validation issues with Index.is_ docstring
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|
CONTRIBUTOR
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Fix validation issues with the `Index.is_ docstring` and add `Index.identical` to see also section. Trying to finish what was started in #32357
|
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MDExOlB1bGxSZXF1ZXN0NDM3MzkxNDYy
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TST: Feather RoundTrip Column Ordering
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| 2020-06-20T09:44:58Z
| 2020-06-20T12:30:47Z
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|
CONTRIBUTOR
| null |
Looks like this in fixed from pyarrow 0.17.1 -> Added a Test.
- [x] closes #https://github.com/pandas-dev/pandas/issues/33878
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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DOC: fixed labels in "Plotting with error bars"
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| 1
| 2020-06-20T10:35:51Z
| 2020-06-20T18:26:53Z
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
x-labels should now be visible in the plot.
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MDExOlB1bGxSZXF1ZXN0NDM3Mzk2NTkx
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DOC: Fixed table formatting in box plot section
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CONTRIBUTOR
| null |
[DOC]fixed table formatting in the box plot section keeing simple rst table format
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDM3Mzk3NjA1
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TST: Ensure dtypes are set correctly for empty integer columns #24386
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CONTRIBUTOR
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- [x ] closes #24386
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- [x ] added tests to verify whether empty integer columns are loaded in as integer columns
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MDExOlB1bGxSZXF1ZXN0NDM3Mzk3NjYx
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Deprecate `center` on `df.expanding`
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| 2020-07-14T17:08:50Z
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CONTRIBUTOR
| null |
`df.expanding(center=True)` currently returns non-sensical results and it is unclear what results would be expected. It was previously removed in #7934 for that same reason,but was reintroduced for unclear reasons in a later refactoring
- [x] closes #20647
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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DOC: document support for in-memory HDFStore GH33166
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| 3
| 2020-06-20T10:52:30Z
| 2020-06-20T20:08:44Z
| 2020-06-20T20:08:40Z
|
CONTRIBUTOR
| null |
I currently have added to the docstring that **kwargs passes its parameters to
PyTables. Maybe this is too much but I also added an example using **kwargs,
passing the driver paramter to create an in-memory HDFStore.
Furthermore, I have added HDFStore class to the reference api, as it was not
autogenerated and also made **kwargs more clear that it passes its parameters
to PyTables.
Added in the cookbook the method of creating in-memory HDFStores, including
an example.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
closes #33166
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TST: IntegerNA Support for DataFrame.diff()
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|
CONTRIBUTOR
| null |
- [x] closes #24171
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Test for IntegerNA support DataFrame.diff().
``` bash
pytest tests/frame/methods/test_diff.py::TestDataFrameDiff::test_diff_integer_na
```
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DOC: add mention of recommended dependencies in users guide
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| 4
| 2020-06-20T11:59:46Z
| 2020-06-20T17:14:05Z
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CONTRIBUTOR
| null |
First-time contributor here!
Ian Ozsvald mentioned installing the optional dependencies for pandas in his talk at PyData Amsterdam 2020. Although these are mentioned in the Getting Started section, I though they should be mentioned in the Users Guide, which is where I typically go for pandas information. It seems like many users aren't aware that these dependencies are available to increase performance.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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|
Plotting Tables x-axis labels display
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CONTRIBUTOR
| null |
Added x-axis labels on top. Adjusted figure sizes in order to have the complete table visible in the user guide.
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DOC: Contributing guide docker expansion
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NONE
| null |
Added instructions for creating development environment with docker on windows, mac os and unix.
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| 2020-06-20T13:49:37Z
| 2020-06-20T13:49:37Z
|
MEMBER
| null |
See eg https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=37674&view=logs&jobId=a67b4c4c-cd2e-5e3c-a361-de73ac9c05f9&j=a67b4c4c-cd2e-5e3c-a361-de73ac9c05f9&t=33d2fdd0-c376-5f94-e6d3-957bdd23a3b8
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| 642,369,977
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MDExOlB1bGxSZXF1ZXN0NDM3NDA4MzQz
| 34,894
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move 3.9 travis build to allowed failures
|
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| 2020-06-20T12:47:03Z
| 2020-06-20T13:07:53Z
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CONTRIBUTOR
| null |
xref #34881
|
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MDExOlB1bGxSZXF1ZXN0NDM3NDA5MjU1
| 34,895
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TST: skip gbq integration tests, xref #34779
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| 1
| 2020-06-20T12:56:48Z
| 2020-06-20T14:43:35Z
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CONTRIBUTOR
| null |
xref #34779
|
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MDExOlB1bGxSZXF1ZXN0NDM3NDEwMjI4
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BUG: Fixes plotting with nullable integers (#32073)
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| 2020-07-07T09:32:47Z
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|
CONTRIBUTOR
| null |
- [x] closes #32073
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Similar to #32410 this tries to fix plotting with nullable integer extension data types by casting them to float before passing on to matplotlib.
|
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MDExOlB1bGxSZXF1ZXN0NDM3NDEwOTU0
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TST: groupby apply called multiple times
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| 2020-06-21T06:06:14Z
| 2020-06-20T22:24:25Z
|
CONTRIBUTOR
| null |
closes #31111
xref https://github.com/pandas-dev/pandas/pull/24748
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ Added test for testing func should be called len(set(groupby_column)) times when passed to groupby.apply(func) ] whatsnew entry
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MDU6SXNzdWU2NDIzNzM5MTY=
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Refactor pandas.core.generic.NDFrame.to_replace() to appease static type analysis
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| null |
CONTRIBUTOR
| null |
Current implementation of `pandas.core.to_replace()` method won't allow type hints, as noted in #32542 . Contributor guidelines [suggest](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#style-guidelines) a refactor in this case.
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DOC: add note about the values of unit for pd.to_datetime
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| 2020-06-20T13:21:13Z
| 2020-06-20T23:53:59Z
| 2020-06-20T20:11:53Z
|
CONTRIBUTOR
| null |
The `unit` option for `pandas.to_datetime` takes different format strings than the `format` option,
which makes sense, but caught me off-guard as a new user. It would be helpful if the documentation
mentioned this.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST: xref 34893, html bank url tests
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| 2020-06-20T13:49:28Z
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CONTRIBUTOR
| null |
xref #34893
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MDExOlB1bGxSZXF1ZXN0NDM3NDExODkw
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|
DOC: explain EWM
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|
CONTRIBUTOR
| null |
- [ ] closes #34867
- [ ] tests passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew - Made docs more clear and renamed files
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MDU6SXNzdWU2NDIzNzcyMDA=
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DOC: Contributor guideline should guide contributors for expected local mypy failures
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| 2020-06-20T13:36:54Z
| 2020-09-01T11:42:04Z
| 2020-09-01T11:42:04Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[Location](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#style-guidelines).
#### Documentation problem
See conversation [here](https://gitter.im/pydata/pandas?at=5eee0e2ba813c72dcffccbb5).
#### Suggested fix for documentation
This 'failing' of mypy should be noted so that inexperienced contributors don't have to Google around and experienced ones don't have to explain repeatedly.
I came across this in [this context](https://github.com/pandas-dev/pandas/pull/32542#discussion_r442959117).
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Replace old string formatting syntax with f-strings #29547
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NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST:add test for df replace GH34871
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| 2020-06-20T14:53:44Z
| 2020-06-26T06:06:33Z
| 2020-06-24T15:59:24Z
|
CONTRIBUTOR
| null |
- [x] improves upon #34871
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry <---- is that helpful for this specific regression test? happy to write this if you think this helps! :)
add tiny regression test for not failing on calling df.replace() with Period column
working on this revealed other quirks of df.replace(): it now does not raise an exception anymore, and the dataframe returned by replace() has the right values. However, the dtype of a Period column changes to Object, which is surprising.
I've added the observations to GH34871
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BUG: plotting layout patch
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CONTRIBUTOR
| null |
This fixes a layout problem.
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| 6
| 2020-06-20T15:40:14Z
| 2020-07-16T18:02:54Z
| 2020-07-16T16:08:55Z
|
CONTRIBUTOR
| null |
- [x] closes #26321
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST: pandas/test/window/ changes for #30999
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| 2020-06-20T15:43:54Z
| 2020-06-23T19:29:03Z
| 2020-06-23T19:28:53Z
|
CONTRIBUTOR
| null |
- [ ] xref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I've made changes for:
- tests/window/moments/test_moments_rolling.py
- tests/window/test_dtypes.py
- tests/window/test_ewm.py
- tests/window/test_expanding.py
- tests/window/test_timeseries_windows.py
In a couple of places there were comments stating the raised error's output. The `msg` variable contains the same information. Should I remove these coments?
Belongs to issue: https://github.com/pandas-dev/pandas/issues/30999
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BUG: incorrect type when indexing sparse dataframe with iterable
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| 6
| 2020-06-20T16:04:08Z
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CONTRIBUTOR
| null |
closes #34526
closes #34540
- [x] 2 tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The problem arose when indexing with an iterable. If the result consisted of columns which originally only had sparse values, the `dtype` was solely inferred from the `fill_value`, which defaults to `0`. Hence, the resulting columns have the dtype ` Sparse[int64, 0]`. This commit changes the type inference logic to use the numpy type promotion rules between the underlying subtype of the `SparseArray.dtype` and the type of the fill value.
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PERF: avoid creating many Series in apply_standard
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| 2020-06-20T16:25:52Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This avoids going through perennial-problem-causing libreduction code (xref #34014, #34080) and instead does the same trick in python-space by re-assigning block.values instead of creating new Series objects.
If we avoid libreduction but _dont_ do this optimization, the most-affected asv is `time_apply_ref_by_name` that clocks in at 6.92x slower. This achieves parity on that asv.
<s>ATM I'm still getting 4 test failures locally, need to troubleshoot.</s> Update: passing
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DOC: explain EWM
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| 2020-06-21T08:14:13Z
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|
CONTRIBUTOR
| null |
- [ ] closes #34867
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDM3NDM4MTMx
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TST: Verify whether Datetime subclasses are also of dtype datetime
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- [x] closes #21142
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDM3NDQ3MTQy
| 34,912
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ENH: specificy missing labels in loc calls GH34272
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| 2020-06-20T18:48:43Z
| 2020-06-26T20:23:40Z
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CONTRIBUTOR
| null |
- [ ] closes #34272
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDM3NDUzNzYx
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|
REF: dont use compute_reduction
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| 2020-06-20T19:55:20Z
| 2020-06-22T16:37:30Z
| 2020-06-20T23:16:13Z
|
MEMBER
| null |
Full asv run shows no change.
This gets rid of one usage of libreduction.compute_reduction; #34909 gets rid of the other.
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MDExOlB1bGxSZXF1ZXN0NDM3NDU1OTE1
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CLN: GH29547 format with f-strings
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| 2020-06-24T17:28:36Z
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|
CONTRIBUTOR
| null |
xref #29547
replace .format() for f-strings in the following:
- pandas/tests/series/indexing/test_numeric.py
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MDU6SXNzdWU2NDI0NTU2MzE=
| 34,915
|
BUG: Cannot create empty dataframe having columns with dtype string
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closed
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| null |
[] | null | 2
| 2020-06-20T22:24:33Z
| 2020-06-23T17:45:33Z
| 2020-06-23T17:45:33Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
I have checked, but this requirement is grossly unreasonable since there are currently 3,426 other open issues. It's impossible for anyone to be sure. This requirement should be reworded.
- [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-traceback
>>> import pandas as pd
>>> pd.DataFrame(columns=['c1'], dtype='string')
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/home/homeuser/PycharmProjects/.venv/myenv/lib/python3.8/site-packages/pandas/core/frame.py", line 435, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File "/home/homeuser/PycharmProjects/.venv/myenv/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 234, in init_dict
if dtype is None or np.issubdtype(dtype, np.flexible):
File "/home/homeuser/PycharmProjects/.venv/myenv/lib/python3.8/site-packages/numpy/core/numerictypes.py", line 388, in issubdtype
arg1 = dtype(arg1).type
TypeError: Cannot interpret 'StringDtype' as a data type
```
#### Problem description
These three alternatives work, so there is no reason for this bug to continue to not be fixed:
```python-traceback
>>> pd.DataFrame(columns=['c1'], dtype='str')
Empty DataFrame
Columns: [c1]
Index: []
>>> pd.DataFrame([{}], columns=['c1'], dtype='string')
c1
0 <NA>
>>> pd.DataFrame([{}], columns=['c1'], dtype='string').drop(labels=0)
Empty DataFrame
Columns: [c1]
Index: []
```
#### Expected Output
```python-traceback
Empty DataFrame
Columns: [c1]
Index: []
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1087-oem
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 40.8.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
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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MDU6SXNzdWU2NDI0NzE5NTQ=
| 34,916
|
API: Default to stdlib timezone objects instead of pytz
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open
| false
| null |
[] | null | 4
| 2020-06-21T00:54:26Z
| 2020-06-22T16:31:43Z
| null |
MEMBER
| null |
i.e. when we get `pd.Timestamp.now("UTC")` we should return a Timestamp with `datetime.timezone.utc` rather than `pytz.UTC`. Similarly when we parse an ISO8601 datetime we should use tzinfo of `timezone(timedelta(seconds=val*60))` instead of `pytz.FixedOffset(val)`
This isn't _that_ hard to implement, but doing it breaks a couple dozen tests where we are currently checking for pytz objects. This would technically be an API change, so putting it up for discussion before implementing it.
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