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CLN: use standard method to set unhashable object
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MEMBER
| null |
#20589 mentioned lgtm. So I decided to test it. And it found this potential improvement. There is even a detailed reference with code examples. https://lgtm.com/rules/1780095/ Very interesting.
Just trying to see if their suggestion works.
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EA: Tighten signature on DatetimeArray._from_sequence
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| 1
| 2020-09-29T01:22:42Z
| 2020-10-03T00:26:03Z
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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
xref #33254
There are a few more steps to get DatetimeArray._from_sequence down to the ideal behavior, this is just trimming the signature down to what its supposed to be.
_from_sequence_not_strict could use a better name
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CI: arm Travis build timing out
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| 10
| 2020-09-29T03:11:30Z
| 2021-04-13T15:55:36Z
| 2021-04-13T15:55:36Z
|
MEMBER
| null |
I'm seeing this in most but not all builds ATM.
Even if we moved it to allowed-failures, it would still be slowing the Travis turnaround time down a lot.
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CLN: clean clipboard
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| 710,787,251
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MDExOlB1bGxSZXF1ZXN0NDk0NTc1MzQ1
| 36,721
|
CLN: Remove unnecessary rolling subclass
|
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MEMBER
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- [x] tests added / passed
- [x] passes `black pandas`
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MDExOlB1bGxSZXF1ZXN0NDk0NzYzMzY5
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|
CI unpin flake8, only run flake8-rst in pre-commit
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- [ ] closes #34150 (maybe?)
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDk0ODUwNTMw
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DOC: Fix typo in docstring
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| 1
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| 2020-09-29T16:39:28Z
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CONTRIBUTOR
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MDExOlB1bGxSZXF1ZXN0NDk0OTE4NDI3
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|
TYP: Ignore remaining mypy errors for pandas\tests\*
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| 1
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| 2020-09-30T11:06:32Z
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|
MEMBER
| null |
- [ ] closes #28926
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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These are the only 3 errors outstanding in pandas\tests\* that are stopping us closing #28926
```
pandas\conftest.py:301: error: List item 0 has incompatible type "Type[Index]"; expected "Type[PandasObject]" [list-item]
pandas\tests\window\conftest.py:79: error: List item 0 has incompatible type "ParameterSet"; expected "Sequence[Collection[object]]" [list-item]
pandas\tests\window\conftest.py:330: error: List item 15 has incompatible type "ParameterSet"; expected "Sequence[Collection[object]]" [list-item]
```
these appear to be from a mypy inference issue. It maybe best to `# type: ignore[list-item]` just these lines and remove the `ignore_errors=True` from setup.cfg that applies to the whole file for all error codes.
if the mypy error is resolved in a future release, the `warn_unused_ignores = True` in setup.cfg means that we will be alerted and the ignores can be removed.
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TYP: update setup.cfg
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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
Refactor/cleanup ``_get_empty_dtype_and_na`` in ``pandas/core/internals/concat.py``
Extract functions, add typing.
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| 2
| 2020-09-29T19:49:59Z
| 2020-10-06T18:04:00Z
| 2020-10-06T18:04:00Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
df = pd.DataFrame(
[
["A", "group_1", pd.Timestamp(2019, 1, 1, 9)],
["B", "group_1", pd.Timestamp(2019, 1, 2, 9)],
["C", "group_2", pd.Timestamp(2019, 1, 3, 9)],
["D", "group_1", pd.Timestamp(2019, 1, 6, 9)],
["E", "group_1", pd.Timestamp(2019, 1, 7, 9)],
["F", "group_1", pd.Timestamp(2019, 1, 10, 9)],
["G", "group_2", pd.Timestamp(2019, 1, 20, 9)],
["H", "group_1", pd.Timestamp(2019, 4, 8, 9)],
],
columns=["index", "group", "eventTime"],
).set_index("index")
groups = df.groupby("group")
df["count_to_date"] = groups.cumcount()
rolling_groups = groups.rolling("10d", on="eventTime")
group_size = rolling_groups.apply(lambda df: df.shape[0])
print(group_size)
```
#### Problem description
The above code causes a segmentation fault inside pandas for versions *after* 1.0.5. Since I need the above code for a project, I am restricted to using pandas 1.0.5 until this is resolved. I am not sure what is causing the segmentation fault, but all the above circumstances are necessary to reproducing the bug (ie `DataFrame` with special index, a column set in the `DataFrame` after grouping, a rolling window on a group, etc).
I have reproduced this bug on a variety of machines and operating systems.
#### Expected Output
```
eventTime count_to_date
group index
group_1 A 2019-01-01 09:00:00 1.0
B 2019-01-02 09:00:00 2.0
D 2019-01-06 09:00:00 3.0
E 2019-01-07 09:00:00 4.0
F 2019-01-10 09:00:00 5.0
H 2019-04-08 09:00:00 1.0
group_2 C 2019-01-03 09:00:00 1.0
G 2019-01-20 09:00:00 1.0
```
Note: This is indeed the output of versions 1.0.5 and prior.
#### Output of ``pd.show_versions()``
This is just one configuration but the bug has been reproduced on three different machines (both linux and mac), all exhibiting the same behavior.
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Thu Jun 18 21:21:34 PDT 2020; root:xnu-4570.71.82.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DOC: Update roadmap for completions
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| 4
| 2020-09-29T20:58:53Z
| 2020-09-30T12:37:44Z
| 2020-09-30T12:37:42Z
|
CONTRIBUTOR
| null |
Moves "Improved documentation" to completed section.
I don't think any others can be marked as done yet, though "Numba-accelerated operations" may be getting close (is that right @mroeschke?).
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| 711,461,744
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MDExOlB1bGxSZXF1ZXN0NDk1MTEyOTUw
| 36,729
|
PERF: using murmur hash for float64 khash-tables
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| 28
| 2020-09-29T21:24:54Z
| 2020-11-14T16:16:15Z
| 2020-11-14T16:15:26Z
|
CONTRIBUTOR
| null |
- [x] closes #28303
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The currently used hash-function can lead to many collisions (see #28303 or [this comment](https://github.com/pandas-dev/pandas/pull/36611#issuecomment-699551943)) for series like 0.0, 1.0, 2.0, ... n.
This PR uses a specialization (for a simple double-value) of [murmur2-hash](https://github.com/aappleby/smhasher/blob/61a0530f28277f2e850bfc39600ce61d02b518de/src/MurmurHash2.cpp#L37), which is used in [stdc++](https://github.com/gcc-mirror/gcc/blob/41d6b10e96a1de98e90a7c0378437c3255814b16/libstdc%2B%2B-v3/libsupc%2B%2B/hash_bytes.cc#L25) and [libc++](https://github.com/llvm/llvm-project/blob/1cfde143e82aeb47cffba436ba7b5302d8e14193/libcxx/include/utility#L977) and more or less state of the art.
An alternative would be to use Python's `_Py_HashDouble`, but because it has the property: `hash(1.0)=1`, `hash(2.0)=2` and so on: there is no desirable [avalanche effect ](https://en.wikipedia.org/wiki/Avalanche_effect), which is not an issue for Python's `dict` implementation, but problematic for khash as it uses a different strategy for collision handling. See #13436 as `_Py_HashDouble` was replaced through the simple hash-function used until now.
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MDExOlB1bGxSZXF1ZXN0NDk1MTQyNzc3
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[BUG]: Rolling selected too large windows with PeriodIndex
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| 2020-10-03T00:59:38Z
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|
MEMBER
| null |
- [x] closes #34225
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
``asi8`` of PeriodIndex is not in nanoseconds, so we can not set ``self.window=freq.nanos``
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EA: tighten TimedeltaArray._from_sequence signature
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| 3
| 2020-09-29T22:16:24Z
| 2020-10-12T08:50:21Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #36718
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MDU6SXNzdWU3MTE1NDU4Mjg=
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|
BUG: Dataframe filter no longer working in code
|
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| 2020-09-30T00:18:05Z
| 2020-09-30T11:11:51Z
| 2020-09-30T11:11:51Z
|
NONE
| null |
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
I was wondering if there were any syntax changes that is preventing the code below from running properly. I am new to Python and I am learning to understand how it works. This code was working 24 hours ago and today it is not. I am using pandas version 0.25.1 on Anaconda
```python
dfs = pd.read_html('https://finance.yahoo.com/trending-tickers')
for df in dfs:
pass
df["% Change"] = df["% Change"].str.replace('%',"")
filt_data = (df['Last Price'].astype(float) >= 10.00) & (df['Last Price'].astype(float) <= 100.00) & (df['% Change'].astype(float) > 1)
symbols_most_active = [i for i in df[filt_data].iloc[:,0]]
print(symbols_most_active)
```
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CI: disable ARM build
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| 2
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| 2020-09-30T20:59:15Z
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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
xref #36719
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DOC: Format more code blocks
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| 2
| 2020-09-30T01:01:17Z
| 2020-10-01T12:13:43Z
| 2020-10-01T01:14:22Z
|
MEMBER
| null |
Actually, contrary to https://github.com/pandas-dev/pandas/pull/36700#issuecomment-700957831 there is quite a lot more clean up to do (so maybe could make for some good first issues @simonjayhawkins), it's just that other docs contain blocks that blacken-docs can't parse (e.g., raw output or ipython code containing magic commands). A workaround is to comment them out, parse the doc, and then uncomment (the commented blocks would then have to be edited manually).
Is it possible to render blocks like this as pure code in rst which are allowed to raise?
```python
.. code-block:: python
>>> pd.DataFrame(np.random.randn(10, 2)).to_hdf('test_fixed.h5', 'df')
>>> pd.read_hdf('test_fixed.h5', 'df', where='index>5')
TypeError: cannot pass a where specification when reading a fixed format.
this store must be selected in its entirety
```
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TST: read binary file objects with read_fwf
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| 1
| 2020-09-30T05:50:52Z
| 2020-09-30T13:10:08Z
| 2020-09-30T13:10:05Z
|
CONTRIBUTOR
| null |
- [x] closes #18035
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry: not needed
`read_fwf` has already supported reading binary file objects. Added the test case from the issue.
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|
QST:How to make dataframe output HTML format by default in Cpython shell
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| 2020-09-30T06:11:33Z
| 2020-10-01T07:39:58Z
| 2020-10-01T07:39:58Z
|
NONE
| null |
---
#### Question about pandas
I wrote jupyter myself. The kernel of the program uses Cpython instead of IPython. The problem is that when I output dataframe data, pandas does not output HTML strings under IPython notebook. How can I set the behavior of pandas to output HTML strings
```python
# Your code here, if applicable
[11]import pandas as pd
[12]data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002, 2003],
'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
[13]df= pd.DataFrame(data)
[14]df
Out: state year pop
0 Ohio 2000 1.5
1 Ohio 2001 1.7
2 Ohio 2002 3.6
3 Nevada 2001 2.4
4 Nevada 2002 2.9
5 Nevada 2003 3.2
```
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TST: honor encoding in read_fwf for memory-mapped files
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| 3
| 2020-09-30T06:14:33Z
| 2020-09-30T17:25:34Z
| 2020-09-30T17:22:42Z
|
CONTRIBUTOR
| null |
- [x] closes #23254
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry: not needed
`encoding` was already working for memory-mapped files in `read_fwf`.
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MDU6SXNzdWU3MTE3MzY2NTQ=
| 36,738
|
BUG: to_timedelta drops decimals from input if precision is greater than nanoseconds
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| 9
| 2020-09-30T08:16:52Z
| 2020-11-15T17:31:47Z
| 2020-11-15T17:31:47Z
|
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.
---
I am trying to convert a time to timedelta. The input data is in string format. When number of decimals is too big, it resets all decimals to zero.
```python
import pandas as pd
d = {'Time':['8:53:08.26','8:53:08.71800000001', '8:53:09.729']}
df = pd.DataFrame(data=d)
pd.to_timedelta(df["Time"])
```
The result of the above pice of code gives:
```
0 0 days 08:53:08.260000
1 0 days 08:53:08
2 0 days 08:53:09.729000
Name: Time, dtype: timedelta64[ns]
```
As it can be seen, all the decimals from second data is lost.
#### Problem description
With the present behavior, a sorted array of data returns a wrong and unsorted array.
#### Expected Output
```
0 0 days 08:53:08.260000
1 0 days 08:53:08.718000
2 0 days 08:53:09.729000
Name: Time, dtype: timedelta64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 7
Version : 6.1.7601
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : es_ES.cp1252
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200925
</details>
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| 711,763,274
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MDU6SXNzdWU3MTE3NjMyNzQ=
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|
Is there any doucument about telling how to cross-compile pandas to arm ? so difficult
|
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| 2020-09-30T08:56:07Z
| 2020-10-29T16:07:19Z
| 2020-10-29T16:07:19Z
|
NONE
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
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DOC: Summing by level on a category index produce extra columns.
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| 2020-10-01T01:18:41Z
|
CONTRIBUTOR
| null |
Generate a dataframe:
```
import pandas as pd
import numpy as np
column_cat = pd.CategoricalDtype(['One', 'Two', 'Three', 'Four'], ordered=True)
groups = list('AABBB')
numbers = pd.Categorical(['One', 'Two','One', 'Two', 'Three'],
categories=column_cat.categories, ordered=True)
d = pd.DataFrame(data=np.arange(15).reshape(3, 5),
columns=pd.MultiIndex.from_arrays([groups, numbers]))
```
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="2" halign="left">A</th>
<th colspan="3" halign="left">B</th>
</tr>
<tr>
<th></th>
<th>One</th>
<th>Two</th>
<th>One</th>
<th>Two</th>
<th>Three</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
</tr>
<tr>
<th>1</th>
<td>5</td>
<td>6</td>
<td>7</td>
<td>8</td>
<td>9</td>
</tr>
<tr>
<th>2</th>
<td>10</td>
<td>11</td>
<td>12</td>
<td>13</td>
<td>14</td>
</tr>
</tbody>
</table>
Now do `d.sum(axis=1, level=1)`, get the following result:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>One</th>
<th>Two</th>
<th>Three</th>
<th>Four</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2</td>
<td>4</td>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>1</th>
<td>12</td>
<td>14</td>
<td>9</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>22</td>
<td>24</td>
<td>14</td>
<td>0</td>
</tr>
</tbody>
</table>
Which produces a non existing column `Four`.
pandas 1.1.1, pandas 1.1.2
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BUG: Setting values with ILOC and list like indexers raises
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| 3
| 2020-09-30T14:21:40Z
| 2020-10-28T00:22:30Z
| 2020-10-28T00:22:30Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"flag": ["x", "y"], "value": [1, 2]})
df.iloc[[True, False], 1] = df.iloc[[True, False], 1] * 2
print(df)
```
#### Problem description
This worked on 1.0.5 and returned
```
flag value
0 x 2
1 y 2
```
On master this raises:
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_5.py", line 221, in <module>
df.iloc[[True, False], 1] = x
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 681, in __setitem__
iloc._setitem_with_indexer(indexer, value)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 1756, in _setitem_with_indexer
self._setitem_single_column(ilocs[0], value, pi)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 1800, in _setitem_single_column
ser._mgr = ser._mgr.setitem(indexer=pi, value=value)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 532, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 397, in apply
applied = getattr(b, f)(**kwargs)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/blocks.py", line 923, in setitem
check_setitem_lengths(indexer, value, values)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexers.py", line 158, in check_setitem_lengths
raise ValueError(
ValueError: cannot set using a list-like indexer with a different length than the value
Process finished with exit code 1
```
Was this change of behavior intended?
#### Expected Output
I would expect, that this would work and returns the results from 1.0.5
#### Output of ``pd.show_versions()``
<details>
master
</details>
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MDExOlB1bGxSZXF1ZXN0NDk1NTUxMjg3
| 36,742
|
TYP: some more static definitions of methods for DatetimeIndex
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| 5
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MEMBER
| null |
xref #32100, https://github.com/pandas-dev/pandas/issues/31160#issuecomment-701244665
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MDU6SXNzdWU3MTIwOTYxNDk=
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BUG: read_parquet Google Cloud Storage (gcs) dir support
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[] | null | 1
| 2020-09-30T16:29:20Z
| 2021-08-13T03:40:17Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.read_parquet("gs://bucket/path")
```
#### Problem description
In the same vain as issue [26388](https://github.com/pandas-dev/pandas/issues/26388), it would be nice for pandas to read multiple parquet files stored under a gcs path, in this case `gs://bucket/path`. Issue [26388](https://github.com/pandas-dev/pandas/issues/26388) hints that this should already work in pandas 1.x.x however, I still get one of the following errors
```
ArrowInvalid: Parquet file size is 0 bytes
```
or
```
OSError: Passed non-file path: bucket/path
```
#### Expected Output
Code runs without error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.6.0.post20200917
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: DTI/TDI.equals with i8
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| 2
| 2020-09-30T16:34:37Z
| 2020-10-02T21:47:36Z
| 2020-10-02T21:29:24Z
|
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
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DOC: TimeStamp.strftime() missing argument description
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| 2020-12-30T08:09:57Z
| 2020-12-30T02:59:13Z
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CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.Timestamp.strftime.html?highlight=strftime#pandas.Timestamp.strftime
#### Documentation problem
The argument for `TimeStamp.strftime()` is missing in the docs
#### Suggested fix for documentation
Argument is a format string. Should reference python docs https://docs.python.org/3/library/datetime.html
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Standardize cast_str behavior in all datetimelike fill_value validators
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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 will in turn allow us to simplify/de-duplicate a bunch of code since we now pass cast_str in all cases.
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[DOC]: Add explanation about DataFrame methods use all Categories
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| 2020-10-01T07:26:09Z
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MEMBER
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- [x] closes #36740
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Added notes for Jupyter & Colab users
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NONE
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Backport PR #36552 on branch 1.1.x (REGR: Series.__mod__ behaves different with numexpr)
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| 1
| 2020-09-30T20:29:02Z
| 2020-10-01T16:49:48Z
| 2020-10-01T16:49:48Z
|
NONE
| null |
Backport PR #36552: REGR: Series.__mod__ behaves different with numexpr
|
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|
Backport PR #36706 on branch 1.1.x (CI: npdev new exception message)
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| 2
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| 2020-10-01T16:48:39Z
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NONE
| null |
Backport PR #36706: CI: npdev new exception message
|
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|
REF: simplify info.py
|
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| 10
| 2020-09-30T21:31:23Z
| 2020-10-21T12:55:00Z
| 2020-10-21T12:54:55Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Enable polymorphism and builder pattern in ``info.py``.
The main benefits:
- Separated data representation from data itself
- More clear construction of each element via builder pattern
- Different builders for various cases (verbose with counts, verbose without counts, non-verbose) enabled one eliminate numerous if statements
May be useful to implement the present PR first and then build ``Series.info`` (https://github.com/pandas-dev/pandas/pull/31796) on top of this.
I do realize that I deleted ``BaseInfo`` class.
Now I see that it is required by the referenced PR, so I can move it back.
**Note:** needed to change test behavior. Here is the reason why.
I noticed inconsistency:
```
>>> df = pd.DataFrame({'long long column': np.random.rand(1000000)})
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 long long column 1000000 non-null float64
dtypes: float64(1)
memory usage: 7.6 MB
```
Here we have two spaces between columns.
However, if we create a dataframe with 10000 columns, then the distance between # col and "Column" is only one space.
```
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(np.random.rand(3, 10001))
>>> with open('out.txt', 'w') as buf: df.info(verbose=True, buf=buf)
```
```
$ tail out.txt
9993 9993 float64
9994 9994 float64
9995 9995 float64
9996 9996 float64
9997 9997 float64
9998 9998 float64
9999 9999 float64
10000 10000 float64
dtypes: float64(10001)
memory usage: 234.5 KB
```
See, only one space between the first and the second columns.
I find it inconsistent.
So, I changed it, so that there are two spaces everywhere.
What do you think?
I am going to finalize it by documenting and introducing typing where required.
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|
BUG: Segfault with string Index when using Rolling after Groupby
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| 8
| 2020-09-30T21:33:33Z
| 2020-10-10T23:35:12Z
| 2020-10-06T18:04:01Z
|
MEMBER
| null |
- [x] closes #36727
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The segfault was caused by ``self.obj.index.asi8=None`` when Index is a string Index. ``self._on.asi8`` solves that issue.
Additionally I noticed, that ``obj.index`` was already sorted, so the insert of ``extra_col`` mixed up the order. We should use ``self.obj.index``.
I will add a whats new after #36689 is merged.
cc @mroeschke
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ENH: Add headers paramater to read_json and read_csv
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| 2020-09-30T22:03:49Z
| 2020-11-27T18:43:36Z
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|
NONE
| null |
- [x] closes #36688
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This adds the option to specify headers when reading a csv or a json file from an URL in Python3.
Let me know if new tests are needed.
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ENH: allow for top and mid-level assignment to DataFrames with MultIndex columns #7475
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| 2020-09-30T23:10:34Z
| 2021-10-04T00:10:48Z
| 2021-10-04T00:10:48Z
|
CONTRIBUTOR
| null |
- [x] closes #7475 (related to #35727)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This allows for one to assign to the top and mid-levels of DataFrame columns. To do so, it adds another fallback execution path to `__setitem__`. The logic is:
- In `__setitem__`, if the other existing paths don't apply, there is a single key, and the columns are a MultiIndex, pass to `_setitem_multilevel`. Here, assign if the key exists in the columns. Otherwise, use the new function `append_block` to assign multiple columns at once.
- In the existing `_setitem_array`, as a fallback, if the length of the assigning key equals the length of the top level of the value columns, recurse these back onto `__setitem__`. This allows for higher level list assignments.
I've also added some tests of the functionality in various cases. Any comments much appreciated.
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BUG: call to pandas/pandas/core/construction.py lead to import error
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| 2020-10-01T00:48:01Z
| 2020-10-01T07:02:46Z
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NONE
| null |
pandas
Version: 1.1.2
https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py
line 17 you can see
from pandas._typing import AnyArrayLike, ArrayLike, Dtype, DtypeObj
There is no more DtypeObj in pandas._typing and this lead to import error.
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BUG: GroupBy().fillna() performance regression
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| 2020-10-01T01:27:16Z
| 2020-10-18T14:56:32Z
| 2020-10-18T14:56:32Z
|
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.
---
```python
import pandas as pd
import numpy as np
N = 2000
df = pd.DataFrame({"A": [1] * N, "B": [np.nan, 1.0] * (N // 2)})
df = df.sort_values("A").set_index("A")
df["B"] = df.groupby("A")["B"].fillna(method="ffill")
```
#### Problem description
The groupby + fillna gets extremely slow increasing the N.
This is a regression from 1.0.5->1.1.0.
Note: if I remove the `.set_index("A")` it's fast again.
#### Expected Output
Same output, just faster.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.110-1.el7.elrepo.x86_64
Version : #1 SMP Fri Jan 5 11:35:48 EST 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DEPR: |&^ as set operations for Index
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| 3
| 2020-10-01T01:34:03Z
| 2020-11-02T01:02:40Z
| 2020-11-02T01:02:40Z
|
MEMBER
| null |
It isn't that burdensome to write `left.union(right)` instead of `left | right`, and deprecating this special-case behavior allow us to fully share the implementation with Series.
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DEPR: Index == Series should return NotImplemented
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MEMBER
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ATM `index == series` returns an ndarray because `Index.__eq__` does _not_ return `NotImplemented`. It isn't clear to me what the reason for this is. The special casing leads to a non-trivial amount of complexity in the code and especially in the tests.
We should deprecate this special casing.
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BUG: inconsistent name-retention in Series ops
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| 2
| 2020-10-01T02:14:15Z
| 2020-10-07T15:00:09Z
| 2020-10-07T15:00:01Z
|
MEMBER
| null |
- [x] closes #33930
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As a follow-up I think we can remove a bunch of name-based tests from tests.arithmetic.
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ENH: Implement IntegerArray reductions
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| 2020-10-01T02:46:29Z
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|
MEMBER
| null |
- [x] ref #33790
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I'm not sure if this is the "best" way to implement these reductions (see linked issue), but in any case I figure this is better than nothing since we can get these reductions pretty much for free. As far as I can tell FloatingArray can use the same machinery.
(I'm not able to pass on kwargs here because the masked reductions don't take them all, e.g. `axis`, and various tests break as a result.)
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| 1
| 2020-10-01T05:47:52Z
| 2020-10-01T10:54:48Z
| 2020-10-01T10:54:27Z
|
CONTRIBUTOR
| null |
…st_chaining_and_caching.py
- [ ] ref https://github.com/pandas-dev/pandas/issues/30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 2
| 2020-10-01T06:13:25Z
| 2020-10-03T01:43:39Z
| 2020-10-02T17:34:28Z
|
CONTRIBUTOR
| null |
- [x] closes #36764
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As far as I can see, there are some PR09 pandas docstring errors. So with this PR I will resolve them. There are more commits soon.
```
PR09
Parameter description should finish with "."
```
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DOC: PR09 errors
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|
CONTRIBUTOR
| null |
#### Location of the documentation
- [x] [/core/frame.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py)
- [x] [/core/generic.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py)
- [x] [/core/flags.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/flags.py)
- [x] [/core/series.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py)
- [x] [/core/groupby/generic.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py)
- [x] [/core/computation/eval.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/computation/eval.py)
- [x] [/io/pytables.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/pytables.py)
- [x] [/io/pickle.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/pickle.py)
- [x] [/io/json/_json.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/json/_json.py)
#### Documentation problem
PR09 pandas docstring errors. `Parameter description should finish with "."`
#### Suggested fix for documentation
Add `.` at the end of the parameter descriptions.
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| 0
| 2020-10-01T07:48:07Z
| 2020-10-07T15:08:18Z
| 2020-10-07T15:08:18Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
See that the spacing between all columns is 2 spaces. Even if there is a long string in Non-Null Count column.
```python
df = pd.DataFrame({'long long column': np.random.rand(1000000)})
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 long long column 1000000 non-null float64
dtypes: float64(1)
memory usage: 7.6 MB
```
Note that there is only one space between # and Column columns when the number of columns is over 1000 (last line of tail).
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(3, 10001))
with open('out.txt', 'w') as buf:
df.info(verbose=True, buf=buf)
```
```bash
$ tail out.txt
9993 9993 float64
9994 9994 float64
9995 9995 float64
9996 9996 float64
9997 9997 float64
9998 9998 float64
9999 9999 float64
10000 10000 float64
dtypes: float64(10001)
memory usage: 234.5 KB
```
#### Problem description
I find inconsistent behavior in spacing between columns in the output ``df.info()`` when dealing with dataframes having over 1000 columns.
#### Expected Output
Expect to have two spaces distance between all columns regardless of the col widths.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f6ccbbc1ee852754a268c46454803bc9a3f9aebf
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+559.gf6ccbbc1e.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 45.2.0.post20200210
Cython : 0.29.21
pytest : 6.0.1
hypothesis : 5.23.11
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.3.1
lxml.etree : 4.4.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : 0.4.1
gcsfs : 0.6.2
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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| 1
| 2020-10-01T07:52:29Z
| 2020-10-07T15:08:18Z
| 2020-10-07T15:08:18Z
|
MEMBER
| null |
- [x] closes #36765
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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MDExOlB1bGxSZXF1ZXN0NDk2MTA2NDAx
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BUG: Index sortlevel ascending add type checking #32334
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| 2
| 2020-10-01T09:22:49Z
| 2020-10-05T05:57:39Z
| 2020-10-01T18:32:25Z
|
CONTRIBUTOR
| null |
- [x] closes #32334
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Add the check of ascending parameter in the sortlevel method, only allow bool value and single bool value list
cc @Dr-Irv
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Expand Series.between() function with only left/right inclusive
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| 3
| 2020-10-01T09:33:27Z
| 2020-11-19T00:16:46Z
| 2020-10-01T13:51:45Z
|
NONE
| null |
https://github.com/pandas-dev/pandas/blob/2a7d3326dee660824a8433ffd01065f8ac37f7d6/pandas/core/series.py#L4690-L4763
This is my first post, I hope this is conform the contributing guidelines and code of conduct.
The code for between has a variable 'inclusive' which makes a difference if the left and right value should be taken into account. Difference between < and <= for example.
My suggestion is to extend the function between a bit. Sometimes you just want to include the left value and exclude the right value. Or the way arround.
This change is possible in many ways. One example is:
```
def between(self, left, right, left_inclusive=True, right_inclusive=True) -> "Series":
"""
Return boolean Series equivalent to left <= series <= right.
This function returns a boolean vector containing `True` wherever the
corresponding Series element is between the boundary values `left` and
`right`. NA values are treated as `False`.
Parameters
----------
left : scalar or list-like
Left boundary.
right : scalar or list-like
Right boundary.
left_inclusive : bool, default True
Include boundaries of left value.
right_inclusive : bool, default True
Include boundaries of right value.
Returns
-------
Series
Series representing whether each element is between left and
right (inclusive).
See Also
--------
Series.gt : Greater than of series and other.
Series.lt : Less than of series and other.
Notes
-----
This function is equivalent to ``(left <= ser) & (ser <= right)``
Examples
--------
>>> s = pd.Series([2, 0, 4, 8, np.nan])
Boundary values are included by default:
>>> s.between(1, 4)
0 True
1 False
2 True
3 False
4 False
dtype: bool
With `inclusive` set to ``False`` boundary values are excluded:
>>> s.between(1, 4, right_inclusive=False)
0 True
1 False
2 False
3 False
4 False
dtype: bool
`left` and `right` can be any scalar value:
>>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
>>> s.between('Anna', 'Daniel')
0 False
1 True
2 True
3 False
dtype: bool
"""
if left_inclusive:
lmask = self >= left
else:
lmask = self > left
if right_inclusive:
rmask = self <= right
else:
rmask = self < right
return lmask & rmask
```
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MDU6SXNzdWU3MTI2ODcwMjU=
| 36,769
|
BUG: to_timedelta() won't accept all freq units returned by infer_freq()
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| 2020-10-01T09:55:04Z
| 2021-08-13T03:41:29Z
| null |
NONE
| null |
- [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = '2H')
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fine
datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = 'H')
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fails.... : ValueError: unit abbreviation w/o a number
datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = '1H') # datetime drops to 'H'
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fails.... : ValueError: unit abbreviation w/o a number
td_infer = pd.to_timedelta('1' + pd.infer_freq(datetime_idx) ) # Works...
```
#### Problem description
to_timedelta() rejects unitary time units like 'H', 'D' etc which infer_freq() returns. Strings like '2H' etc are accepted. Even if you create the date range with '1H' freq it is dropped back to 'H' by date_range(). So you are forced to handle this special case and prepend a '1' which feels very clumsy.
#### Expected Output
to_timedelta() should treat strings like 'H' as for '1H' - so output of infer_freq() can be safely passed through.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.6.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 28.8.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CI: troubleshoot travis ci on 1.1.x
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- [ ] closes #xxxx
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BUG: Timedelta drops decimals if precision is greater than nanoseconds
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| 2020-10-01T12:09:11Z
| 2020-11-15T17:32:28Z
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MEMBER
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- [x] whatsnew entry
I am not quite sure, if we should round to nanoseconds or cut if off after the 9th decimal. I implemented a simple cut off, but would be happy to change if rounding is better.
I would also add a note to the user guide if the future behavior is clear
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Update README.md
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MDExOlB1bGxSZXF1ZXN0NDk2MjU4NTIz
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DOC: Fix code block line length
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https://github.com/pandas-dev/pandas/pull/36734#discussion_r498058234
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scatterplot issue
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| 2020-10-02T09:08:37Z
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NONE
| null |
added basic idea to add labels to scatter plot
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BUG: Nullable floats can look like ints in repr
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| 3
| 2020-10-01T14:21:54Z
| 2020-10-16T01:46:54Z
| 2020-10-16T01:46:54Z
|
MEMBER
| null |
Nullable floats when output to the screen can look like ints, but in my opinion should look the same as regular non-NaN floats so as not to cause confusion.
```python
import pandas as pd
s = pd.Series([0.0, 1.0, None], dtype="Float64")
print(s)
# 0 0
# 1 1
# 2 <NA>
# dtype: Float64
print(s.astype(float))
# 0 0.0
# 1 1.0
# 2 NaN
# dtype: float64
pd.__version__
# '1.2.0.dev0+560.g86f543143'
```
cc @jorisvandenbossche
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Very useful toolkit for pythonUpdate
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| 2020-10-02T09:08:28Z
| 2020-10-01T14:52:56Z
|
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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|
DOC: Fix code style in documentation
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| 19
| 2020-10-01T14:54:50Z
| 2020-10-10T22:55:19Z
| 2020-10-10T22:24:28Z
|
MEMBER
| null |
The docs use certain coding style conventions that are no longer used in pandas (e.g., we adopt black's opinions in terms of line wrapping and quoting), for instance https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html. We should update the docs to match current style preferences.
[blacken-docs](https://github.com/asottile/blacken-docs) is a useful tool that can help in doing _some_ of this formatting, but it has to be used carefully since it might render lines that are too long and will fail flake8-rst checks, or it may not be able to parse certain files because of nuances in how the docs are written (raw output or magic commands in code blocks). One approach is to comment out the erring lines, let the tool handle all the others, then finish the commented lines manually.
Example cleanup: https://github.com/pandas-dev/pandas/pull/36700/files
List of files that need updating (if you submit a PR be sure to link back to this issue):
**doc/source/development:**
- [x] code_style.rst
- [x] contributing.rst
- [x] contributing_docstring.rst
- [x] developer.rst
- [x] extending.rst
- [x] index.rst
- [x] internals.rst
- [x] maintaining.rst
- [x] meeting.rst
- [x] policies.rst
- [x] roadmap.rst
**doc/source/getting_started/comparison:**
- [x] comparison_with_r.rst
- [x] comparison_with_sas.rst
- [x] comparison_with_sql.rst
- [x] comparison_with_stata.rst
- [x] index.rst
**doc/source/getting_started/intro_tutorials:**
- [x] 01_table_oriented.rst
- [x] 02_read_write.rst
- [x] 03_subset_data.rst
- [x] 04_plotting.rst
- [x] 05_add_columns.rst
- [x] 06_calculate_statistics.rst
- [x] 07_reshape_table_layout.rst
- [x] 08_combine_dataframe.rst
- [x] 09_timeseries.rst
- [x] 10_text_data.rst
**doc/source/getting_started:**
- [x] index.rst
- [x] install.rst
- [x] overview.rst
- [x] tutorials.rst
**doc/source/user_guide**:
- [x] 10min.rst
- [x] advanced.rst
- [x] basics.rst
- [x] boolean.rst
- [x] categorical.rst
- [x] computation.rst
- [x] cookbook.rst
- [x] dsintro.rst
- [x] duplicates.rst
- [x] enhancingperf.rst
- [x] gotchas.rst
- [x] groupby.rst
- [x] index.rst
- [x] integer_na.rst
- [x] io.rst
- [x] merging.rst
- [x] missing_data.rst
- [x] options.rst
- [x] reshaping.rst
- [x] scale.rst
- [x] sparse.rst
- [x] text.rst
- [x] timedeltas.rst
- [x] timeseries.rst
- [x] visualization.rst
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|
ENH: Implement FloatingArray reductions
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| 3
| 2020-10-01T15:28:36Z
| 2020-10-03T05:18:31Z
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|
MEMBER
| null |
Mostly copy / paste from https://github.com/pandas-dev/pandas/pull/36761
@jorisvandenbossche Would this require a whatsnew note? Seems the current v1.2.0 note is fairly high level and doesn't talk about specific methods that are supported. Should we talk about this or leave as-is?
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CI: troubleshoot UnsatisfiableError in travis-36-cov on 1.1.x
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| 2020-10-01T16:44:58Z
| 2020-11-26T10:31:31Z
| 2020-10-01T17:29:43Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDk2NDExMTA2
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DOC: Fix code style in documentation
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CONTRIBUTOR
| null |
ref #36777
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MDExOlB1bGxSZXF1ZXN0NDk2NDIxMTUx
| 36,781
|
Revert "Update README.md"
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| 2020-10-01T17:22:24Z
| 2020-10-01T17:23:08Z
| 2020-10-01T17:22:58Z
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MEMBER
| null |
Reverts pandas-dev/pandas#36772
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|
BUG: replacing out of bound datetimes is not possible
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| 2021-08-13T03:43:57Z
| 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
from datetime import datetime
from pandas import DataFrame
# typo in the data entry, should have been 2020!
df = DataFrame({'x': [datetime(2920, 10, 1)]})
# let's try to fix that in code:
df.x.replace({datetime(2920, 10, 1): datetime(2020, 10, 1)})
# or
df.replace({datetime(2920, 10, 1): datetime(2020, 10, 1)})
```
Raises:
```python
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2920-10-01 00:00:00
```
<details>
<summary>Full traceback details</summary>
```python-traceback
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-1-a4bdb67c8945> in <module>
5 df = DataFrame({'x': [datetime(2920, 10, 1)]})
6 # let's try to fix that in code:
----> 7 df.x.replace({datetime(2920, 10, 1): datetime(2020, 10, 1)})
/site-packages/pandas/core/series.py in replace(self, to_replace, value, inplace, limit, regex, method)
4561 method="pad",
4562 ):
-> 4563 return super().replace(
4564 to_replace=to_replace,
4565 value=value,
/site-packages/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6495 to_replace, value = keys, values
6496
-> 6497 return self.replace(
6498 to_replace, value, inplace=inplace, limit=limit, regex=regex
6499 )
/site-packages/pandas/core/series.py in replace(self, to_replace, value, inplace, limit, regex, method)
4561 method="pad",
4562 ):
-> 4563 return super().replace(
4564 to_replace=to_replace,
4565 value=value,
/site-packages/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6538 )
6539 self._consolidate_inplace()
-> 6540 new_data = self._mgr.replace_list(
6541 src_list=to_replace,
6542 dest_list=value,
/site-packages/pandas/core/internals/managers.py in replace_list(self, src_list, dest_list, inplace, regex)
640 mask = ~isna(values)
641
--> 642 masks = [comp(s, mask, regex) for s in src_list]
643
644 result_blocks = []
/site-packages/pandas/core/internals/managers.py in <listcomp>(.0)
640 mask = ~isna(values)
641
--> 642 masks = [comp(s, mask, regex) for s in src_list]
643
644 result_blocks = []
/site-packages/pandas/core/internals/managers.py in comp(s, mask, regex)
633 return ~mask
634
--> 635 s = com.maybe_box_datetimelike(s)
636 return _compare_or_regex_search(values, s, regex, mask)
637
/site-packages/pandas/core/common.py in maybe_box_datetimelike(value, dtype)
88
89 if isinstance(value, (np.datetime64, datetime)):
---> 90 value = tslibs.Timestamp(value)
91 elif isinstance(value, (np.timedelta64, timedelta)):
92 value = tslibs.Timedelta(value)
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_to_tsobject()
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_datetime_to_tsobject()
pandas/_libs/tslibs/np_datetime.pyx in pandas._libs.tslibs.np_datetime.check_dts_bounds()
```
</details>
#### Problem description
It is useful to be able to replace the dates which are out of bound, because these are not supported by pandas. However, it is currently difficult because replace no longer works for them. This is a regression as the above code worked well in pandas 1.0.4, but does not work in pandas 1.1.2 nor on master.
#### Expected Output
`df` should be equal to `DataFrame({'x': [datetime(2020, 10, 1)]})`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.0
</details>
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Test that nan value counts are included when dropna=False. GH#31944
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| 2
| 2020-10-01T19:38:27Z
| 2020-10-06T22:43:21Z
| 2020-10-06T22:43:17Z
|
CONTRIBUTOR
| null |
- [x] closes #31944
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added test per @Dr-Irv request.
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MDExOlB1bGxSZXF1ZXN0NDk2NTM4NTYw
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CLN: test_moments_rolling.py for quantile/kurt/skew
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| 1
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REGR: Row series are broken after applying to_datetime()
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| 8
| 2020-10-01T20:56:11Z
| 2020-12-04T16:21:24Z
| 2020-12-04T16:21:24Z
|
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
from io import StringIO
from pandas import read_csv, to_datetime, options
df = read_csv(StringIO("""\
,A,B,C,D,E,F
P0,,2020-10-01 08:00:00+00:00,,,,2020-10-16 00:01:00+00:00
"""), index_col=0)
# works
options.display.max_rows = 6
df.apply(lambda d: to_datetime(d, utc=True), axis=0).apply(lambda x: str(x), axis=1)
# raises
options.display.max_rows = 5
df.apply(lambda d: to_datetime(d, utc=True), axis=0).apply(lambda x: str(x), axis=1)
# raises
df.apply(lambda d: to_datetime(d, utc=True), axis=0).apply(lambda x: x.dropna().apply(lambda y: getattr(y, 'year')), axis=1)
# in v 1.0.4 would return:
# B F
# P0 2020 2020
```
#### Problem description
Assigning the output of `pd.to_datetime` to a column of a dataframe, although not demonstrated in documentation is [a popular use of this helper function](https://stackoverflow.com/a/26763793/6646912). In previous versions of pandas (1.0.x) it was possible to convert multiple columns using `to_datetime` in combination with `DataFrame.apply`. It is still possible in 1.1.2 and master:
```python
>>>df.apply(lambda d: to_datetime(d, utc=True), axis=0).dtypes
A datetime64[ns, UTC]
B datetime64[ns, UTC]
...
E datetime64[ns, UTC]
F datetime64[ns, UTC]
Length: 6, dtype: object
```
However, the rows in the subsequent apply operations are broken for certain operations. For example, trying to print them out raises: `TypeError: cannot concatenate object of type '<class 'numpy.ndarray'>'; only Series and DataFrame objs are valid`. This was not the case in pandas 1.0.4.
```python-traceback
X in Y
10
11 # raises
---> 12 df.apply(lambda d: to_datetime(d, utc=True), axis=0).apply(lambda x: str(x), axis=1)
/pandas/core/frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
7545 kwds=kwds,
7546 )
-> 7547 return op.get_result()
7548
7549 def applymap(self, func) -> "DataFrame":
/pandas/core/apply.py in get_result(self)
178 return self.apply_raw()
179
--> 180 return self.apply_standard()
181
182 def apply_empty_result(self):
/pandas/core/apply.py in apply_standard(self)
253
254 def apply_standard(self):
--> 255 results, res_index = self.apply_series_generator()
256
257 # wrap results
/pandas/core/apply.py in apply_series_generator(self)
282 for i, v in enumerate(series_gen):
283 # ignore SettingWithCopy here in case the user mutates
--> 284 results[i] = self.f(v)
285 if isinstance(results[i], ABCSeries):
286 # If we have a view on v, we need to make a copy because
X in <lambda>(x)
10 df.apply(lambda x: str(x), axis=1)
11 # raises
---> 12 df.apply(lambda d: to_datetime(d, utc=True), axis=0).apply(lambda x: str(x), axis=1)
/pandas/core/series.py in __repr__(self)
1313 show_dimensions = get_option("display.show_dimensions")
1314
-> 1315 self.to_string(
1316 buf=buf,
1317 name=self.name,
/pandas/core/series.py in to_string(self, buf, na_rep, float_format, header, index, length, dtype, name, max_rows, min_rows)
1372 String representation of Series if ``buf=None``, otherwise None.
1373 """
-> 1374 formatter = fmt.SeriesFormatter(
1375 self,
1376 name=name,
/pandas/io/formats/format.py in __init__(self, series, buf, length, header, index, na_rep, name, float_format, dtype, max_rows, min_rows)
259 self.adj = _get_adjustment()
260
--> 261 self._chk_truncate()
262
263 def _chk_truncate(self) -> None:
/pandas/io/formats/format.py in _chk_truncate(self)
283 else:
284 row_num = max_rows // 2
--> 285 series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
286 self.tr_row_num = row_num
287 else:
/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
272 ValueError: Indexes have overlapping values: ['a']
273 """
--> 274 op = _Concatenator(
275 objs,
276 axis=axis,
/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'numpy.ndarray'>'; only Series and DataFrame objs are valid
```
#### Expected Output
Should not raise.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.0
</details>
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CLN: Remove param _set_identity from MultiIndex
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| 2
| 2020-10-01T20:57:18Z
| 2020-10-02T21:56:57Z
| 2020-10-02T21:13:22Z
|
CONTRIBUTOR
| null |
I don't see how `_set_identity`can be needed and other index classes don'r have this parameter, so best to just remove it.
Other index classes always end by calling `_reset_identity`, so I just do that here also. I'm not sure that's really needed, but that's for another day.
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MDExOlB1bGxSZXF1ZXN0NDk2NTQ1NjQz
| 36,787
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BUG: Rolling returned nan with FixedForwardWindowIndexer for count when window contained only missing values
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| 2020-10-01T21:04:25Z
| 2020-10-01T21:56:59Z
| 2020-10-01T21:56:53Z
|
MEMBER
| null |
- [x] closes #35579
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Issue still has milestone 1.1.3. If it's to late for this release, I'll move the whats new note to 1.1.4 for when the associated PR is merged.
cc @mroeschke
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MDU6SXNzdWU3MTMxODkxNjk=
| 36,788
|
API: why do DataFrame bool ops have default_axis=columns while arith/cmp have default_axis=None?
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| 0
| 2020-10-01T21:14:44Z
| 2020-10-02T21:41:08Z
| 2020-10-02T21:41:08Z
|
MEMBER
| null |
Changing the default_axis to None doesn't break any tests, and that would allow for a bit of code simplification. So is there a reason for the current implementation?
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MDExOlB1bGxSZXF1ZXN0NDk2NTYzMjUz
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TST: Add test for 32724
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| 2020-10-01T21:43:02Z
| 2020-10-02T18:35:37Z
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|
MEMBER
| null |
- [x] closes #32724
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Was fixed in the past.
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MDExOlB1bGxSZXF1ZXN0NDk2NTY0Mzcz
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BUG: GroupBy.ffill()/bfill() do not return NaN values for NaN groups
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| 2020-10-01T21:45:55Z
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|
MEMBER
| null |
- [x] closes #34725
- [x] tests added / passed
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- [x] whatsnew entry
See #34725 for a good description of the problem.
This fixes the issue in the tidiest way I could find.
One test is added that covers all relevant cases I could think of. The comments in the test can be removed, they are just there to make it easier for the reviewer to follow as the test is a bit complicated.
This also fixes the copy-pastable example from the #34725.
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How to use nrows along with chunksize in read_json()
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| 2
| 2020-10-01T21:53:46Z
| 2020-12-23T16:32:22Z
| 2020-12-23T16:32:22Z
|
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).
---
#### Question about pandas
Why do I need to use _nrows_ when reading large json line files with _chunksize_ option?
Since version 1.1 I'm having troubles with the function _read_json()_ because even if I specify the option _chunksize_ with the correct value (the value that used to work with pandas v.1.0.5), the file seems to be read at once, with a memroy error in my case. If I add the _nrows_ option this doesn't happen but why? And what is the value you have to specify for the _nrows_ parameter in order to load the entire file? Do you have to know in advance the maximum number of rows? Is there any special value for "all rows" like -1 o 0 ?
Thanks
```python
#this raises a Memory Error (with a 4GB file) - this worked on version 1.0.5
reader = pd.read_json(f"{path}map_records.json",orient='records' ,lines=True, chunksize=100000)
chunks=[chunk[(chunk.bidbasket=="BSKGEOALL00000000001")&(chunk.tipomappa == "AULTIPMPS_GIT")][['bidsubzona','idoriginale','bidciv','bidbasket','tipomappa']] for chunk in reader]
#this works, but it loads up to <nrorws> rows and I have to know the maximum number of rows in advance
reader = pd.read_json(f"{path}map_records.json",orient='records' ,lines=True, chunksize=100000, nrows=20000000)
chunks=[chunk[(chunk.bidbasket=="BSKGEOALL00000000001")&(chunk.tipomappa == "AULTIPMPS_GIT")][['bidsubzona','idoriginale','bidciv','bidbasket','tipomappa']] for chunk in reader]
```
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ERR: error handling in DataFrame.__rmatmul__
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| 0
| 2020-10-01T22:19:02Z
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|
MEMBER
| null |
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API: make DataFrame.__boolop__ default_axis match DataFrame.__arithop__ default_axis
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| 1
| 2020-10-01T22:39:18Z
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MEMBER
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Fix a test warning generated by an ipython test, unrelated
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| 3
| 2020-10-01T22:50:39Z
| 2020-10-02T23:48:20Z
| 2020-10-02T23:35:03Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
data = {
'groupby_col': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', ],
'agg_col': [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],
}
df = pd.DataFrame(data).set_index("groupby_col")
grouped = df.groupby('groupby_col')
rolled = grouped.rolling(4)
result = rolled.mean()
print(result)
```
Output:
```
agg_col
groupby_col groupby_col
A A NaN
A NaN
A NaN
A 0.75
A 0.50
B B NaN
B NaN
B NaN
B 0.25
B 0.25
```
#### Problem description
The duplicate ``groupby_col`` seems at least odd.
...
#### Expected Output
Would expect, that we get ``groupby_col`` only once in the index and a ``Series`` instead of a ``DataFrame``
Maybe related to #36507, but
```
data = {
'groupby_col': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', ],
'agg_col': [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],
}
df = pd.DataFrame(data).set_index("groupby_col")
grouped = df.groupby('groupby_col')
result = grouped.apply(sum)
print(result)
```
works. So I am not sure.
#### Output of ``pd.show_versions()``
<details>
master
</details>
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MDExOlB1bGxSZXF1ZXN0NDk2NTk1NzAw
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DEPR: automatic alignment on frame.__cmp__(series)
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| 1
| 2020-10-01T23:14:45Z
| 2020-10-03T01:16:39Z
| 2020-10-03T00:55:37Z
|
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
xref #28759, may close it depending on if we want to do anything else on that front
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API: disallow frame.__and__(other, axis="foo", fill_value="bar") etc
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| 1
| 2020-10-01T23:20:46Z
| 2020-10-10T22:27:19Z
| 2020-10-10T22:27:19Z
|
MEMBER
| null |
For code-sharing purposes we re-use ops.arith_method_FRAME for both flex and non-flex methods. As a result, the non-flex methods have the flex signature, and accept arguments not generally accepted by dunder methods. We even have a few tests e.g.
```
result = d.__and__(s, axis="columns")
result = d.__and__(s, axis=0)
```
in tests.frame.test_operators.
Should we disallow passing these arguments? Is it relevant that we do _not_ have flex versions of and/or/xor?
|
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|
DOC: remove outdated doc closes #31487
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| 1
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| 2020-10-02T23:00:15Z
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MEMBER
| null |
- [x] closes #31487
- [ ] tests added / passed
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MDU6SXNzdWU3MTMyNjQxMzY=
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|
Reading xml files not directly available in Pandas?
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| 2020-10-16T08:21:11Z
| 2020-10-02T00:21:10Z
|
NONE
| null |
- [ X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). (There are workarounds to this, but this is a question to the devs)
---
#### Question about pandas
I have already figured out a workaround to reading xml files by using '**xml.etree.ElementTree**', but I was not able to find a read_xml() function in the pandas library.
There is another package '**https://pypi.org/project/pandas-read-xml/**' that implements this function, but it seems like extra steps for an individual to download another package if they wanted to read the xml file (should they decide not to implement it themselves).
May I know the thought process for not having such function, or if I am blatantly missing something, kindly excuse the ignorance.
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MDExOlB1bGxSZXF1ZXN0NDk2NjI5NzAw
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BUG/CLN: Clean float / complex string formatting
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| 6
| 2020-10-02T01:24:54Z
| 2020-10-14T15:06:37Z
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|
MEMBER
| null |
Noticed while working on another bug. The _is_number helper here is wrong and can cause incorrect results given that this code path is hit by arbitrary strings (e.g., it thinks "foo" is a number). Also the _trim_zeros_complex helper apparently does nothing:
```python
[ins] In [3]: _trim_zeros_float(["0.00000"])
Out[3]: ['0.0']
[ins] In [4]: _trim_zeros_complex(["1.000+1.000000j"])
Out[4]: ['1.000+1.000000j']
```
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MDExOlB1bGxSZXF1ZXN0NDk2NjMyNjM4
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BUG: Fix FloatingArray output formatting
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| 2
| 2020-10-02T01:38:28Z
| 2020-10-16T06:17:06Z
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| null |
- [x] closes #36775
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
CLN: Use more pytest idioms in test_momemts_ewm.py
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| 1
| 2020-10-02T02:34:31Z
| 2020-10-02T20:27:26Z
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Use `pytest.mark.parameterize` and unnest helper test functions
|
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MDExOlB1bGxSZXF1ZXN0NDk2NzAzMDg2
| 36,802
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DOC: ran blacken docs tool and checked output to improve formatting #36777
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| 2020-10-02T06:09:05Z
| 2020-10-06T08:15:40Z
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|
CONTRIBUTOR
| null |
For task #36777
Ran blacken-tools on user_guide/10min.rst, user_guide/advanced.rst, basics.rst
|
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|
github --> GitHub
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| 2020-10-02T13:30:29Z
| 2020-10-02T13:30:18Z
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NONE
| null |
Changed from "github" to "GitHub" in README.md to make it consistent throughout the document.
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DOC: unclear kwarg null_counts
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| 3
| 2020-10-02T13:48:42Z
| 2020-11-25T20:59:23Z
| 2020-11-25T20:59:23Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.info.html?highlight=info#pandas.DataFrame.info
#### Documentation problem
Keyword ``null_counts`` from its name may be considered as a need to count null items.
However, the documentation explains that is quite the opposite - count **non-null** items.
I suggest to rename it with ``show_counts`` and maintain the old behavior.
#### Suggested fix for documentation
Replace ``null_counts`` with ``show_counts``.
It will make it more clear what this kwarg means without even the need to read docs.
#### API
It the suggested rename is implemented, then need to start deprecation of kwarg ``null_counts``.
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BUG: resample inconsistency with closed left/right
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| 2020-10-02T14:28:58Z
| 2021-08-13T03:45:25Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
t = pd.date_range("2011-01-01T00:00:00", "2011-02-01T00:00:00", freq="10T")
y = np.ones(len(t))
df = pd.DataFrame(data=y, index=t, columns=['y'])
df
# default for '1M' is closed-left
resampled_df = df.resample('1M', closed='left', label='right').sum()
resampled_df
# bin edges are last day of month @ start of day
# this is why the whole day of 2011-01-31 has been included in the second bin
resampled_df.index[0]
# now with closed-right
# I am expecting 1 data point (precisely on the bin edge @ 2011-01-31 00:00:00) to shift into the first bin
resampled_df = df.resample('1M', closed='right', label='right').sum()
resampled_df
# now the first bin includes the whole day of 2011-01-31
# despite the fact that the bin edge is 2011-01-31 00:00:00
resampled_df.index[0]
```
executed...
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> t = pd.date_range("2011-01-01T00:00:00", "2011-02-01T00:00:00", freq="10T")
>>> y = np.ones(len(t))
>>> df = pd.DataFrame(data=y, index=t, columns=['y'])
>>>
>>> df
y
2011-01-01 00:00:00 1.0
2011-01-01 00:10:00 1.0
2011-01-01 00:20:00 1.0
2011-01-01 00:30:00 1.0
2011-01-01 00:40:00 1.0
... ...
2011-01-31 23:20:00 1.0
2011-01-31 23:30:00 1.0
2011-01-31 23:40:00 1.0
2011-01-31 23:50:00 1.0
2011-02-01 00:00:00 1.0
[4465 rows x 1 columns]
>>>
>>> # default for '1M' is closed-left
>>> resampled_df = df.resample('1M', closed='left', label='right').sum()
>>>
>>> resampled_df
y
2011-01-31 4320.0
2011-02-28 145.0
>>>
>>> # bin edges are last day of month @ start of day
>>> # this is why the whole day of 2011-01-31 has been included in the second bin
>>> resampled_df.index[0]
Timestamp('2011-01-31 00:00:00', freq='M')
>>>
>>> # now with closed-right
>>> # I am expecting 1 data point (precisely on the bin edge @ 2011-01-31 00:00:00) to shift into the first bin
>>> resampled_df = df.resample('1M', closed='right', label='right').sum()
>>>
>>> resampled_df
y
2011-01-31 4464.0
2011-02-28 1.0
>>>
>>> # now the first bin includes the whole day of 2011-01-31
>>> # despite the fact that the bin edge is 2011-01-31 00:00:00
>>> resampled_df.index[0]
Timestamp('2011-01-31 00:00:00', freq='M')
```
#### Problem description
Switching between closed left/right should affect at most 1 data point in a timeseries, but in the example above it affects an entire day of data.
We were also surprised that '1M' (calendar month end) is defined as last day of month _@ start of day_.
#### Expected Output
see comments in example above
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : None
```
</details>
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ENH: read_csv(data_url, verify = False)
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| 2
| 2020-10-02T14:38:52Z
| 2021-10-01T19:17:20Z
| 2020-10-02T23:28:26Z
|
NONE
| null |
#### Is your feature request related to a problem?
Pandas currently allows a csv to be opened from a URL, but does not allow you to set verify to false in the event that URL has an ssl certificate error.
#### Describe the solution you'd like
please add verify=false parameter to the pandas.read_csv() function when reading a file from a weburl.
#### API breaking implications
not sure
#### Describe alternatives you've considered
downloading the file first locally and overriding the ssl error via request.
#### Additional context
SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:1076)
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
<ipython-input-9-922c6d1e33ad> in <module>
----> 1 df = pd.read_csv(fileDl)
```python
# Your code here, if applicable
```
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Fix select_dtypes(include='int') for Windows.
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…st_indexing.py
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CLN: test_moments_consistency_*.py
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MEMBER
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- [x] tests added / passed
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In-lining some common functions to make it easier to follow test failure tracebacks
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DOC: use blacken to fix code style in documentation #36777
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CONTRIBUTOR
| null |
Addresses part of #36777
Ran blacken-tools on cookbook.rst and checked for warnings from flake8-rst
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DOC: use black to fix code style in doc pandas-dev#36777
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CONTRIBUTOR
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Partially addresses #36777. Ran black on all the files under `doc/source/getting_started/comparison`.
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DOC: add example & prose of slicing with labels when index has duplicate labels
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CONTRIBUTOR
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- [x] closes #36251
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DOC: uses black to fix formatting #36777
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CONTRIBUTOR
| null |
Fixes /docs/source/user_guide/merging.rst formatting. Passes flake8-rst test.
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ref https://github.com/pandas-dev/pandas/issues/36777
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Rolling on DataFrameGroupBy duplicated index column when part of the grouping cols is from index
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| 2020-10-02T19:29:15Z
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MEMBER
| null |
…grouping cols is from index
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Could not find a elegant way, to avoid adding the components in the for loop, so I dropped it from the resulting MultiIndex again.
Had to change a test, which depended on the wrong behavior.
cc @mroeschke
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DOC: update code style for remaining intro tutorial docs for #36777
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CONTRIBUTOR
| null |
Addresses part of #36777
Ran blacken-tools and checked for warnings from flake8-rst for the remaining 5 intro tutorials. Note that tutorial 3 met the formatting requirements as is, so there is no change there.
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Backport PR #36675 on branch 1.1.x (REGR: Series.loc with a MultiIndex containing Timestamp raises InvalidIndexError)
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NONE
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Backport PR #36675: REGR: Series.loc with a MultiIndex containing Timestamp raises InvalidIndexError
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"diff_url": "https://github.com/pandas-dev/pandas/pull/36818.diff",
"html_url": "https://github.com/pandas-dev/pandas/pull/36818",
"merged_at": "2020-10-03T11:50:23Z",
"patch_url": "https://github.com/pandas-dev/pandas/pull/36818.patch",
"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/36818"
}
|
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