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Pandas strftime function prints unix epoch time in local TZ instead of UTC
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| 2021-01-12T00:16:50Z
| 2021-01-13T19:05:09Z
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NONE
| null |
I'm trying to convert a given date and time to unix epoch time using Pandas. This is normally a very straightforward thing to do, merely converting the string of date and time to a datetime object, and then printing the object as a unix epoch timestamp using strftime, for example:
`pd.to_datetime('2021-01-01 00:00:00').strftime('%s')`
Running the above example on a remote linux machine I have set up, whose system date is recorded in UTC, gets me the expected output of "1609459200" which is 2020-01-01 00:00:00 in unix epoch time. However, when pulling my code onto my Mac laptop and running locally has resulted in what was unexpected to me at first: that the above code prints off a unix epoch time 7 hours in advance, or a string reading "1609484400".
Running the date command on my Mac OS terminal gives me the time in MST, which is where I live, instead of UTC like the remote linux machine I pulled my code from. MST is 7 hours behind UTC so I assume that Pandas is trying to account for the MST time recorded on my laptop and doing some kind of internal conversion. I figure that I need to specify that the time I'm inputing is UTC, so I tried that both with "utc=True" for `pd.to_datetime()` as well as "tz='UTC'" for `pd.Timestamp()`. No dice though, as I still get a return value of "1609484400".
Output when running with pd.to_datetime():
> dt = pd.to_datetime('2021-01-01 00:00:00', utc=True)
> dt
> ->Timestamp('2021-01-01 00:00:00+0000', tz='UTC')
>
> dt.strftime('%Y-%m-%d %H:%M:%S')
> -> '2021-01-01 00:00:00' # Converting back to the original format gives me the original string
>
> dt.strftime('%B %d, %Y, %r')
> -> 'January 01, 2021, 12:00:00 AM' # Expected output
>
> dt.strftime('%s')
> -> '1609484400' # This is '2021-01-01 07:00:00+00:00' in unix epoch time (7 hours ahead)
>
> dt.tz_convert('MST').strftime('%s')
> -> '1609484400' # Converting the timezone to MST before strftime strangely produces the same result
The exact same outputs occurred using `pd.Timestamp()` with the timezone set to either UTC or MST. Again, the expected output for `dt.strfime('%s')` should be "1609484400" in UTC, and I'm confused why I can't produce this value on my local machine. I know I could try to futz around with my Mac's system date, or hardcode a solution into my script by subtracting 7 hours from the dt object, but I don't want to change my Mac's system date, nor do I want to hardcode a solution into the script residing on my local machine. Am I doing something wrong or fundamentally misunderstanding something here, or is this a bug for the epoch time printoff for strftime?
I am using:
Pandas version 1.2.0
Mac OS version 10.13.6
Thanks in advance for your help!
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MEMBER
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- [ ] closes #xxxx
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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REF: eliminate inner functions in describe
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MEMBER
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- [ ] xref #36833
- [ ] tests added / passed
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- [ ] whatsnew entry
Remove inner functions in ``describe_ndframe``, while passing the required arguments.
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API: concatting of Series/DataFrame - handling (not skipping) of empty objects
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[] | null | 4
| 2021-01-12T08:36:18Z
| 2021-02-03T14:31:32Z
| null |
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/38843 and https://github.com/pandas-dev/pandas/pull/39035.
Currently, we generally (some exceptions can be considered bugs, I think) do not drop empty objects when concatting DataFrames, but we do explicitly drop empties when concatting Series (in `dtypes/concat.py::concat_compat`, for `axis==0`).
We should make this consistent throughout pandas, and generally I would argue for not skipping empties: when not skipping empty objects, the resulting dtype of a concat-operation only depends on the input *dtypes*, and not on the exact content (the exact values, how many values (shape)). In general we want to get rid of value-dependent behaviour. In the past we discussed this in the context of the certain values (eg presence of NaNs or not), but I think also the shape should not matter (eg when slicing dataframes before concatting, you can get empties or not depending on *values*).
*If* people agree on going the way of not skipping empties in `concat` (and `append`, and friends), some different areas of work:
* **DataFrames**: in general we already do not skip empty objects when determining the resulting dtype (except for DataFrames with EA columns, see below). However:
* [ ] Add a bunch of explicit tests for this behaviour.
* [ ] There are still some bugs, see eg https://github.com/pandas-dev/pandas/issues/32934 about M8[ns] + float64 giving M8[ns] for when empty and object otherwise
* **DataFrame/Series with new (nullable) extension dtypes**: since those are still experimental, I think we can still just make a breaking change here?
* [ ] Update `concat_compat` to not skip empty nullable EAs
* **Series** (and DataFrame with EA columns): those now skip empties. Can we "just" change this (it's long standing behaviour, and changing it can certainly break code (eg if you at once have no longer a datetime column but object dtype column)? Can we deprecate it? (doesn't sound easy) Or leave it as breaking change for pandas 2.0? (which means keeping the inconsistency for a while longer ..)
So IMO it's mainly the last bullet point (Series/DataFrame with longer-existing EAs) that requires some more discussion on *how* we want to change it.
---
Some illustrative examples:
```python
# dataframe with int64 (default consolidated dtype) and period (EA dtype) column
>>> df1 = pd.DataFrame({'a': [1, 2, 3], 'b': pd.period_range("2012", freq="D", periods=3)})
# dataframe with object dtype columns
>>> df2 = pd.DataFrame({'a': ['a', 'b', 'c'], 'b': ['a', 'b', 'c']})
```
For Series with basic dtype (int64), int64 + object dtype results in object dtype, but not when the object dtype Series is empty:
```python
>>> pd.concat([df1['a'], df2['a']]).dtype
dtype('O')
>>> pd.concat([df1['a'], df2['a'][:0]]).dtype
dtype('int64')
```
For DataFrame, you can see that the int64 + object always gives object (even when one is empty), but for period dtype, the empty object dtype gets ignored:
```python
>>> pd.concat([df1, df2]).dtypes
a object
b object
dtype: object
>>> pd.concat([df1, df2[:0]]).dtypes
a object
b period[D]
dtype: object
```
cc @pandas-dev/pandas-core
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MDU6SXNzdWU3ODQwODU5MjQ=
| 39,123
|
Raise an error in read_csv when names and prefix both are not None
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| 0
| 2021-01-12T09:56:31Z
| 2021-05-14T16:05:15Z
| 2021-05-14T16:05:15Z
|
NONE
| null |
#### Problem description
As a result of the discussion in issue #27394, `read_csv` is changed such that it raises an error when both `header` and `prefix` are different from `None`. A user had misunderstood how to (not) use `header` and `prefix` together. I think that the usage of `names`and `prefix` can be misunderstood in a similar way.
It could also be that a user accidentally provides both arguments and expects `prefix` to have effect. Right now, it seems like `prefix` is silently ignored when `names` is provided.
#### Describe the solution you'd like
Raise a ValueError when the `read_csv` arguments `prefix` and `names` both differ from `None`, in accordance with issue #27394 and pull request #31383.
#### API breaking implications
This will "break" code that passes values (!=`None`) for both `prefix` and `names`, but since it was an accepted solution for issue #27394, I think it could be used here as well.
#### Describe alternatives you've considered
Another possibility is to issue a warning instead of an error, but that would be inconsistent with the behavior when `prefix` and `header` is both not `None`.
#### Additional context
Examples below are run in pandas version 1.3.0.dev0+210.g9f1a41dee.
When I run
```python
pandas.read_csv("my_data.csv", prefix="XZ", header=0)
```
I get the following output:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 605, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 457, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 814, in __init__
self._engine = self._make_engine(self.engine)
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 1045, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 1853, in __init__
ParserBase.__init__(self, kwds)
File "/home/malin/workspace/py/pandas/pandas/io/parsers.py", line 1334, in __init__
raise ValueError(
ValueError: Argument prefix must be None if argument header is not None
```
but running
```python
pandas.read_csv("my_data.csv", prefix="XZ", names=["a", "b", "c"])
```
works fine, except that `prefix` is silently ignored.
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DOC: fixed a minor mistake worksheet -> workbook
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| 1
| 2021-01-12T10:12:41Z
| 2021-01-12T13:52:02Z
| 2021-01-12T13:51:58Z
|
CONTRIBUTOR
| null |
A small typo related to #38990
A worksheet can't have multiple worksheets, but a workbook can. Minor fix but may confuse the newcomers
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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January 2021 Developer Meeting
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| 2021-01-12T10:48:13Z
| 2021-01-14T14:22:36Z
| 2021-01-14T14:22:36Z
|
MEMBER
| null |
The monthly dev meeting is Wednesday January 13th, at 18:00 UTC (12 am Central). Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248?pwd=T2l2Qi9vaC82Z294ZEtFczYxMVM2dz09
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda. All are welcome to attend.
@pandas-dev/pandas-core @pandas-dev/pandas-triage
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| 3
| 2021-01-12T14:02:53Z
| 2021-08-25T14:11:35Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df_dict_list = [{"word": 1, "value": 0}, {"word": "knowledg", "value": 2}]
df = pd.DataFrame(df_dict_list)
df.plot.barh(x="word", legend=None)
```
(executed as part of a notebook, but this should not matter)
#### Problem description
The code works in Pandas 1.1.5, but not in 1.2. I suppose that this is not intentional. Additionally, the bug seems to occur only when the first value is an integer and a following value cannot be parsed to an int. This probably goes rather deep into the library, so I cannot say whether barh is the actual problem or whether it lies somewhere else.
#### Stack trace
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/core/indexes/base.py in astype(self, dtype, copy)
705 try:
--> 706 casted = self._values.astype(dtype, copy=copy)
707 except (TypeError, ValueError) as err:
ValueError: invalid literal for int() with base 10: 'knowledg'
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-3-3b2bde8f17ab> in <module>
----> 1 df.plot.barh(x="word", legend=None)
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/plotting/_core.py in barh(self, x, y, **kwargs)
1197 other axis represents a measured value.
1198 """
-> 1199 return self(kind="barh", x=x, y=y, **kwargs)
1200
1201 def box(self, by=None, **kwargs):
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
953 data.columns = label_name
954
--> 955 return plot_backend.plot(data, kind=kind, **kwargs)
956
957 __call__.__doc__ = __doc__
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/plotting/_matplotlib/__init__.py in plot(data, kind, **kwargs)
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61 plot_obj.generate()
62 plot_obj.draw()
63 return plot_obj.result
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py in generate(self)
278 self._compute_plot_data()
279 self._setup_subplots()
--> 280 self._make_plot()
281 self._add_table()
282 self._make_legend()
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py in _make_plot(self)
1435 elif self.orientation == "horizontal":
1436 ax.yaxis.update_units(self.ax_index)
-> 1437 self.tick_pos = ax.convert_yunits(self.ax_index).astype(np.int)
1438 self.ax_pos = self.tick_pos - self.tickoffset
1439
~/.conda/envs/BibTex/lib/python3.8/site-packages/pandas/core/indexes/base.py in astype(self, dtype, copy)
706 casted = self._values.astype(dtype, copy=copy)
707 except (TypeError, ValueError) as err:
--> 708 raise TypeError(
709 f"Cannot cast {type(self).__name__} to dtype {dtype}"
710 ) from err
TypeError: Cannot cast Index to dtype int64
```
</details>
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MDExOlB1bGxSZXF1ZXN0NTUzNTY5NDE2
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CLN: remove redundant consolidation
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| 0
| 2021-01-12T15:58:47Z
| 2021-01-13T15:35:15Z
| 2021-01-13T13:21:12Z
|
MEMBER
| null |
We only get here with nblocks == 1
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QST:
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| 2021-01-12T17:32:21Z
| 2021-01-31T13:48:57Z
| 2021-01-31T13:48:57Z
|
NONE
| null |
Hi,
I am new to pandas. I was facing issue with openpyxl in append mode
```
import pandas as pd
col = ['Expiry','pnl']
df = pd.DataFrame([], columns=col)
writer = pd.ExcelWriter('asd.xlsx', engine='openpyxl', mode='a')
df.to_excel(writer, "Sheet1")
writer.save()
writer.close()
```
It runs successfully but when I open excel file it says
**We found a problem with some content. Do you want us to try to recover?**
I have searched for solution in internet. But nothing was working. Could you please help me?
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**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: getting started under coming from sql
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| 2021-01-12T18:59:46Z
| 2021-01-12T19:08:25Z
| 2021-01-12T19:06:59Z
|
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"]
**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: "getting started" under "coming from SQL" assumes you're using a notebook
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| 2021-08-30T07:06:19Z
| null |
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html
#### Documentation problem
I'm getting started with Pandas. I went to the docs site and found a section called "getting started" which sounded good to me. It then had a few boxes for where you're coming from and one of them was "coming from SQL" which also sounded good for me. I'm a back end dev. I was able to make a Python script (I'm using VS Code) that imported the library and ran the commands, but I saw no output. The docs showed output from `tips.head()`, but I saw no output from that line when I ran my script.
Googling my issue, I found this: https://stackoverflow.com/questions/51580863/output-of-pandas-head-function-is-not-displayed
The accepted answer suggested running `print(df.head())` instead of just `df.head()`. That worked for me. After reading that Stack Overflow page, I remembered that people also often use pandas with notebooks, so I went back to the docs page and looked at it closely. At that point, I noticed the `In [5]:` near the steps. I think people who've never used notebooks at all before might not even recognize this.
#### Suggested fix for documentation
- Remove the requirement to run the getting started code in a notebook, or
- Include alternative line for those running the script as is without viz tools like notebooks, or
- Add warning at top of getting started guide that readers are required to setup a notebook and run the code in it for their experience to match that of the guide
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MDU6SXNzdWU3ODQ1NTMxODY=
| 39,131
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BUG: pd.Series([1]).isin(pd.Series(['1'])) != pd.Series(['1']).isin(pd.Series([1]))
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| 2
| 2021-01-12T20:01:48Z
| 2021-01-19T11:07:37Z
| 2021-01-19T11:07:12Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.Series([1,2,3]).isin(pd.Series(['1','2','3'])) # returns: True, True, True
pd.Series(['1','2','3']).isin(pd.Series([1,2,3])) # returns: False, False, False
```
#### Problem description
The .isin() method is inconsistent when asked to compare numerical strings to ints, such that `(pd.Series([1,2,3]).isin(pd.Series(['1','2','3']))) != (pd.Series(['1','2','3']).isin(pd.Series([1,2,3])))`
There should be consistent handling of these cases.
This bug has been touched upon in issues #16938 and #24918 , but this report is distinct in identifying a smaller subset of the problem that should be solvable in a way that those issues have not been.
#### Expected Output
Two options
- For backwards compatibility, I would suggest that pandas now needs to offer `.isin()` matching between str and int, as some users will have used this in their code.
- Alternatively isin could raise an error if the dtypes input are not the same. This would be easier to achieve after `pd.NA` is fully integrated and locked down in pandas (Why? Because people will not like it if they can't compare superficially equivalent columns because the existance of an NA value has changed the dytpe of a column, as in pd<v1.0.0)
^^ Mainly we need to take a call on the desired behaviour for `isin`; the current inconsistency is not valid behaviour but it not clear what behaviour we should have instead.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Nov 10 00:07:31 PST 2020; root:xnu-4903.278.51~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.2.0
Cython : None
pytest : 3.10.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.6.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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PERF: cythonize kendall correlation
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| 5
| 2021-01-12T20:39:16Z
| 2021-01-23T20:26:53Z
| 2021-01-20T21:08:10Z
|
MEMBER
| null |
- [x] closes #28329
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
ASV's.
before after ratio
[a6bc6ecc] [1eb86441]
<perf-cythonize-kendall~2^2> <perf-cythonize-kendall>
- 81M 57.6M 0.71 stat_ops.Correlation.peakmem_corr_wide('kendall')
- 1.41±0.02s 362±7ms 0.26 stat_ops.Correlation.time_corr_wide_nans('kendall')
- 1.57±0.04s 398±4ms 0.25 stat_ops.Correlation.time_corr_wide('kendall')
- 34.6±4ms 8.75±0.06ms 0.25 stat_ops.Correlation.time_corr('kendall')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
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ENH: Support ExtensionArray (and masked EAs speficially) in indexing
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| 2021-01-12T21:00:38Z
| 2021-11-16T12:45:29Z
| null |
MEMBER
| null |
One area where the general ExtensionArray support is lacking is to store them in the index (right now they get converted to ndarray when storing in an `Index`), and have efficient indexing operations (hashtable, index engines).
Several of our long-time extension dtypes have their own subclass (Categorical, Period, Datetime, IntervalIndex), but we need to solve this *generally* for ExtensionArrays (so it can also work for external EAs), and should also focus on solving it well for the new nullable ExtensionArrays (using masked arrays).
I think there are multiple aspects to this (probably more, but currently thinking of those):
### 1) Storing ExtensionArrays in an Index object
Supporting to "just" store EAs in the Index and support its methods (and eg falling back to ndarray for the indexing engine) is probably not that hard. There are PRs https://github.com/pandas-dev/pandas/pull/34159 (storing EAs in base Index class) and https://github.com/pandas-dev/pandas/pull/37869 (having specific ExtensionIndex subclass).
I think both approaches are *technically* not that different (put the required special cases in `if` blocks in the base class vs in overridden methods in the subclass), but for me it's mainly a **user API** design discussion (summarized as "I don't think that end users should see an "ExtensionIndex").
So for this part, we should have that API discussion.
### 2) A protocol for specifying the values (ndarray) used for indexing operations
While for an initial version of support, we can use `np.asarray(EA)` as the values passed to the `IndexEngine`, we should ideally have a general method in the EA interface to be able to specify which values can be used for indexing.
There is some discussion related to this in https://github.com/pandas-dev/pandas/issues/32586 and https://github.com/pandas-dev/pandas/issues/33276 (eg can we re-use some of the existing `_values_for_..` methods? ...). And we can probably continue this aspect over there.
A general method is mostly important for external EAs, because we will probably have special support for our own EAs: the existing Index subclasses already do this, and for the nullable EAs we need to add this (see next section below).
### 3) Support for masked arrays in the indexing operations (IndexEngine, HashTable, etc)
Specifically to have better support for the nullable dtypes (without needing to convert to ndarray), I think we should look into adding support for using masks in the low-level index operations (IndexEngine, HashTable, etc).
Some (not-index related) hashtable methods like `HashTable.unique` already have optional support for masks.
I think this is technically the most challenging item, and needs to be worked out more in detail what this work item would entail.
cc @jbrockmendel @TomAugspurger @jreback
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BUG: Close resources opened by pyxlsb
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| 1
| 2021-01-12T22:01:14Z
| 2021-01-13T15:20:28Z
| 2021-01-13T13:22:03Z
|
CONTRIBUTOR
| null |
Spin-off from #39047:
- Close resources opened by pyxlsb
- Use context manager when opening non-fsspec URLs (I'm not sure whether that reduced any `ResourceWarning`s but it can't hurt)
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ENH: Add support for `date_unit` to be specified per column in `to_json`
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[] | null | 2
| 2021-01-12T22:14:27Z
| 2021-01-19T09:13:41Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I would want to encode my dataframe consisting of `datetime64` & `timedelta64` columns with different units of precision per column in `to_json`.
#### Describe the solution you'd like
Currently in `to_json`, the `date_unit` param achieves this for the entire dataframe, but I would like to encode each column with different precision. Hence a `dict-like` support for `date_unit` would be good to have.
#### Describe alternatives you've considered
None
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BUG: setting categorical values into object dtype DataFrame
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| 0
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| 2021-01-14T21:54:22Z
| 2021-01-14T20:48:17Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
```
df = pd.DataFrame({"A": range(3)}, dtype=object)
cat = pd.Categorical(["alpha", "beta", "gamma"])
df.iloc[range(3), 0] = cat
>>> df
A
0 NaN
1
2
```
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ASV: Add xlrd engine for xls file
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| 2
| 2021-01-12T22:44:37Z
| 2021-01-13T16:55:12Z
| 2021-01-13T13:41:20Z
|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Is one parametrized function more efficient than multiple functions during setup and teardown? If not I would make a separate function for the xls benchmark
cc @jorisvandenbossche
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BUG: Unable to write to hdf when format='table' and list-like values are present in a column
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| 1
| 2021-01-13T00:06:48Z
| 2021-01-13T00:43:14Z
| 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
In[43]: df = pd.DataFrame({'a':[[1, 2, 3]]})
In[44]: df
Out[44]:
a
0 [1, 2, 3]
In[45]: df.to_hdf('sample.hdf', 'test')
/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/core/generic.py:2449: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->Index(['a'], dtype='object')]
encoding=encoding,
In[46]: df.to_hdf('sample.hdf', 'test')
/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/core/generic.py:2449: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->Index(['a'], dtype='object')]
encoding=encoding,
In[47]: df.to_hdf('sample.hdf', 'test', format='table')
Traceback (most recent call last):
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3418, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-47-13d7ad5be031>", line 1, in <module>
df.to_hdf('sample.hdf', 'test', format='table')
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/core/generic.py", line 2449, in to_hdf
encoding=encoding,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 270, in to_hdf
f(store)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 262, in <lambda>
encoding=encoding,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 1129, in put
track_times=track_times,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 1801, in _write_to_group
track_times=track_times,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 4238, in write
data_columns=data_columns,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 3907, in _create_axes
errors=self.errors,
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/pytables.py", line 4896, in _maybe_convert_for_string_atom
for i in range(len(block.shape[0])):
TypeError: object of type 'int' has no len()
```
#### Problem description
My dataframe has list-like values and when we pass `format=table`, I'm not able to write the dataframe to hdf.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-36-generic
Version : #40~20.04.1-Ubuntu SMP Wed Jan 6 10:15:55 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.0
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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BUG: Placeholders not being filled on docstrings
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| 20
| 2021-01-13T02:29:00Z
| 2021-01-17T16:00:43Z
| 2021-01-17T15:59:40Z
|
CONTRIBUTOR
| null |
- [x] closes #39115
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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| 1
| 2021-01-13T03:32:56Z
| 2021-01-14T20:48:36Z
| 2021-01-14T20:48:36Z
|
MEMBER
| null |
```
s = pd.Series([1, 2])
df = pd.DataFrame([1, 2])
# Reducers: list-like
ops = np.array([np.sum, np.mean])
print(df.agg(ops))
print(s.agg(ops))
print(df.apply(ops))
print(s.apply(ops)) # Raises
# Reducers: dict-like
ops = pd.Series({0: np.mean})
print(df.agg(ops)) # Raises
print(s.agg(ops)) # Raises
print(df.apply(ops)) # Raises
print(s.apply(ops)) # Raises
# Transforms - list-like
ops = np.array([np.abs, np.sqrt])
print(df.transform(ops))
print(df.apply(ops))
print(s.transform(ops))
print(s.apply(ops)) # Raises
# Transforms - dict-like
ops = pd.Series({0: np.abs})
print(df.transform(ops))
print(s.transform(ops))
print(s.apply(ops)) # Raises
print(df.apply(ops)) # Raises
```
All lines that don't raise give the expected output.
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ENH: Allow Series.apply to accept list-like and dict-like
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| 3
| 2021-01-13T04:30:13Z
| 2021-01-15T01:27:53Z
| 2021-01-14T20:48:36Z
|
MEMBER
| null |
- [x] closes #39140
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I only added the Series.apply list/dict-like addition to the whatsnew. For both Series and DataFrame .agg, this PR also enables passing a Series as a dict-like, but other dict-likes already work. It didn't seem notable to add this to the whatsnew.
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MDU6SXNzdWU3ODQ3ODcyNDQ=
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BUG: .dt.isocalendar().week results in the last day of the year as week 1
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| 2021-01-13T04:55:33Z
| 2021-01-13T18:23:56Z
| 2021-01-13T18:23:56Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
## Issue 1
```python
import pandas as pd
# sample dataframe
d = pd.DataFrame({'v': [pd.Timestamp('2018-12-03 00:00:00'), pd.Timestamp('2018-12-10 00:00:00'), pd.Timestamp('2018-12-17 00:00:00'), pd.Timestamp('2018-12-24 00:00:00'), pd.Timestamp('2018-12-31 00:00:00')]})
# display(d)
v
0 2018-12-03
1 2018-12-10
2 2018-12-17
3 2018-12-24
4 2018-12-31
# get the week number
wn = d.v.dt.isocalendar().week
# display(wn)
0 49
1 50
2 51
3 52
4 1
Name: week, dtype: UInt32
```
#### Problem description
- The last day of the week should not be 1, for a given year.
- `d.v.dt.week` also generates the incorrect output, with a 1
- This is an issue for a number of reasons, but in terms of plotting with week number on the x-axis, the plot line goes from 52, back to 1.
#### Expected Output
- `d.v.dt.strftime('%W').astype(int)` will generate the expected output
```python
0 49
1 50
2 51
3 52
4 53
Name: week, dtype: UInt32
```
## Issue 2:
```python
import pandas as pd
# sample data
data = {'date': pd.bdate_range('2020-11-01', freq='D', periods=93)}
df = pd.DataFrame(data)
# get week number
df['week_strf'] = df.date.dt.strftime('%W').astype('int')
df['week_isocal'] = df.date.dt.isocalendar().week
# display(df.head(10))
date week_strf week_isocal
0 2020-11-01 43 44
1 2020-11-02 44 45
2 2020-11-03 44 45
3 2020-11-04 44 45
4 2020-11-05 44 45
5 2020-11-06 44 45
6 2020-11-07 44 45
7 2020-11-08 44 45
8 2020-11-09 45 46
9 2020-11-10 45 46
```
#### Problem description
- The two methods for getting the week number do not produce the same result
#### Expected Output
- `week_strf` and `week_isocal` should have the same week number
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.19.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210110
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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QST:Why DataFrame.pivot() will change the order of the rows of DataFrame?
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| 11
| 2021-01-13T05:02:55Z
| 2021-04-20T23:23:08Z
| 2021-04-20T23:23:08Z
|
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
**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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BUG: dataframe concatenation segementation fault (core dump)
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[] | null | 13
| 2021-01-13T05:58:43Z
| 2021-03-04T17:55:26Z
| 2021-03-04T17:55:26Z
|
NONE
| null |
- [ X ] I have checked that this issue has not already been reported.
- [ X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.concat(
(
pd.read_csv(os.path.join(dirpath, filename))
for dirpath, filename in file_generator
),
ignore_index=True,
)
```
#### Problem description
A subgroup of my files can cause the above function to break. And it happens 100% with those files with pandas1.2.0, with 1.1.5 no problem.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.16-200.fc33.x86_64
Version : #1 SMP Mon Dec 21 14:08:22 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.1.3
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
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Backport PR #38982 on branch 1.2.x (REGR: Bug fix for ExtensionArray groupby aggregation on non-numeric types)
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Backport PR #38982: REGR: Bug fix for ExtensionArray groupby aggregation on non-numeric types
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Refactor - ArrayManager overview issue
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MEMBER
| null |
Initial proof of concept for a non-consolidating "ArrayManager" (storing the columns of a DataFrame as a list of 1D arrays) is merged in https://github.com/pandas-dev/pandas/pull/36010.
This issue is meant to track the required follow-up work items to get this to a more feature-complete implementation.
- **Functionality**: get all tests passing
- There are big chunks of tests failing because some larger sets of functionality is not yet implemented or relying on BlockManager internals (this last aspect is also covered in https://github.com/pandas-dev/pandas/issues/34669). Those bigger topics are:
- [x] `quantile` / `describe` related (ArrayManager.quantile is not yet implemented) -> https://github.com/pandas-dev/pandas/pull/40189
- [x] `equals` related (ArrayManager.equals is not yet implemented) -> #39721
- [x] `groupby` related tests (there are still a few parts of groupby that directly uses the blocks) -> #39885, #40050
- [x] `concat` related (`internals/concat.py` only deals with the simple case when no reindexing is needed for ArrayManager at the moment, the full functionality (similarly to what `concatenate_block_managers` / the `JoinUnits` now cover) still needs to be implemented) -> https://github.com/pandas-dev/pandas/pull/39612
- [ ] indexing related (some of the ArrayManager methods like `setitem`, `iset`, `insert` are not yet fully implementated for all corner cases + get indexing tests passing)
- [ ] IO related:
- [x] JSON (https://github.com/pandas-dev/pandas/issues/27164, fixed in #41809
- [ ] pytables code still relies on block internals
- In addition, the ArrayManager currently also uses an "apply_with_block" fallback for things that are right now implemented directly on the Block classes. Long term, all those cases should also be refactored so that the core functionality of the specific function can be shared between ArrayManager and BlockManager, without directly relying on the Block classes.
- [ ] Some examples of this: `replace`, `where`, `interpolate`, `shift`, `diff`, `downcast`, `putmask`, ... (those could all be refactored one at a time).
- There will also be tests that either are 1) BlockManager specific (using block internals to test) or 2) testing behaviour specific to DataFrames with BlockManager (we will probably want to change some aspects about eg copy/view, setitem-like ops, etc. Those changes all need to discussed separately of course, but might also require some skipped tests initially).
Such tests can be skipped with eg ``@td.skip_array_manager_invalid_test``.
- **Design** questions:
- [ ] What to do with Series, which now is a SingleBlockManager inheriting from BlockManager (should we also have a "SingleArrayManager"?) -> #40152
- [ ] ... (probably more items will come up) ...
- **Performance**
- Currently, I didn't yet look at performance (I only ran a few of the ASV benchmarks, see top post of https://github.com/pandas-dev/pandas/pull/36010). I also think that we should first focus on getting a larger part of the functionality working (which will also make it easier to run benchmarks), but afterwards we will need to identify the different areas where performance improvements are needed.
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REGR: Assigning multiple new columns with loc fails when index is a MultiIndex
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| 1
| 2021-01-13T14:52:49Z
| 2021-01-15T14:14:18Z
| 2021-01-15T14:14:18Z
|
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
rows = [(1,2), (3,4)]
initial_cols = ["a", "b"]
df = pd.DataFrame(42, index=pd.MultiIndex.from_tuples(rows), columns=initial_cols)
new_cols = ["c", "d"]
df.loc[:, new_cols] = None
```
#### Problem description
When running the above code in pandas 1.1.5, you will get the following error:
```
File "foo.py", line 11, in <module>
df.loc[:, new_cols] = None
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 666, in __setitem__
indexer = self._get_setitem_indexer(key)
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 609, in _get_setitem_indexer
return self._convert_tuple(key, is_setter=True)
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 734, in _convert_tuple
idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 1198, in _convert_to_indexer
return self._get_listlike_indexer(key, axis, raise_missing=True)[1]
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/home/guido/anaconda3/envs/pandas_115_bug/lib/python3.7/site-packages/pandas/core/indexing.py", line 1298, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['c', 'd'], dtype='object')] are in the [columns]"
```
Till version 1.1.4, the above code would result in the dataframe being extended with the two new columns c and d. With the release of pandas 1.1.5, the code results in the above error. It might be related to the fix for #37711
Single column assignments do still work, i.e. replacing the single assignment line with the following two separate assignment lines does work:
```python
df.loc[:, "c"] = None
df.loc[:, "d"] = None
```
The problem also only occurs whenever the index is a multiindex. If the index is not a multiindex, the error will not occur.
#### Expected Output
Expected output is that no error occurs and that the two columns c and d are added with values None for all rows.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-33-generic
Version : #36-Ubuntu SMP Wed Dec 9 09:14:40 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
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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BUG: Cells containing "NA" (string) in excel is imported as NaN
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| 2021-01-13T15:40:18Z
| 2021-01-15T19:13:02Z
| 2021-01-15T19:13:01Z
|
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
test_dict = {
"TestInput": ["NA", "na", "N.A."],
"TestCol2": [1234, "1234", ""]
}
test_df = pd.DataFrame(test_dict)
print(test_df)
file_path = "./test-input.xlsx"
test_df.to_excel(file_path, index=False)
input_df = pd.read_excel(file_path, dtype="object")
print(input_df)
```
#### Problem description
The string "NA" is imported as NaN instead of a string.
During DataFrame creation, the top-left cell contains the string "NA". However, when the same DataFrame was imported from excel, it is imported as NaN, as if the cell was blank.
Screenshot of output from jupyter below:
<img width="526" alt="Screenshot 2021-01-13 at 11 36 59 PM" src="https://user-images.githubusercontent.com/35208548/104473723-40106a00-55f8-11eb-8b1e-958ae39a675d.png">
#### Expected Output
The imported DataFrame should be identical to the DataFrame that was initially created with "NA" imported a string and not NaN.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:40:21 PST 2020; root:xnu-7195.60.75~1/RELEASE_ARM64_T8101
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 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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REF: implement is_exact_match
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| 1
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| 2021-01-14T15:33:08Z
| 2021-01-14T14:23:03Z
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MEMBER
| null |
Working to smooth out some inconsistencies in setitem between code paths.
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DOC: 1.3 release notes
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|
BUG: comparison of pandas NaT with datetime.date evaluating to True
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| 6
| 2021-01-13T17:21:11Z
| 2021-01-16T10:04:01Z
| 2021-01-16T01:18:12Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas
import datetime
pandas.NaT < datetime.datetime.now().date()
```
#### Problem description
I understood the convention is for NA-types to evaluate to False in all inequalities. The above code evaluates to True.
These DO evaluate to False, as expected.
`pandas.NaT < datetime.datetime.now()`
`pandas.NaT > datetime.datetime.now().date()`
This seems to have been introduced with 1.2.0, and to not have been the behaviour under 1.1.5.
#### Output of ``pd.show_versions()``
```
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
```
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CLN remove 'import pandas as pd' from pandas/core/generic.py
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| 2
| 2021-01-13T17:55:04Z
| 2021-01-31T13:28:25Z
| 2021-01-14T19:18:37Z
|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
---
will do the rest (excluding `pandas/tests`) and add a check if this is the kind of thing we want
|
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BUG: Minor fix setitem_cache_updating tests
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| 1
| 2021-01-13T20:16:26Z
| 2021-01-14T15:56:19Z
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|
CONTRIBUTOR
| null |
BUG: tiny fix in tests, i guess from rapid development ;) -> https://github.com/pandas-dev/pandas/issues/5424#issuecomment-27658793
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|
ENH: Dedicated method for creating conditional columns
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[] | null | 5
| 2021-01-13T21:31:09Z
| 2021-10-09T12:34:37Z
| null |
CONTRIBUTOR
| null |
Creating conditional columns is an operation you perform a lot when wrangling data. Still right now there is no dedicated and explicit way of creating conditional columns in pandas. This can be seen on [StackOverflow](https://stackoverflow.com/questions/19913659/pandas-conditional-creation-of-a-series-dataframe-column) which is my top hit when I google "pandas conditional column" (this will obviously vary per person).
On this StackOverflow link we see the result of not being explicit since there are at least 6 different methods mentioned and a lot of anti patterns, also methods we do not want users to use because of inefficiency (`apply` with if else, `map` with `lambda x` for example).
This ticket is made as a starting point for discussion if we should have a more explicit way of creating conditional columns in pandas. Also because we need to look ahead when switching to EA's because the most used methods (and top accepted answer on SO) are numpy methods and return numpy arrays.
---
### What do we have now?:
For single conditional columns:
- `np.where`
- `.loc` with assignment
- `Series.where`
- `Series.mask`, see [comment](https://github.com/pandas-dev/pandas/issues/39154#issuecomment-760638244)
For multi conditional columns:
- `np.select`
- complex list comprehensions
- `.apply` with if else (not an option imo)
---
I don't have a suggestion yet, but I know I really like the clean code you can create with `np.select` and also with SQL CASE WHEN.
### Other languages:
SQL has CASE WHEN:
```sql
CASE
WHEN condition1 THEN result1
WHEN condition2 THEN result2
WHEN conditionN THEN resultN
ELSE result
END;
```
R (dplyr) has case_when:
```r
df %>% mutate(g = case_when(a == 2 | a == 5 | a == 7 | (a == 1 & b == 4) ~ 2,
a == 0 | a == 1 | a == 4 | a == 3 | c == 4 ~ 3,
TRUE ~ NA_real_))
```
---
### Related tickets / questions:
- #30357 (using apply with if else)
- #34543 (conditional join)
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CI: Unreliable Tests write to S3
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CONTRIBUTOR
| null |
Seen this in a few PRs:
https://github.com/pandas-dev/pandas/pull/39153/checks?check_run_id=1697646268
```
________________________ TestS3.test_write_s3_csv_fails ________________________
[gw1] linux -- Python 3.7.9 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.tests.io.parser.test_network.TestS3 object at 0x7feea42e2610>
tips_df = total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 ....75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
[244 rows x 7 columns]
s3so = {'client_kwargs': {'endpoint_url': 'http://127.0.0.1:5551/'}}
def test_write_s3_csv_fails(self, tips_df, s3so):
# GH 32486
# Attempting to write to an invalid S3 path should raise
import botocore
# GH 34087
# https://boto3.amazonaws.com/v1/documentation/api/latest/guide/error-handling.html
# Catch a ClientError since AWS Service Errors are defined dynamically
error = (FileNotFoundError, botocore.exceptions.ClientError)
with pytest.raises(error, match="The specified bucket does not exist"):
tips_df.to_csv(
> "s3://an_s3_bucket_data_doesnt_exit/not_real.csv", storage_options=s3so
)
E Failed: DID NOT RAISE (<class 'FileNotFoundError'>, <class 'botocore.exceptions.ClientError'>)
pandas/tests/io/parser/test_network.py:224: Failed
______________________ TestS3.test_write_s3_parquet_fails ______________________
[gw1] linux -- Python 3.7.9 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.tests.io.parser.test_network.TestS3 object at 0x7feea435c790>
tips_df = total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 ....75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
[244 rows x 7 columns]
s3so = {'client_kwargs': {'endpoint_url': 'http://127.0.0.1:5551/'}}
@td.skip_if_no("pyarrow")
def test_write_s3_parquet_fails(self, tips_df, s3so):
# GH 27679
# Attempting to write to an invalid S3 path should raise
import botocore
# GH 34087
# https://boto3.amazonaws.com/v1/documentation/api/latest/guide/error-handling.html
# Catch a ClientError since AWS Service Errors are defined dynamically
error = (FileNotFoundError, botocore.exceptions.ClientError)
with pytest.raises(error, match="The specified bucket does not exist"):
tips_df.to_parquet(
"s3://an_s3_bucket_data_doesnt_exit/not_real.parquet",
> storage_options=s3so,
)
E Failed: DID NOT RAISE (<class 'FileNotFoundError'>, <class 'botocore.exceptions.ClientError'>)
```
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CI: Mark network test as xfail
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| 4
| 2021-01-13T22:52:12Z
| 2021-01-13T23:41:29Z
| 2021-01-13T23:38:47Z
|
MEMBER
| null |
- [x] xref #39155
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
https://github.com/pandas-dev/pandas/issues/39155#issuecomment-759783358
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Backport PR #39156 on branch 1.2.x (CI: Mark network test as xfail)
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NONE
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Backport PR #39156: CI: Mark network test as xfail
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Minor typo on warnings
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| 12
| 2021-01-13T23:50:07Z
| 2021-08-25T17:55:55Z
| 2021-08-25T17:55:55Z
|
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/blob/25110a92b291de6688c3accad4c34d84837445e8/pandas/_libs/parsers.pyx#L1993-L1996
`"Columns ({warning_names}) have mixed types.Specify dtype option on import or set low_memory=False."`
Note the lack of a space after the period on `types.Specify`. That's because there's no comma separating the strings inside the list, so they get appended while still inside the list, and get passed as a single string to `" ".join(...)`, thus it has nothing to join.
Simply adding the missing comma should fix it.
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ERR: Fix missing space in warning message
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| 5
| 2021-01-13T23:51:03Z
| 2021-05-26T02:20:12Z
| 2021-05-26T02:20:11Z
|
CONTRIBUTOR
| null |
- [x] closes #39158
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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Backport PR #39029: BUG: read_csv does not close file during an error in _make_reader
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| 1
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| 2021-01-19T15:54:13Z
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|
CONTRIBUTOR
| null |
Backport PR #39029
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MDExOlB1bGxSZXF1ZXN0NTU0NTU3MDA1
| 39,161
|
Fix regression in loc setitem raising KeyError when enlarging df with multiindex
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| 2
| 2021-01-14T00:03:38Z
| 2021-01-15T16:04:31Z
| 2021-01-15T14:14:18Z
|
MEMBER
| null |
- [x] closes #39147
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
The previous fix only viewed row indexers not column indexers
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| 785,550,073
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MDU6SXNzdWU3ODU1NTAwNzM=
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BUG: Calling tz_localize suppresses ValueError when converting from NaT to np.int64
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| 4
| 2021-01-14T00:16:33Z
| 2021-08-15T03:09:22Z
| 2021-08-15T03:09:21Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
ser = pd.Series([pd.NaT], dtype="datetime64[ns]")
ser = ser.dt.tz_localize("America/Los_Angeles")
print(ser.astype(np.int64)[0])
```
#### Problem description
The code snippet above prints `-9223372036854775808` (-2<sup>63</sup>) instead of raising a `ValueError`, which is the behavior that would occur if `tz_localize` were not called.
#### Expected Output
Should raise a `ValueError: Cannot convert NaT values to integer`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-36-generic
Version : #40~20.04.1-Ubuntu SMP Wed Jan 6 10:15:55 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 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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MDExOlB1bGxSZXF1ZXN0NTU0NjA2OTIw
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|
API/BUG: always try to operate inplace when setting with loc/iloc[foo, bar]
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| 16
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| 2021-03-08T22:15:38Z
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|
MEMBER
| null |
- [x] closes #38896
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
(The PR title should have a caveat "in cases that make it to `BlockManager.setitem`)
Discussed briefly on today's call. The main idea, as discussed in #38896 and #33457, is that `df[col] = ser` should _not_ alter the existing `df[col]._values`, while `df.iloc[:, num] = ser` should _always_ try to operate inplace before falling back to casting. This PR focuses on the latter case.
ATM, restricting to cases where we a) get to Block.setitem, b) _could_ do the setitem inplace, and c) are setting all the entries in the array, we have 4 different behaviors. Examples are posted at the bottom of this post.
This PR changes Block.setitem so that in the _can_hold_element case we _always_ operate inplace, always retain the same underlying array.
------
Existing Behavior
1) If the `new_values` being set are categorical, we overwrite existing values and then discard them, do _not_ get a view on new_values. (only can replicate with Series until #39136)
```
ser = pd.Series(np.arange(5), dtype=object)
cat = pd.Categorical(ser)[::-1]
vals = ser.values
ser.iloc[ser.index] = cat
assert (vals == cat).all() # <-- vals are overwritten
assert ser.dtype == cat.dtype # < -- vals are discarded
assert not np.shares_memory(ser._values.codes, cat.codes) # <-- we also dont get a view on the new values
```
2) If the new_values are any other EA, we do _not_ overwrite existing values and _do_ get a view on the new_values.
```
df = pd.DataFrame({"A": np.arange(5, dtype=np.int32)})
arr = pd.array(df["A"].values) + 1
vals = df.values
df.iloc[df.index, 0] = arr
assert (df.dtypes == arr.dtype).all() # <-- cast
assert not (vals == df).any(axis=None) # <-- did not overwrite original
```
3) If the new_values are a new non-EA dtype, we overwrite the old values and create a new array, get a view on neither.
```
df = tm.makeDataFrame() # <-- float64
old = df.values
new = np.arange(df.size).astype(np.int16).reshape(df.shape)
df.iloc[:, [0, 1, 2, 3]] = new
assert (old == new).all()
assert not np.shares_memory(df.values, old)
assert not np.shares_memory(df.values, new)
```
4) If the new_values have the same dtype as the existing, we overwrite existing and keep the same array
```
df = tm.makeDataFrame() # <-- float64
old = df.values
new = np.arange(df.size).astype(np.float64).reshape(df.shape)
df.iloc[:, [0, 1, 2, 3]] = new
assert (old == new).all()
assert np.shares_memory(df.values, old)
assert not np.shares_memory(df.values, new)
```
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ENH: add additional extras_require sections for optional dependencies
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[] | null | 0
| 2021-01-14T05:05:13Z
| 2021-01-17T15:24:58Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
It would be nice to have more `extras_require` sections in the `setup.py` for optional dependencies.
#### Describe the solution you'd like
I'd like to see more sections added here:
https://github.com/pandas-dev/pandas/blob/master/setup.py#L743
for optional dependencies; e.g. `pandas[s3]` for Pandas + everything you need to write to S3 (just for the sake of example; this is probably not the right division). I found a similar request here: https://github.com/pandas-dev/pandas/issues/35206. (Sorry if a previous issue exists and I missed it.)
#### API breaking implications
None
#### Describe alternatives you've considered
Pin all desired optional dependencies.
#### Additional context
The idea is that if you're using a tool like `pip-compile`, then currently you have to pin all of these requirements, even if they are only used transitively by Pandas. It would be nice to do say `pandas[s3]` in your `requirements.in` and have `s3fs` get added to the compiled requirements.
Happy to make a PR if folks don't object to the idea.
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REF/TYP: extract function/type args in describe.py
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MEMBER
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- [ ] xref #36833
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Extracted function ``_refine_percentiles`` and typed function arguments in ``pandas/core/describe.py``.
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BUG: .apply with collections
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| 3
| 2021-01-14T07:48:34Z
| 2021-08-15T03:10:17Z
| null |
NONE
| null |
consider following example:
```
pd.DataFrame([[0, 1, 2], [3, 4, 5]]).apply(lambda x: [x] * 2, axis = 1)
[[3, 4, 5], [3, 4, 5]]
[[3, 4, 5], [3, 4, 5]]
```
but if use numpy array to replace list, i.e.
```
pd.DataFrame([[0, 1, 2], [3, 4, 5]]).apply(lambda x: np.asarray([x]) * 2, axis = 1)
[[0, 2, 4]]
[[6, 4, 8]]
```
I can not understand the conclusion use list (first example)
if pandas take element in apply(axis = 1) as pd.Series
And [x] cast it to nest list for example ,
First Row [[0, 1, 2]] * 2 -> [[0, 1, 2], [0, 1, 2]]
Second Row [[3, 4, 5]] * 2 -> [[3, 4, 5], [3, 4, 5]]
should not have above conclusion.
It seems pandas only maintain the last row and apply it to all rows.
And when use replace [x] by {“x”: x},
Have similar problem.
I can understand use numpy collection and pandas collection
inside the elements of DataFrame (avoid use list tuple or dict) may be more safe.
But please explain this conclusion for me.
My pandas version is 1.1.3
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QST: Why is `storage_options` parameter missed in `read_table` method?
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| 2021-01-14T07:56:48Z
| 2021-08-31T13:13:22Z
| 2021-08-31T13:13:22Z
|
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).
---
#### 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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BUG: assert_frame_equal() with check_like=True errors with non-comparable types
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| 3
| 2021-01-14T08:48:04Z
| 2021-01-17T11:24:06Z
| 2021-01-16T14:21:18Z
|
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 pandas as pd
import pandas.testing as pdt
# Note that df.columns contains both str and int
df = pd.DataFrame([[0, 1, 2]], columns=["foo", "bar", 42])
pdt.asset_frame_equal(df, df, check_like=True)
```
#### Problem description
This code raises:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-05cc1ba40d40> in <module>
----> 1 pdt.assert_frame_equal(df, df, check_like=True)
[... skipping hidden 2 frame]
~/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py in sort_values(self, return_indexer, ascending, na_position, key)
4664 # ignore na_position for MultiIndex
4665 if not isinstance(self, ABCMultiIndex):
-> 4666 _as = nargsort(
4667 items=idx, ascending=ascending, na_position=na_position, key=key
4668 )
~/.local/lib/python3.8/site-packages/pandas/core/sorting.py in nargsort(items, kind, ascending, na_position, key, mask)
365
366 if is_extension_array_dtype(items):
--> 367 return items.argsort(ascending=ascending, kind=kind, na_position=na_position)
368 else:
369 items = np.asanyarray(items)
~/.local/lib/python3.8/site-packages/pandas/core/arrays/base.py in argsort(self, ascending, kind, na_position, *args, **kwargs)
584
585 values = self._values_for_argsort()
--> 586 return nargsort(
587 values,
588 kind=kind,
~/.local/lib/python3.8/site-packages/pandas/core/sorting.py in nargsort(items, kind, ascending, na_position, key, mask)
377 non_nans = non_nans[::-1]
378 non_nan_idx = non_nan_idx[::-1]
--> 379 indexer = non_nan_idx[non_nans.argsort(kind=kind)]
380 if not ascending:
381 indexer = indexer[::-1]
TypeError: '<' not supported between instances of 'int' and 'str'
```
The cause is PR #37479, which added the following to `assert_index_equal()`:
```python
# If order doesn't matter then sort the index entries
if not check_order:
left = left.sort_values()
right = right.sort_values()
```
This is code is triggered by `assert_frame_equal(…, check_like=True)`. `.sort_order()` does not work when an index contains non-comparable types, like `str` and `int`.
Detected via iiasa/ixmp#390.
#### Expected output
In pandas < 1.2.0, the last line above returned `True`.
The description of the `check_like` argument is:
https://github.com/pandas-dev/pandas/blob/25110a92b291de6688c3accad4c34d84837445e8/pandas/_testing/asserters.py#L1127-L1130
…i.e. this does not indicate that the columns index may only contain comparable types, so the function should not raise an exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-36-generic
Version : #40-Ubuntu SMP Tue Jan 5 21:54:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 50.3.2
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.3.0
blosc : 1.8.1
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.6.1
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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BUG: `df.agg` call passes different things to a custom function depending on whether a unused kwarg is supplied or not
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| 5
| 2021-01-14T15:28:58Z
| 2021-01-17T20:03:03Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported
* The issue could potentially be similar to that reported in https://github.com/pandas-dev/pandas/issues/33517, but I'm not sure.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
import scipy.stats
def circ_mean(data, dummy_kwarg=0):
# print(data)
return 180/np.pi*scipy.stats.circmean(data*np.pi/180)
def numpy_mean(data, dummy_kwarg=0):
return np.mean(data)
@pd.api.extensions.register_dataframe_accessor("my")
class CircstatsAccessor(object):
def __init__(self, pandas_obj):
self._obj = pandas_obj
def circ_mean(self, axis=0, level=None, **kwargs):
df = self._obj
if axis != 0 or level is not None:
df = df.groupby(axis=axis, level=level)
return df.agg(circ_mean, **kwargs)
def numpy_mean(self, axis=0, level=None, **kwargs):
df = self._obj
if axis != 0 or level is not None:
df = df.groupby(axis=axis, level=level)
return df.agg(numpy_mean, **kwargs)
df = pd.DataFrame(
data={
"col1": [10, 11, 12, 13],
"col2": [20, 21, 22, 23],
},
index=[1, 2, 3, 4]
)
# Compute results with the standard `df.mean` call
# I'd like my custom mean function to do a similar thing
df.mean(level=0, axis=0)
# If I don't pass in any kwargs, `df.my.circ_mean` behaves as expected
# Results approximately match those from `df.mean`
df.my.circ_mean(level=0, axis=0)
# If I pass in a kwarg that is not ever used, `df.my.circ_mean`
# returns unusual results - the returned values in `col1` are
# identical to those in `col2`, whereas they were
# different before
df.my.circ_mean(level=0, axis=0, dummy_kwarg=0)
# If I call `df.my.numpy_mean`, results are identical
# without or without providing the kwarg
df.my.numpy_mean(level=0, axis=0)
df.my.numpy_mean(level=0, axis=0, dummy_kwarg=0)
```
#### Problem description
As discussed in the code comments above, I see a difference in behavior in my `circ_mean` function depending on whether a dummy (un-used) keyword argument is specified. Uncommenting the `print` command in the `circ_mean` function indicates that `df.agg` is passing in different things depending on whether or not this keyword is provided.
I would expect there to be no difference in behavior since this keyword has no effect. Interestingly, I see the expected no difference in behavior if I replace the more complicated circular mean call with a simple `np.mean` call inside my custom function (compare `circ_mean` and `numpy_mean` functions).
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.15.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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REF: extract more functions in pandas/core/describe.py
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| 1
| 2021-01-14T15:35:23Z
| 2021-01-15T10:16:56Z
| 2021-01-14T16:34:39Z
|
MEMBER
| null |
- [ ] xref #36833
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Extracted ``describe_series``, ``describe_frame`` and ``select_column`` in ``pandas/core/describe.py``.
The next time I think I will introduce the classes ``SeriesDescriber`` and ``DataFrameDescriber`` and will start moving the functions into the methods incrementally, if that is OK.
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ENH: Reimplement and undeprecate DataFrame.lookup
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[] | null | 26
| 2021-01-14T15:59:31Z
| 2021-02-17T19:31:00Z
| 2021-01-14T16:21:55Z
|
NONE
|
too heated
|
#### Is your feature request related to a problem?
I seriously get the impression that Pandas is actively being sabotaged. As a case in point, `DataFrame.lookup` was deprecated in v1.2. The problems with this are:
1. The [doc for `DataFrame.lookup`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.lookup.html) says to see itself for an example. Well, there is no example in the doc page. The only example I'm aware of is [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#looking-up-values-by-index-column-labels) instead which is a different page.
2. The [changelog](https://pandas.pydata.org/docs/whatsnew/v1.2.0.html#deprecations) for this deprecation points to [GH18682](https://github.com/pandas-dev/pandas/pull/18682) which looks to be wholly irrelevant to this change. A lot has gone wrong here.
3. There is significant user code that already uses `lookup`. Why break it? If the current implementation of `lookup` is suboptimal, shouldn't it be optimized instead?
4. It is extremely complicated to use `melt` when `lookup` works quite simply. For example, compare [this simple answer using `lookup`](https://stackoverflow.com/a/45487394/) with [this complicated answer using `melt`](https://stackoverflow.com/a/65722008/).
Does nobody review changes, docs, and release notes anymore prior to the release? It looks this way.
#### Describe the solution you'd like
1. Optimize `lookup` if attainable using `melt` or otherwise.
1. Undeprecate `lookup`.
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| 3
| 2021-01-14T16:00:03Z
| 2021-01-22T23:26:52Z
| 2021-01-22T23:26:52Z
|
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).
---
#### Question about pandas
Using the `value_counts` method after grouping an empty DataFrame and selecting a column raises an error.
Example:
```python
>>> pd.DataFrame(columns=["A", "B"]).groupby("A")["B"].value_counts()
```
Error:
```python-traceback
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/username/.virtualenvs/my_project/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 736, in value_counts
codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
File "/Users/username/.virtualenvs/my_project/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 705, in <lambda>
llab = lambda lab, inc: lab[inc]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 0 but corresponding boolean dimension is 1
```
Instead, I would expect that `value_counts` returns an empty series, like when using the `max` method, like the following,
```python
>>> pd.DataFrame(columns=["A", "B"]).groupby("A")["B"].max()
```
which gives this result:
```
Series([], Name: B, dtype: object)
```
If it is normal to have an error when using `value_counts` for an empty `SeriesGroupBy`, why is it so?
Thank you.
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ENH: Undeprecate and reimplement DataFrame.lookup
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NONE
| null |
#### Is your feature request related to a problem?
I seriously get the impression that Pandas is actively being sabotaged. As a case in point, `DataFrame.lookup` was deprecated in v1.2.0. The problems with this are:
1. The [doc for `DataFrame.lookup`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.lookup.html) says to see itself for an example. Well, there is no example in the doc page. The only example I'm aware of is [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#looking-up-values-by-index-column-labels) instead which is a different page.
2. The [changelog](https://pandas.pydata.org/docs/whatsnew/v1.2.0.html#deprecations) for this deprecation points to [GH18682](https://github.com/pandas-dev/pandas/pull/18682) which looks to be wholly irrelevant to this change. A lot has gone wrong here.
3. There is significant user code that already uses `lookup`. Why break it? If the current implementation of `lookup` is suboptimal, shouldn't it be optimized instead?
4. It is extremely complicated to use `melt` when `lookup` works quite simply. For example, compare [this simple answer using `lookup`](https://stackoverflow.com/a/45487394/) with [this complicated answer using `melt`](https://stackoverflow.com/a/65722008/).
Does nobody review changes, docs, and release notes anymore prior to release? It looks this way.
#### Describe the solution you'd like
1. Undeprecate `lookup`.
2. Optimize `lookup` if attainable using `melt`.
|
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CLN,TYP Remove string return annotations
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| 6
| 2021-01-14T16:49:32Z
| 2021-01-16T11:33:14Z
| 2021-01-16T11:15:51Z
|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
this was just done with
```python
parser = argparse.ArgumentParser()
parser.add_argument('paths', nargs='*', type=Path)
args = parser.parse_args()
for path in args.paths:
text = path.read_text()
text = re.sub(r'-> "(\w+)"', '-> \\1', text)
path.write_text(text)
```
There's other to do (which I'll get to in other PRs), but this already gets rid of a big load of them
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CI: use conda incubator in doc build
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| 2021-01-14T17:17:25Z
| 2021-01-25T08:25:37Z
| null |
MEMBER
| null |
#39043 enabled conda incubator for the benchmark build. But I didn't get the doc build to work. I had something like this
```yml
web_and_docs:
name: Web and docs
runs-on: ubuntu-latest
defaults:
run:
shell: bash -l -e {0}
steps:
- name: Checkout
uses: actions/checkout@v1
- name: Cache conda
uses: actions/cache@v2
with:
path: ~/conda_pkgs_dir
key: ${{ runner.os }}-conda-${{ hashFiles('${{ env.ENV_FILE }}') }}
- uses: conda-incubator/setup-miniconda@v2
with:
activate-environment: pandas-dev
channel-priority: strict
environment-file: ${{ env.ENV_FILE }}
use-only-tar-bz2: true
- name: Environment Detail
run: |
conda info
conda list
- name: Build Pandas
run: |
python setup.py build_ext -j 2
python -m pip install -e . --no-build-isolation --no-use-pep517
- name: Build website
run: python web/pandas_web.py web/pandas --target-path=web/build
- name: Build documentation
run: doc/make.py --warnings-are-errors | tee sphinx.log ; exit ${PIPESTATUS[0]}
# This can be removed when the ipython directive fails when there are errors,
# including the `tee sphinx.log` in te previous step (https://github.com/ipython/ipython/issues/11547)
- name: Check ipython directive errors
run: "! grep -B10 \"^<<<-------------------------------------------------------------------------$\" sphinx.log"
......
```
There are no errors in the output of `doc/make.py --warnings-are-errors`, but it exits with code 1. Also, using multiple workers to build the doc doesn't improve the performance.
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| 3
| 2021-01-14T20:48:04Z
| 2021-05-13T17:48:56Z
| 2021-05-13T17:48:56Z
|
CONTRIBUTOR
| null |
several recent PRs are showing a web / docs build failure: https://github.com/pandas-dev/pandas/pull/39136/checks?check_run_id=1704155153
though not sure we are actually seeing this on master.
cc @pandas-dev/pandas-core
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| 2021-01-14T21:44:04Z
| 2021-01-14T22:29:08Z
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MEMBER
| null |
- [x] xref #39176
cc @jreback nbformat should be enoguh I hope.
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| 6
| 2021-01-14T21:44:49Z
| 2021-09-25T22:23:16Z
| 2021-09-25T22:23:16Z
|
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.
---
This bug is happening just in the pandas' version 1.2.0. The indexes are disappearing in the following condition:
- Using MultiIndex;
- Using an empty dataframe;
- GroupBy the empty dataframe with multi indexes;
- Aggregate using the `list` method.
#### Code Sample
Here is a code example:
```python
import pandas as pd
# Create a mock dataframe
df = pd.DataFrame(data=[[1, 2, 1],[1, 2, 0]], columns=['a', 'b', 'c'])
# filter it to be an empty dataframe
df = df[df['c']<0]
# group to be multiIndexes and aggregate the groupby using the list method
df = df.groupby(['a','b']).agg(d=('c', list))
print(df.index)
```
The output of this piece of code is:
```
Index([], dtype='object')
```
and it should be:
```
MultiIndex([], names=['a', 'b'])
```
#### Problem description
This bug is happening only in the pandas' version 1.2.0, I've tried using versions 1.1.4 and 1.1.5, and the bug does not appear.
It is a very specific error that happens just when using MultiIndex, empty DataFrame, groupby, and aggregate using `list` method.
In this case, the indexes names will disappear, and the index will no longer be `MultiIndex` but `Index`.
#### Expected Output
The output should be an empty dataframe multi indexed with the indexes names preserved.
#### Output of ``pd.show_versions()``
Follows the ``pd.show_versions()``
```
python-bits : 64
OS : Linux
OS-release : 5.4.0-53-generic
Version : #59-Ubuntu SMP Wed Oct 21 09:38:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.2.0
numpy : 1.18.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0
Cython : 0.29.14
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
```
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Backport PR #39177 on branch 1.2.x (CI: Pin nbformat to 5.0.8)
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| 2021-01-14T23:24:37Z
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NONE
| null |
Backport PR #39177: CI: Pin nbformat to 5.0.8
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|
WIP: Troublehsoot nbformat
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| 2021-01-14T23:32:34Z
| 2021-04-26T22:00:27Z
| 2021-01-15T00:19:53Z
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MEMBER
| null |
I could reproduce this locally, but after automatically downloading new object.inv files the issue was gone.
It seemed as the warning was unknown keyword and, have to troubleshoot here
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BUG: ExcelWriter.book --> no member ->Slow Execution Time<-
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| 6
| 2021-01-14T23:49:52Z
| 2021-02-08T15:14:25Z
| 2021-02-08T15:14:25Z
|
NONE
| null |
- [*] I have checked that this issue has not already been reported.
- [*] I have confirmed this bug exists on the latest version of pandas.
- [*] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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.
Edit: As indicated in https://github.com/pandas-dev/pandas/issues/39181#issuecomment-769507379, the source of the performance degradation was trailing nans at the end of the DataFrame after reading - rhshadrach
#### Code Sample, a copy-pastable example
```python
# Your code here
writer = ExcelWriter(# pylint: disable=abstract-class-instantiated
path=fileName, engine='xlsxwriter')
workbook = writer.book # pylint: disable=no-member
header_format = workbook.add_format({'bold': True, 'align': 'center',
'fg_color': '#80bfff', 'border': 2,
'font_name': 'Times New Roman', 'font_size': 9})
#### Problem description
the pylint detects that ExcelWriter don't have "book" member, but the code works well just have
a very bad execution time in comparison with the 1.1.5 Pandas version
#### Expected Output
Save DataFrames in the same file but in different sheets.
Expected time: 30 sec (pandas==1.1.5)
Current time: 90 sec (pandas==1.2.0)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Spanish_Mexico.1252
pandas : 1.2.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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CI/DOC: Add option to build whatsnew
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[] | null | 3
| 2021-01-15T03:51:04Z
| 2021-02-12T22:00:44Z
| 2021-02-12T16:37:31Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Adds the option to compile the whatsnew. It also builds the API (to resolve links) and opens the whatsnew page in a browser when complete. This is the most frequent reason why I build the docs, and cuts down build time by 40% on my machine.
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MDU6SXNzdWU3ODY1MzYyODI=
| 39,183
|
January 2021 Docs Meeting
|
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| 2021-01-15T05:02:57Z
| 2021-02-16T04:51:47Z
| 2021-01-21T15:03:44Z
|
MEMBER
| null |
- [Thursday, January 21st at 9am EST - 14:00 UTC](https://everytimezone.com/?t=6008c400,348)
- [Zoom link](https://nyu.zoom.us/j/97315680910?pwd=NEJ3ZityVUM3VkltOGdVS01JbzZOQT09)
- [Notes](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#)
---
It was mentioned at the Developer Meeting (https://github.com/pandas-dev/pandas/issues/39125) that there would be interest in having a meeting specifically focused on documentation. I hope the core team doesn't mind me pushing ahead on that 😄
Please [**provide your availability**](https://doodle.com/poll/m46rp3ngx8f7sd62?utm_source=poll&utm_medium=link) in the next few days so we can find a time that works for the most people. Will comment with the date and Zoom link once finalized. All are welcome.
[Agenda/notes doc](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#) — feel free to add things in advance. Thanks!
@pandas-dev/pandas-core @pandas-dev/pandas-triage @pandas-dev/pydata-sphinx-theme @bollwyvl
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|
BUG: Numpy ufuncs e.g. np.[op](df1, df2) aligns columns in pandas 1.2.0 where it did not before
|
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| 10
| 2021-01-15T05:41:59Z
| 2021-01-20T07:22:57Z
| 2021-01-20T07:22:57Z
|
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
df = pd.DataFrame({k: [1,2,3,4,5] for k in 'abcd'})
np.add(df[['a', 'b']], df[['c', 'd']])
```
#### Problem description
This is a regression from pandas 1.1.5 (both versions are using numpy 1.19.5).
Normally if we want to add, subtract, multiply or divide df columns with different names we get NaNs because the column names don't match. E.g:
```python
>>> df[['a', 'b']] + df[['c', 'd']]
a b c d
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
```
To get around this, we would use np.[op](df1, df2).
However, we get the same output as above.
```python
>>> np.add(df[['a', 'b']], df[['c', 'd']])
a b c d
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
```
#### Expected Output
```python
# Using pandas 1.1.5:
>>> np.add(df[['a', 'b']], df[['c', 'd']])
a b
0 2 2
1 4 4
2 6 6
3 8 8
4 10 10
```
#### Temporary solution
```python
# This may have a potential copy penalty with the conversion to numpy
>>> df[['a', 'b']] + df[['c', 'd']].to_numpy()
a b
0 2 2
1 4 4
2 6 6
3 8 8
4 10 10
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
Just my 2 cents: I was more than willing to test this on a nightly / master release, but it doesn't appear you release those. It could be quite beneficial to publish nightlies to PyPl so we don't report issues that have already been fixed. For some, it might be easier to test a nightly than peruse recent open and closed issues.
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MDExOlB1bGxSZXF1ZXN0NTU1NTExNDgy
| 39,185
|
DOC: set the documentation language
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| 1
| 2021-01-15T07:22:26Z
| 2021-01-21T17:46:42Z
| 2021-01-21T17:46:42Z
|
MEMBER
| null |
Ensures the language is specified in the generated HTML. [Documentation on the configuration option.](https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-language)
Before:
```
$ curl -s https://pandas.pydata.org/docs/dev/getting_started/comparison/comparison_with_spreadsheets.html | grep "<html"
<html>
```
After:
```
$ python make.py --single getting_started/comparison/comparison_with_spreadsheets.rst
$ grep "<html" build/html/getting_started/comparison/comparison_with_spreadsheets.html
<html lang="en">
```
- [ ] ~~closes #xxxx~~
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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REF: extract classes in pandas/core/describe.py
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| 1
| 2021-01-15T12:12:34Z
| 2021-01-20T03:31:05Z
| 2021-01-19T20:23:32Z
|
MEMBER
| null |
- [ ] xref #36833
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Extract classes ``SeriesDescriber`` and ``DataFrameDescriber``.
In the next steps I am going to enable strategy pattern and move corresponding describe functions to the concrete strategy classes (for each datatype).
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Backport PR #39161 on branch 1.2.x (Fix regression in loc setitem raising KeyError when enlarging df with multiindex)
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| 2021-01-15T15:21:15Z
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NONE
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Backport PR #39161: Fix regression in loc setitem raising KeyError when enlarging df with multiindex
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REGR: Different results from DataFrame.apply and str accessor
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| 2
| 2021-01-15T15:17:31Z
| 2021-01-16T10:02:08Z
| 2021-01-16T01:12:46Z
|
MEMBER
| null |
- [ ] closes #38979
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
@jbrockmendel despite my comment https://github.com/pandas-dev/pandas/issues/38979#issuecomment-760470446, I had no joy trying to locate the caching issue.
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BUG: queries on categorical string columns in read_hdf return unexpected results
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| 2
| 2021-01-15T15:57:44Z
| 2021-01-30T20:46:14Z
| 2021-01-30T20:46:14Z
|
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
h5_path = 'test.h5'
df = pd.DataFrame({'col': ['a', 'b', 's']})
categorical_values = list(sorted(list(df.col.unique()))) # sorted because of https://github.com/pandas-dev/pandas/issues/16623
max_widths = {'col': 1} # will also set this column to be a "data column"
df.col = df.col.astype('category')
df.col.cat.set_categories(categorical_values, inplace=True)
df.to_hdf(h5_path, 'main', mode='a', format='table', append=True, min_itemsize=max_widths)
read_df = pd.read_hdf(h5_path, where='col == "q"') #returns df with index=2, col = s
```
```python
h5_path = 'test.h5'
df = pd.DataFrame({'col': ['Word', 'Test']})
categorical_values = list(sorted(list(df.col.unique())))
max_widths = {'col': 4}
df.col = df.col.astype('category')
df.col.cat.set_categories(categorical_values, inplace=True)
df.to_hdf(h5_path, 'main', mode='a', format='table', append=True, min_itemsize=max_widths)
read_df = pd.read_hdf(h5_path, where='col == "W"') #returns df with index=0, col=Word
read_df = pd.read_hdf(h5_path, where='col == "T"') # returns empty df (as expected)
```
#### Problem description
Using the where clause for on disk hdf queries appears to give incorrect results sometimes. From what I have tested, this appears to only happen for columns that are both string based and categoricals. This is important because the output is completely inaccurate and makes this feature mostly unusable for these column types. I should note that I have not seen issues with querying for values that are present in the dataframe however.
#### Expected Output
For all read_hdf calls, the expected output is an empty dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-514.21.2.el7.x86_64
Version : #1 SMP Tue Jun 20 12:24:47 UTC 2017
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3ODcwNDEzNjk=
| 39,190
|
BUG: pd.to_csv(..., compression="zip") creates multiple files in zip archive that cannot be read by pd.read_csv()
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[] | null | 4
| 2021-01-15T16:54:39Z
| 2021-01-15T19:15:50Z
| 2021-01-15T19:10:30Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# enough data to cause chunking into multiple files
n_data = 100000
df = pd.DataFrame(
{'name': ["Raphael"]*n_data,
'mask': ["red"]*n_data,
'weapon': ["sai"]*n_data,
}
)
compression_opts = dict(method='zip', archive_name='out.csv')
df.to_csv('out.csv.zip', index=False, compression=compression_opts)
# reading back the data produces an error
r_df = pd.read_csv("out.csv.zip")
# passing in compression_opts doesn't work either
r_df = pd.read_csv("out.csv.zip", compression=compression_opts)
```
How pandas was installed:
```bash
~/projects/testing$ ~/.pyenv/versions/3.8.6/bin/python3.8 -m venv text_venv
~/projects/testing$ . text_venv/bin/activate
(text_venv) ~/projects/testing$ pip install pandas==1.2.0
```
#### Problem description
Introduced in 1.2.0, `to_csv` with compression="zip" has an issue with chunking of data into multiple data files within the zip archive, when reading back the data this produces an error. When the data is very small (for example if `n_data` above was 2), this produces only one data file in the archive and no error when reading the archive.
This has been reported by other users on StackOverflow here: https://stackoverflow.com/questions/65689941/when-using-pandas-dataframe-to-csv-with-compression-zip-it-creates-a-zip-f
#### Expected Output
The `to_csv` should work with `read_csv` using zip compression. The behaviour with small data and medium sized data should be the same. The archive should always contain a single file or be read into a single df.
#### Output of ``pd.show_versions()``
<details>
Python 3.8.6 (default, Nov 25 2020, 11:35:37)
[Clang 10.0.1 (clang-1001.0.46.4)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
/Users/tom.s/projects/testing/text_venv/lib/python3.8/site-packages/setuptools/distutils_patch.py:25: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
warnings.warn(
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Nov 10 00:07:31 PST 2020; root:xnu-4903.278.51~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
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
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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Revert "BUG/REG: RollingGroupby MultiIndex levels dropped (#38737)"
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| 2
| 2021-01-15T17:26:31Z
| 2021-01-16T09:58:33Z
| 2021-01-16T01:10:14Z
|
MEMBER
| null |
This reverts commit a37f1a45e83d7f803d7fcab7d384da28e9c1e714.
reverts PR #38737
I think we ended up deciding to revert on master and backport to keep master and 1.2.x in sync and not release this change in 1.2.1 to allow for further discussion.
once this revert PR merged:
- [x] backport
- [x] the blocker tag can then be removed from #38787
- [x] #38523 will be reopen temporarily
- [ ] @mroeschke will open a new PR with these changes (in draft? milestoned 1.2.2?)
if not these changes, the mutliindex with a single level may still need resolution (and backport) to close #38523
cc @mroeschke @jorisvandenbossche
add 1.2.1 milestone for this revert for backport.
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QST: DataFrame.join on key columns
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| 7
| 2021-01-15T19:30:09Z
| 2021-09-03T14:18:15Z
| null |
CONTRIBUTOR
| 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
In pandas 1.2.0, DataFrame.join on key columns doesn't seem to follow the rules of SQL right join and outer join.
In the example below, column `B` in the joining results should've preserved its original values in `pdf2`, following the rules of SQL joins.
I'm wondering whether this is intentional. If yes, it would be great I can get help to understand how it works.
```python
>>> import pandas as pd
>>> pdf1 = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> pdf2 = pd.DataFrame({"B": [11, 12, 13], "C": [14, 15, 16]}, index=[2,3,4])
>>> pdf1.join(pdf2, on='B', lsuffix='_left', how='right')
A B_left B C
NaN NaN NaN 2 14
NaN NaN NaN 3 15
0.0 1.0 4.0 13 16
>>> pdf1.join(pdf2, on='B', lsuffix='_left', how='outer')
A B_left B C
0.0 1.0 4.0 4 16.0
1.0 2.0 5.0 5 NaN
2.0 3.0 6.0 6 NaN
NaN NaN NaN 2 14.0
NaN NaN NaN 3 15.0
>>> pd.__version__
'1.2.0'
```
To reproduce:
```python
import pandas as pd
pdf1 = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
pdf2 = pd.DataFrame({"B": [11, 12, 13], "C": [14, 15, 16]}, index=[2,3,4])
pdf1.join(pdf2, on='B', lsuffix='_left', how='right')
pdf1.join(pdf2, on='B', lsuffix='_left', how='outer')
```
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CI: Set xfail to strict=False for network tests
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| 5
| 2021-01-15T19:55:06Z
| 2021-01-16T15:01:19Z
| 2021-01-16T01:14:54Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
This one is ugly. Botocore and boto3 were upgraded tonight to 1.19.55 and 1.16.55. Since aiobotocore requires them to be .52 this is no longer installed, but aiobotocore is an required dependency from s3fs 0.5.*. So this get's downgraded to 0.4.2 were only botocore was a required dependency (no version specification). As soon as the requirements of aiobotocore are updated again this will switch back to s3fs=0.5.2 which will cause the tests to fail again. Hence setting them to strict=False
sorry for the long explanation :)
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REGR: fillna on datetime64[ns, UTC] column hits RecursionError
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| 6
| 2021-01-15T20:20:22Z
| 2021-01-16T12:32:09Z
| 2021-01-16T01:09:21Z
|
MEMBER
| null |
- [ ] closes #38851
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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@jbrockmendel this reverts #37040 in it's entirety for backport
feel free to push to this branch if we **need** any of the ancillary cleanup on 1.2.x otherwise can open a new PR to add those cleanups back to master.
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TST/REF: split large categorical indexing test
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REGR: NaT.__richmp__(dateobj)
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MEMBER
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- [x] whatsnew entry
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TST: Pytables Test Failures
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[] | null | 1
| 2021-01-15T23:39:07Z
| 2021-08-15T03:26:56Z
| null |
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=52416&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a
```
setup_path = 'tmp.__ocigUbjgSV__.h5'
@pytest.mark.filterwarnings(
"ignore:\\nthe :pandas.io.pytables.AttributeConflictWarning"
)
def test_coordinates(setup_path):
assert (c.values == np.arange(2) + 3).all()
result = store.select("df", where=c)
expected = df.loc[3:4, :]
tm.assert_frame_equal(result, expected)
assert isinstance(c, Index)
# multiple tables
_maybe_remove(store, "df1")
_maybe_remove(store, "df2")
df1 = tm.makeTimeDataFrame()
df2 = tm.makeTimeDataFrame().rename(columns="{}_2".format)
store.append("df1", df1, data_columns=["A", "B"])
store.append("df2", df2)
c = store.select_as_coordinates("df1", ["A>0", "B>0"])
df1_result = store.select("df1", c)
df2_result = store.select("df2", c)
result = concat([df1_result, df2_result], axis=1)
expected = concat([df1, df2], axis=1)
expected = expected[(expected.A > 0) & (expected.B > 0)]
> tm.assert_frame_equal(result, expected)
E AssertionError: (None, <18 * BusinessDays>)
pandas\tests\io\pytables\test_store.py:653: AssertionError
```
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NONE
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Backport PR #39191: Revert "BUG/REG: RollingGroupby MultiIndex levels dropped (#38737)"
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NONE
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Backport PR #39188: REGR: Different results from DataFrame.apply and str accessor
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NONE
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Backport PR #39193: CI: Set xfail to strict=False for network tests
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MDExOlB1bGxSZXF1ZXN0NTU2MDUxMTcz
| 39,201
|
Backport PR #39196 on branch 1.2.x (REGR: NaT.__richmp__(dateobj))
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| 2021-01-16T01:18:22Z
| 2021-01-16T10:06:32Z
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NONE
| null |
Backport PR #39196: REGR: NaT.__richmp__(dateobj)
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MDExOlB1bGxSZXF1ZXN0NTU2MDUyMDkx
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REGR: to_stata tried to remove file before closing it
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| 15
| 2021-01-16T01:24:02Z
| 2021-01-19T15:54:08Z
| 2021-01-18T15:22:07Z
|
CONTRIBUTOR
| null |
Spin-off from #39047:
If an error occurred during `to_stata`, the code tried to first remove the file and then close the file handle:
1. removing the file failed on Windows (it is still opened)
2. It created an empty file when closing the handle
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MDU6SXNzdWU3ODczMTU5MTM=
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STYLE: dont' use pd.api.types anywhere in tests
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| 10
| 2021-01-16T01:28:21Z
| 2021-01-21T15:04:52Z
| 2021-01-21T15:04:52Z
|
CONTRIBUTOR
| null |
example here: https://github.com/pandas-dev/pandas/pull/37367/files#diff-69c698518ca64a05f8b137ff9bb5445ce9fd21139b396e9b0d4ce7338b2a3984R132
we should just ban this with a precommit style rule (and import things directly). the doc-strings also should be updated to do the import once.
```
grep -r pd.api.types ~/pandas-dev/pandas
/home/jreback/pandas-dev/pandas/_libs/lib.pyx: >>> pd.api.types.is_scalar(dt)
/home/jreback/pandas-dev/pandas/_libs/lib.pyx: >>> pd.api.types.is_scalar([2, 3])
/home/jreback/pandas-dev/pandas/_libs/lib.pyx: >>> pd.api.types.is_scalar({0: 1, 2: 3})
/home/jreback/pandas-dev/pandas/_libs/lib.pyx: >>> pd.api.types.is_scalar((0, 2))
/home/jreback/pandas-dev/pandas/_libs/lib.pyx: >>> pd.api.types.is_scalar(Fraction(3, 5))
/home/jreback/pandas-dev/pandas/tests/frame/test_ufunc.py: if pd.api.types.is_extension_array_dtype(dtype) or isinstance(dtype, dict):
/home/jreback/pandas-dev/pandas/tests/frame/test_ufunc.py: pd.api.types.is_extension_array_dtype(dtype_a)
/home/jreback/pandas-dev/pandas/tests/frame/test_ufunc.py: or pd.api.types.is_extension_array_dtype(dtype_b)
/home/jreback/pandas-dev/pandas/tests/frame/test_ufunc.py: if pd.api.types.is_extension_array_dtype(dtype) or isinstance(dtype, dict):
/home/jreback/pandas-dev/pandas/tests/extension/base/dtype.py: return not pd.api.types.is_string_dtype(dtype)
/home/jreback/pandas-dev/pandas/tests/extension/base/dtype.py: return not pd.api.types.is_object_dtype(dtype)
/home/jreback/pandas-dev/pandas/tests/extension/arrow/arrays.py: if pd.api.types.is_scalar(item):
/home/jreback/pandas-dev/pandas/tests/extension/arrow/test_bool.py: assert pd.api.types.is_bool_dtype(data)
/home/jreback/pandas-dev/pandas/tests/extension/json/array.py: if pd.api.types.is_bool_dtype(item.dtype):
/home/jreback/pandas-dev/pandas/tests/extension/decimal/array.py: if pd.api.types.is_list_like(value):
/home/jreback/pandas-dev/pandas/tests/extension/decimal/array.py: if pd.api.types.is_scalar(key):
/home/jreback/pandas-dev/pandas/tests/extension/list/array.py: elif pd.api.types.is_string_dtype(dtype) and not pd.api.types.is_object_dtype(
/home/jreback/pandas-dev/pandas/tests/extension/test_integer.py: and pd.api.types.is_integer_dtype(other.dtype)
/home/jreback/pandas-dev/pandas/tests/extension/test_floating.py: and pd.api.types.is_float_dtype(other.dtype)
/home/jreback/pandas-dev/pandas/core/dtypes/inference.py: >>> pd.api.types.is_number(1)
/home/jreback/pandas-dev/pandas/core/dtypes/inference.py: >>> pd.api.types.is_number(7.15)
/home/jreback/pandas-dev/pandas/core/dtypes/inference.py: >>> pd.api.types.is_number(False)
/home/jreback/pandas-dev/pandas/core/dtypes/inference.py: >>> pd.api.types.is_number("foo")
/home/jreback/pandas-dev/pandas/core/dtypes/inference.py: >>> pd.api.types.is_number("5")
/home/jreback/pandas-dev/pandas/core/generic.py: >>> cat_dtype = pd.api.types.CategoricalDtype(
```
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| 39,204
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BUG: assert_frame_equal raising TypeError with check_like and mixed dtype in Index or columns
|
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| 4
| 2021-01-16T02:18:32Z
| 2021-01-16T14:59:56Z
| 2021-01-16T14:21:19Z
|
MEMBER
| null |
- [x] closes #39168
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Worked on 1.1.5 for assert_frame_equal and was not supported for assert_index_equal
|
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| 787,331,064
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MDU6SXNzdWU3ODczMzEwNjQ=
| 39,205
|
CI: failing numpy_dev build on 1.2.x
|
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| 4
| 2021-01-16T02:48:17Z
| 2021-01-17T13:23:14Z
| 2021-01-17T13:23:13Z
|
CONTRIBUTOR
| null |
this is passing on master, but not on 1.2.1
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=52437&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=ce687173-08c6-5301-838d-71b2dda24510
```
2021-01-16T02:31:05.2414654Z =================================== FAILURES ===================================
2021-01-16T02:31:05.2415844Z _ TestComparison.test_index_series_compat[eq-IntervalIndex-array-assert_numpy_array_equal] _
2021-01-16T02:31:05.2416708Z [gw1] linux -- Python 3.8.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2021-01-16T02:31:05.2417059Z
2021-01-16T02:31:05.2417505Z self = <pandas.tests.arithmetic.test_interval.TestComparison object at 0x7f7984759fa0>
2021-01-16T02:31:05.2420975Z op = <built-in function eq>
2021-01-16T02:31:05.2447033Z constructor = <class 'pandas.core.indexes.interval.IntervalIndex'>
2021-01-16T02:31:05.2461011Z expected_type = <built-in function array>
2021-01-16T02:31:05.2461472Z assert_func = <function assert_numpy_array_equal at 0x7f79d19fb280>
2021-01-16T02:31:05.2461911Z
2021-01-16T02:31:05.2462190Z @pytest.mark.parametrize(
2021-01-16T02:31:05.2462598Z "constructor, expected_type, assert_func",
2021-01-16T02:31:05.2462923Z [
2021-01-16T02:31:05.2463268Z (IntervalIndex, np.array, tm.assert_numpy_array_equal),
2021-01-16T02:31:05.2463755Z (Series, Series, tm.assert_series_equal),
2021-01-16T02:31:05.2464080Z ],
2021-01-16T02:31:05.2464384Z )
2021-01-16T02:31:05.2464794Z def test_index_series_compat(self, op, constructor, expected_type, assert_func):
2021-01-16T02:31:05.2465386Z # IntervalIndex/Series that rely on IntervalArray for comparisons
2021-01-16T02:31:05.2465772Z breaks = range(4)
2021-01-16T02:31:05.2466225Z index = constructor(IntervalIndex.from_breaks(breaks))
2021-01-16T02:31:05.2466569Z
2021-01-16T02:31:05.2466845Z # scalar comparisons
2021-01-16T02:31:05.2467209Z other = index[0]
2021-01-16T02:31:05.2467523Z result = op(index, other)
2021-01-16T02:31:05.2467991Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2468402Z assert_func(result, expected)
2021-01-16T02:31:05.2468765Z
2021-01-16T02:31:05.2469036Z other = breaks[0]
2021-01-16T02:31:05.2469532Z result = op(index, other)
2021-01-16T02:31:05.2469988Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2470394Z assert_func(result, expected)
2021-01-16T02:31:05.2470739Z
2021-01-16T02:31:05.2471260Z # list-like comparisons
2021-01-16T02:31:05.2471642Z other = IntervalArray.from_breaks(breaks)
2021-01-16T02:31:05.2472056Z result = op(index, other)
2021-01-16T02:31:05.2472452Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2472923Z assert_func(result, expected)
2021-01-16T02:31:05.2473207Z
2021-01-16T02:31:05.2473879Z other = [index[0], breaks[0], "foo"]
2021-01-16T02:31:05.2474269Z result = op(index, other)
2021-01-16T02:31:05.2474668Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2475135Z > assert_func(result, expected)
2021-01-16T02:31:05.2475399Z
2021-01-16T02:31:05.2475764Z pandas/tests/arithmetic/test_interval.py:287:
2021-01-16T02:31:05.2476208Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-01-16T02:31:05.2476557Z
2021-01-16T02:31:05.2476986Z left = array([False, False, False]), right = array([ True, False, False])
2021-01-16T02:31:05.2477357Z err_msg = None
2021-01-16T02:31:05.2477641Z
2021-01-16T02:31:05.2477946Z def _raise(left, right, err_msg):
2021-01-16T02:31:05.2478307Z if err_msg is None:
2021-01-16T02:31:05.2478738Z if left.shape != right.shape:
2021-01-16T02:31:05.2479107Z raise_assert_detail(
2021-01-16T02:31:05.2479563Z obj, f"{obj} shapes are different", left.shape, right.shape
2021-01-16T02:31:05.2479942Z )
2021-01-16T02:31:05.2480247Z
2021-01-16T02:31:05.2480506Z diff = 0
2021-01-16T02:31:05.2480879Z for left_arr, right_arr in zip(left, right):
2021-01-16T02:31:05.2481340Z # count up differences
2021-01-16T02:31:05.2481806Z if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
2021-01-16T02:31:05.2482323Z diff += 1
2021-01-16T02:31:05.2482597Z
2021-01-16T02:31:05.2482891Z diff = diff * 100.0 / left.size
2021-01-16T02:31:05.2483349Z msg = f"{obj} values are different ({np.round(diff, 5)} %)"
2021-01-16T02:31:05.2483814Z > raise_assert_detail(obj, msg, left, right, index_values=index_values)
2021-01-16T02:31:05.2484450Z E AssertionError: numpy array are different
2021-01-16T02:31:05.2484795Z E
2021-01-16T02:31:05.2485284Z E numpy array values are different (33.33333 %)
2021-01-16T02:31:05.2485649Z E [left]: [False, False, False]
2021-01-16T02:31:05.2486175Z E [right]: [True, False, False]
2021-01-16T02:31:05.2486482Z
2021-01-16T02:31:05.2486757Z pandas/_testing.py:1159: AssertionError
2021-01-16T02:31:05.2487496Z _ TestComparison.test_index_series_compat[eq-Series-Series-assert_series_equal] _
2021-01-16T02:31:05.2488191Z [gw1] linux -- Python 3.8.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2021-01-16T02:31:05.2488567Z
2021-01-16T02:31:05.2488904Z self = <pandas.tests.arithmetic.test_interval.TestComparison object at 0x7f79840c7730>
2021-01-16T02:31:05.2489563Z op = <built-in function eq>, constructor = <class 'pandas.core.series.Series'>
2021-01-16T02:31:05.2490223Z expected_type = <class 'pandas.core.series.Series'>
2021-01-16T02:31:05.2490622Z assert_func = <function assert_series_equal at 0x7f79d19fb3a0>
2021-01-16T02:31:05.2490958Z
2021-01-16T02:31:05.2491206Z @pytest.mark.parametrize(
2021-01-16T02:31:05.2491526Z "constructor, expected_type, assert_func",
2021-01-16T02:31:05.2491885Z [
2021-01-16T02:31:05.2492201Z (IntervalIndex, np.array, tm.assert_numpy_array_equal),
2021-01-16T02:31:05.2492660Z (Series, Series, tm.assert_series_equal),
2021-01-16T02:31:05.2492958Z ],
2021-01-16T02:31:05.2493334Z )
2021-01-16T02:31:05.2493713Z def test_index_series_compat(self, op, constructor, expected_type, assert_func):
2021-01-16T02:31:05.2494196Z # IntervalIndex/Series that rely on IntervalArray for comparisons
2021-01-16T02:31:05.2494601Z breaks = range(4)
2021-01-16T02:31:05.2515866Z index = constructor(IntervalIndex.from_breaks(breaks))
2021-01-16T02:31:05.2516454Z
2021-01-16T02:31:05.2516755Z # scalar comparisons
2021-01-16T02:31:05.2517121Z other = index[0]
2021-01-16T02:31:05.2517451Z result = op(index, other)
2021-01-16T02:31:05.2518053Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2518530Z assert_func(result, expected)
2021-01-16T02:31:05.2518818Z
2021-01-16T02:31:05.2519159Z other = breaks[0]
2021-01-16T02:31:05.2519477Z result = op(index, other)
2021-01-16T02:31:05.2519960Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2520457Z assert_func(result, expected)
2021-01-16T02:31:05.2520751Z
2021-01-16T02:31:05.2521443Z # list-like comparisons
2021-01-16T02:31:05.2521826Z other = IntervalArray.from_breaks(breaks)
2021-01-16T02:31:05.2522244Z result = op(index, other)
2021-01-16T02:31:05.2522645Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2523109Z assert_func(result, expected)
2021-01-16T02:31:05.2523399Z
2021-01-16T02:31:05.2523698Z other = [index[0], breaks[0], "foo"]
2021-01-16T02:31:05.2524100Z result = op(index, other)
2021-01-16T02:31:05.2524497Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2524958Z > assert_func(result, expected)
2021-01-16T02:31:05.2525215Z
2021-01-16T02:31:05.2525528Z pandas/tests/arithmetic/test_interval.py:287:
2021-01-16T02:31:05.2526028Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-01-16T02:31:05.2526680Z pandas/_libs/testing.pyx:46: in pandas._libs.testing.assert_almost_equal
2021-01-16T02:31:05.2527142Z cpdef assert_almost_equal(a, b,
2021-01-16T02:31:05.2527546Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-01-16T02:31:05.2527956Z
2021-01-16T02:31:05.2528320Z > raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
2021-01-16T02:31:05.2528734Z E AssertionError: Series are different
2021-01-16T02:31:05.2529231Z E
2021-01-16T02:31:05.2529534Z E Series values are different (33.33333 %)
2021-01-16T02:31:05.2529923Z E [index]: [0, 1, 2]
2021-01-16T02:31:05.2530368Z E [left]: [False, False, False]
2021-01-16T02:31:05.2530715Z E [right]: [True, False, False]
2021-01-16T02:31:05.2531028Z
2021-01-16T02:31:05.2531331Z pandas/_libs/testing.pyx:161: AssertionError
2021-01-16T02:31:05.2532134Z _ TestComparison.test_index_series_compat[ne-IntervalIndex-array-assert_numpy_array_equal] _
2021-01-16T02:31:05.2532992Z [gw1] linux -- Python 3.8.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2021-01-16T02:31:05.2533317Z
2021-01-16T02:31:05.2533705Z self = <pandas.tests.arithmetic.test_interval.TestComparison object at 0x7f7984d5c5e0>
2021-01-16T02:31:05.2534243Z op = <built-in function ne>
2021-01-16T02:31:05.2534860Z constructor = <class 'pandas.core.indexes.interval.IntervalIndex'>
2021-01-16T02:31:05.2535420Z expected_type = <built-in function array>
2021-01-16T02:31:05.2535864Z assert_func = <function assert_numpy_array_equal at 0x7f79d19fb280>
2021-01-16T02:31:05.2536160Z
2021-01-16T02:31:05.2536407Z @pytest.mark.parametrize(
2021-01-16T02:31:05.2536807Z "constructor, expected_type, assert_func",
2021-01-16T02:31:05.2537102Z [
2021-01-16T02:31:05.2537468Z (IntervalIndex, np.array, tm.assert_numpy_array_equal),
2021-01-16T02:31:05.2537854Z (Series, Series, tm.assert_series_equal),
2021-01-16T02:31:05.2538200Z ],
2021-01-16T02:31:05.2538511Z )
2021-01-16T02:31:05.2538887Z def test_index_series_compat(self, op, constructor, expected_type, assert_func):
2021-01-16T02:31:05.2606624Z # IntervalIndex/Series that rely on IntervalArray for comparisons
2021-01-16T02:31:05.2607151Z breaks = range(4)
2021-01-16T02:31:05.2687364Z index = constructor(IntervalIndex.from_breaks(breaks))
2021-01-16T02:31:05.2687833Z
2021-01-16T02:31:05.2688104Z # scalar comparisons
2021-01-16T02:31:05.2688414Z other = index[0]
2021-01-16T02:31:05.2688719Z result = op(index, other)
2021-01-16T02:31:05.2689117Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2689504Z assert_func(result, expected)
2021-01-16T02:31:05.2689783Z
2021-01-16T02:31:05.2690033Z other = breaks[0]
2021-01-16T02:31:05.2690325Z result = op(index, other)
2021-01-16T02:31:05.2690722Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2691115Z assert_func(result, expected)
2021-01-16T02:31:05.2691394Z
2021-01-16T02:31:05.2692145Z # list-like comparisons
2021-01-16T02:31:05.2692512Z other = IntervalArray.from_breaks(breaks)
2021-01-16T02:31:05.2692846Z result = op(index, other)
2021-01-16T02:31:05.2693221Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2693623Z assert_func(result, expected)
2021-01-16T02:31:05.2693887Z
2021-01-16T02:31:05.2694180Z other = [index[0], breaks[0], "foo"]
2021-01-16T02:31:05.2694546Z result = op(index, other)
2021-01-16T02:31:05.2694934Z expected = expected_type(self.elementwise_comparison(op, index, other))
2021-01-16T02:31:05.2695322Z > assert_func(result, expected)
2021-01-16T02:31:05.2695556Z
2021-01-16T02:31:05.2695863Z pandas/tests/arithmetic/test_interval.py:287:
2021-01-16T02:31:05.2696276Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-01-16T02:31:05.2696602Z
2021-01-16T02:31:05.2696918Z left = array([ True, True, True]), right = array([False, True, True])
2021-01-16T02:31:05.2697265Z err_msg = None
2021-01-16T02:31:05.2697480Z
2021-01-16T02:31:05.2697762Z def _raise(left, right, err_msg):
2021-01-16T02:31:05.2698114Z if err_msg is None:
2021-01-16T02:31:05.2698459Z if left.shape != right.shape:
2021-01-16T02:31:05.2698803Z raise_assert_detail(
2021-01-16T02:31:05.2699198Z obj, f"{obj} shapes are different", left.shape, right.shape
2021-01-16T02:31:05.2699761Z )
2021-01-16T02:31:05.2700009Z
2021-01-16T02:31:05.2700245Z diff = 0
2021-01-16T02:31:05.2700598Z for left_arr, right_arr in zip(left, right):
2021-01-16T02:31:05.2700997Z # count up differences
2021-01-16T02:31:05.2701434Z if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
2021-01-16T02:31:05.2701881Z diff += 1
2021-01-16T02:31:05.2702133Z
2021-01-16T02:31:05.2702418Z diff = diff * 100.0 / left.size
2021-01-16T02:31:05.2702802Z msg = f"{obj} values are different ({np.round(diff, 5)} %)"
2021-01-16T02:31:05.2703257Z > raise_assert_detail(obj, msg, left, right, index_values=index_values)
2021-01-16T02:31:05.2703684Z E AssertionError: numpy array are different
2021-01-16T02:31:05.2704002Z E
2021-01-16T02:31:05.2704325Z E numpy array values are different (33.33333 %)
2021-01-16T02:31:05.2704694Z E [left]: [True, True, True]
2021-01-16T02:31:05.2705054Z E [right]: [False, True, True]
2021-01-16T02:31:05.2705306Z
2021-01-16T02:31:05.2705579Z pandas/_testing.py:1159: AssertionError
2021-01-16T02:31:05.2706269Z _ TestComparison.test_index_series_compat[ne-Series-Series-assert_series_equal] _
2021-01-16T02:31:05.2706975Z [gw1] linux -- Python 3.8.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
```
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MDExOlB1bGxSZXF1ZXN0NTU2MTYzMjY2
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Backport PR #39194: REGR: fillna on datetime64[ns, UTC] column hits RecursionError
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MEMBER
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Backport PR #39194
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DOC: add new page 'how to read these doc' in get started section
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This page explains the code block are run in notebook
- [x] closes #39130
- [x] ~tests added / passed~
- [x] ~Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them~
- [x] ~whatsnew entry~
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Backport PR #39204: BUG: assert_frame_equal raising TypeError with check_like and mixed dtype in Index or columns
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Backport PR #39204
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API: Should apply be smart?
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From https://github.com/pandas-dev/pandas/pull/34998#issuecomment-759416692
> ..., this got me a bit thinking about the general issue we are tackling here: **should `apply()` be smart and try to infer what kind of function is being applied?** Or should it not try to be smart and always do the same (eg always prepend groups by default).
>
> For the specific case where the applied function is a transformation (preserving identical index), the consequence of the changes we decided to do in this PR, is that we want `apply` to no longer be smart.
>
> But currently (and this is long-time behaviour), `apply` tries to be smart and this is also documented. In the "flexible apply" section in the docs, there is a note that says:
>
> > ``apply`` can act as a reducer, transformer, *or* filter function, depending on exactly what is passed to it.
So depending on the path taken, and exactly what you are grouping. Thus the grouped column(s) may be included in
the output as well as set the indices.
>
> (So if we go forward as is with this PR, that note should actually be updated.)
>
> The consequence of this PR is that `apply` will no longer try to detect if it acted as a *transformer* or not (at least by default with `group_keys=True`, as `group_keys=False` still gives this reindexing behaviour for a transformer as discussed above).
>
> But if we no longer detect the transformer case, should we then also stop detecting the *reducer* case?
IMO, `groupby.apply` should not be a one-stop-shop for all your UDF needs. If users want to use a reducer, use `agg`; if they want to use a transformer, use `transform`; if they want to use a filter, use `filter`. The role of `apply`, to me, should be for UDFs that don't fit into these roles. In this sense, I think `apply` should not go out of its way to generate more convenient/sensible output for these classes of UDFs.
The result of a groupby is a mapping from each group to a python object. Whatever behavior is decided for `apply`, it should be well-defined and entire with no more assumptions than that. Here is a naive attempt at doing so.
- If all constituent results are Series/DataFrames, concatenate results together.
- Otherwise, the result is a Series where each value is what was returned from the UDF; dtype is inferred.
- keyword-only arg on `apply` (not `groupby`) to control if the groups are added to the index, columns, or neither.
Perhaps numpy-specific support should be added in. Probably things need to be added to support UDFs that reach `groupby.apply` via resample/window, but I'm not familiar enough with that code to reason about it.
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TYP: misc annotations
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: DataFrame.apply axis=1 for str ops with no axis argument
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| 2021-01-16T20:09:14Z
| 2021-01-19T20:29:25Z
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|
MEMBER
| null |
E.g.
```
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
print(df.apply('pct_change', axis=0))
print(df.apply('pct_change', axis=1))
```
both give the output
```
a b
0 NaN NaN
1 1.0 0.333333
```
This should instead raise rather than silently give incorrect results.
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BUG: DataFrame.apply axis=1 for str ops with no axis argument
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- [x] closes #39211
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- [x] whatsnew entry
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CLN: use getvalue() instead of seek(0)+read()
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| 1
| 2021-01-16T21:04:45Z
| 2021-01-19T21:19:24Z
| 2021-01-19T20:20:38Z
|
CONTRIBUTOR
| null |
`BytesIO`'s `getvalue()` is the same as `seek(0)` followed by `read()` if the buffer is not used in any other place (`getvalue` doesn't change the position within the buffer). getvalue is slightly faster then seek+read.
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QST:compute score across columns meeting a specific conditions
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[] | null | 1
| 2021-01-16T21:08:12Z
| 2021-01-16T21:22:27Z
| 2021-01-16T21:22:27Z
|
NONE
| null |
I have the following dataframe
Q11_1 Q11_2 Q11_3 Q11_4 Q11_5 Q11_6
2 2 2 1 1 5
2 2 2 3 3 3
2 2 2 2 3 3
2 2 2 2 4 3
4 4 2 4 5 2
How could I compute the score according to the following rule
if Q11_1 >=3:
score =1 else score =0
then if Q11_2 >=3 then we add 1 else score stay the same
then if Q11_3 >=3 then we add 1 else score stay the same
then if Q11_4 >=4 then we add 1 else score stay the same
then if Q11_5 >=4 then we add 1 else score stay the same
then if Q11_6 >=4 then we add 1 else score stay the same
This would give
Q11_1 Q11_2 Q11_3 Q11_4 Q11_5 Q11_6 score
2 2 2 1 1 5 1
2 2 2 3 3 3 0
2 2 2 2 3 3 0
2 2 2 2 4 3 1
4 4 2 4 5 2 4
thank you for your help. I couldn't find how to handle this
```
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TST GH26807 Break up test_strings
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|
MEMBER
| null |
This PR partially addresses xref #26807 in the case of pandas/tests/test_strings.py. It deletes that file and makes a new subpackage pandas/tests/strings/ with 8 new test modules plus a conftest file.
I have only moved tests and flattened the structure by moving the tests from being methods to functions - I haven't changed them in any other way. There are 1814 tests that were in pandas/tests/test_strings.py and are now in pandas/tests/strings/. I tried to keep each test module logically cohesive and less than 1000 lines long - test_strings.py still contains all the tests that don't really fit anywhere else.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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API/BUG: treat different UTC tzinfos as equal
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MEMBER
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- [x] closes #23959
- [x] tests added / passed
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REF: Split up parsers into smaller files
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| 2021-01-17T00:38:48Z
| 2021-01-22T18:44:52Z
| 2021-01-21T23:48:43Z
|
MEMBER
| null |
- [x] closes #38930
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Refactored them into 4 files, similar to described in the issue. If this is accepted I will try to clean this up a bit more through unifying the calls a bit
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"following_url": "https://api.github.com/users/jbrockmendel/following{/other_user}",
"gists_url": "https://api.github.com/users/jbrockmendel/gists{/gist_id}",
"gravatar_id": "",
"html_url": "https://github.com/jbrockmendel",
"id": 8078968,
"login": "jbrockmendel",
"node_id": "MDQ6VXNlcjgwNzg5Njg=",
"organizations_url": "https://api.github.com/users/jbrockmendel/orgs",
"received_events_url": "https://api.github.com/users/jbrockmendel/received_events",
"repos_url": "https://api.github.com/users/jbrockmendel/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/jbrockmendel/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jbrockmendel/subscriptions",
"type": "User",
"url": "https://api.github.com/users/jbrockmendel"
}
|
[] |
closed
| false
| null |
[] | null | 0
| 2021-01-17T01:06:09Z
| 2021-01-19T02:26:06Z
| 2021-01-19T02:25:56Z
|
MEMBER
| null |
Split off from a bigger branch
|
{
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"url": "https://api.github.com/repos/pandas-dev/pandas/issues/39218/reactions"
}
|
https://api.github.com/repos/pandas-dev/pandas/issues/39218/timeline
| null | 0
|
{
"diff_url": "https://github.com/pandas-dev/pandas/pull/39218.diff",
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|
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