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https://api.github.com/repos/pandas-dev/pandas/issues/26913 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26913/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26913/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26913/events | https://github.com/pandas-dev/pandas/issues/26913 | 457,209,864 | MDU6SXNzdWU0NTcyMDk4NjQ= | 26,913 | Tracking issue for EA Series Operations Support | {
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] | closed | false | null | [] | null | 4 | 2019-06-18T00:53:59Z | 2020-09-19T14:35:38Z | 2020-09-19T14:35:37Z | NONE | null | *Stale* | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26914 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26914/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26914/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26914/events | https://github.com/pandas-dev/pandas/pull/26914 | 457,216,976 | MDExOlB1bGxSZXF1ZXN0Mjg5MDc0Njc5 | 26,914 | WIP/POC: Allow (N, 1) and (1,N) EAs | {
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"description": "Extendin... | closed | false | null | [] | null | 29 | 2019-06-18T01:30:21Z | 2021-11-20T23:23:20Z | 2019-07-01T20:57:03Z | MEMBER | null | Discussed on the mailing list last week, cc @TomAugspurger @jreback @jorisvandenbossche
The central claim: if we allow EAs to take on shapes (N, 1) and (1, N), then we can get some major simplifications in core.internals.
For the proof of concept I've made the Datetimelike arrays accept these shapes and updated `DatetimeTZBlock` to see what simplifications are available. The `_wrap_data` implementation would also work for `Categorical` and `PandasArray`.
Three categories of simplifications are not in here yet and you'll have to use your imaginations
- some can only be made once all EAs allow these shapes, e.g. not having to pass `ndim` arg to Block constructors
- some would require additional tweaks on the EA subclasses, e.g. making `shift`, `__iter__`, or `take` aware of 2Dness
- some I just haven't got to yet. core.internals is rough.
Added bonus: this fleshes out bugs (see #26864)
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https://api.github.com/repos/pandas-dev/pandas/issues/26915 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26915/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26915/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26915/events | https://github.com/pandas-dev/pandas/issues/26915 | 457,230,197 | MDU6SXNzdWU0NTcyMzAxOTc= | 26,915 | Should Pandas adopt a naming convention for its protocol methods | {
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] | closed | false | null | [] | null | 6 | 2019-06-18T02:34:47Z | 2019-08-02T15:00:26Z | 2019-06-19T17:14:48Z | NONE | null | numpy has defined various protocol extensions over the years, and has consistently
named them with an array prefix, and "dunder" notation:
- `__array__`
- `__array_prepare__`, `__array_wrap__`, [Subclassing ndarray](http://docs.python.org/doc/numpy/user/basics.subclassing.html)
- `__aray_ufunc__` [NEP13](https://www.numpy.org/neps/nep-0013-ufunc-overrides.html
)
- `__array_function__` [NEP18](https://www.numpy.org/neps/nep-0018-array-function-protocol.html)
pandas has't been as diligent. The one example I've been dealing with, is `_reduce`. Originally an internal Series method (not a protocol), it now dispatches to `_reduce` for subclasses of `ExtensionArray`. https://github.com/pandas-dev/pandas/blob/baa77c33fb71c29acea21ba06adaf426ed4cb561/pandas/core/series.py#L3743-L3745
When reading the code for a new EA project, its hard to pick out that _reduce is actually an override of the parent class, instead of just an internal function written by the EA author. Something like `__pandas_reduce__` would have made this clearer.
Due to inexperience with EA, I lost a bit of time figuring out why `s.sum()` wasn't invoking the EA implementation I thought it would.
Granted, it's not exactly a protocol. For the forseeable future, this will always be an `ExtensionArray` subclass, rather than a duck typed object. But, for readability,
and while EA is experimental, this might be the right time to clean it up. | {
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} | 9 | 2019-06-18T03:08:55Z | 2019-06-25T14:10:04Z | 2019-06-25T12:48:14Z | MEMBER | null | - [x] closes #24775
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26917 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26917/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26917/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26917/events | https://github.com/pandas-dev/pandas/issues/26917 | 457,293,709 | MDU6SXNzdWU0NTcyOTM3MDk= | 26,917 | Tabular numbers should be used on tables | {
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"color": "006b7... | closed | false | null | [] | null | 5 | 2019-06-18T06:55:39Z | 2020-02-10T07:45:35Z | 2020-02-10T07:45:35Z | NONE | null | **Describe the bug**
Tables rendered in Jupyter Lab (likely also Jupyter Notebook), e.g. from pandas, do not use tabular numbers. This causes sorted columns to look unsorted and makes numbers harder to read and compare.
**To Reproduce**
Create a table like this:
<img width="299" alt="Screenshot 2019-06-14 at 08 38 42" src="https://user-images.githubusercontent.com/204911/59488447-dc541700-8e7f-11e9-8aff-50274888859c.png">
**Expected behavior**
With tabular numbers applied through CSS:
<img width="302" alt="Screenshot 2019-06-14 at 08 41 07" src="https://user-images.githubusercontent.com/204911/59488557-3228bf00-8e80-11e9-917d-62e6024233bf.png">
CSS used to apply tabular numbers:
```
table { font-variant-numeric: tabular-nums; }
```
**Screenshots**
Screenshots attached above.
**Desktop (please complete the following information):**
- OS: Any
- Browser: Any
- JupyterLab: Tested on 0.35.6, but probably applies to any version of Jupyter Lab
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"description": "Extending pandas with... | closed | false | null | [] | null | 8 | 2019-06-18T07:17:23Z | 2019-07-31T16:20:22Z | 2019-06-30T16:44:46Z | NONE | null | This summarized my current understanding of how the dispatch of Series methods *should* work for EA (related https://github.com/pandas-dev/pandas/issues/26913, https://github.com/pandas-dev/pandas/issues/26730, and https://github.com/pandas-dev/pandas/issues/26835) and It also provides helpful warnings against the several pitfalls I fell into while figuring things out. There are a lot more details involved then I foresaw.
The document is sloppy pseudo-sphinx for now (I haven't even compiled it yet), just to see if you agree with the content before spending the effort to polish it. I will, if/when the content represent a consensus instead of just my own take on the issue.
The description of how things work doesn't match the current state of the code right now, because there are a lot of little cleanups to be made in various functions:
- Getting rid of `com.values_for`
- Getting rid of `_values` and maybe even `_data` in some cases
- Getting rid of redundant calls to `np.asarray(self)` or `np.asarray(self._values)` and such.
but I don't think there's actually any radical change implied. It's just about making methods consistent in how they delegate to numpy, so that EA developers can count on predictable behavior and so reason about how the pandas, numpy, and their own classes interact.
So I hope this will actually seem natural and undramatic, but I may have overlooked one or more vital issues.
One important realization I've come to is that the recent discussion regarding NEP18 was somewhat
of a red herring rabbit-hole. IMO, pandas should simply provide the explicit guarantee that some things go via `_reduce` (or a future [`__reduce_pandas__`]()https://github.com/pandas-dev/pandas/issues/26915), while the others go to `np.<func>(array)`, and that the return value type should be an ExtensionArray when applicable. How the EA author deals with numpy's dispatch logic is nothing we need or should want to to speak out on. That's between the EA author and numpy.
Avoiding NEP18 altogether means that #26817, the `round()` PR, can be simplified to just a one line change to `Series.round`, and adding a new 4-line `round` method on `DecimalArray` as an example.
This would be the typical change needed to support (and provde examples) for other Series methods which are currently out of alignment. So going down the NEP18 (as suggested to me) seems like it was a misstep IMO, now we know.
Possible Concerns for future (not covered in doc):
- How should we support EA for np operation which Series does not provide, like `ceil` (discussed in #26892). We may need to modify `Series.apply` with a special case for EA, so that `s.apply(np.floor)` works for EA. I'm not sure.
- What if future operations are added to the Series namespace? If we ever add a new Series method, which is not a reduction (going via `_reduce`), and is not a convenience around some
numpy function (so that the guarantee mentioned above doesn't hold), we will need to have a new
mechanism in order to allow EA to support it. if this is a real concern, and there's no good answer, we might want to think about having a more general dispatching interface in place right now, instead of settling on the numpy dispatch guarantee mentioned, so as to be handle both existing and future operations.
As an imaginary example, if next year we add the `Series.fnorb` method, which fnorbs a numeric array using a new, cutting-edge, cython-optimized fnorbing algorithm, how do we then allow EA authors to provide an implementation for their own int ndarray backed EA?
cc @jorisvandenbossche, @TomAugspurger. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26919 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26919/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26919/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26919/events | https://github.com/pandas-dev/pandas/issues/26919 | 457,445,253 | MDU6SXNzdWU0NTc0NDUyNTM= | 26,919 | BUG: DataFrame(ndarray, dtype=..) does unsafe conversion | {
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} | 0 | 2019-06-18T12:14:57Z | 2021-05-21T15:45:13Z | 2021-05-21T15:45:13Z | MEMBER | null | From https://github.com/pandas-dev/pandas/pull/26848. When you pass a ndarray to the DataFrame constructor and specify a dtype, this does a "plain" numpy `astype`, which can have some unwanted side-effects (that we avoid in other parts of pandas) such as np.nan -> integer conversion and out of bounds timestamps:
```
In [26]: pd.DataFrame(np.array([[1, np.nan], [2, 3]]), dtype='int64')
Out[26]:
0 1
0 1 -9223372036854775808
1 2 3
In [27]: pd.DataFrame(np.array([['2300-01-01']], dtype='datetime64[D]'), dtype='datetime64[ns]')
Out[27]:
0
0 1715-06-13 00:25:26.290448384
```
Both cases are guarded in DataFrame.astype:
```
In [29]: pd.DataFrame(np.array([[1, np.nan], [2, 3]])).astype(dtype='int64')
...
~/scipy/pandas/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
678
679 if not np.isfinite(arr).all():
--> 680 raise ValueError('Cannot convert non-finite values (NA or inf) to '
681 'integer')
682
ValueError: Cannot convert non-finite values (NA or inf) to integer
In [30]: pd.DataFrame(np.array([['2300-01-01']], dtype='datetime64[D]')).astype(dtype='datetime64[ns]')
...
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2300-01-01 00:00:00
```
I suppose we want to do such a safe astype in DataFrame constructor itself as well? | {
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} | 1 | 2019-06-18T13:46:09Z | 2019-06-21T21:18:31Z | 2019-06-21T21:18:27Z | NONE | null | #### Issue: Using variable lookback-lengths in pandas methods
Per underlying stock asset, I utilize a homodyne discriminator derived from close prices which determines the appropriate lookback length for other indicators such as macd, smart volume, ehlers rsi, etc.
When I retrieve history on an asset, the data arrives in bulk. I want to apply my indicators en-masse to the entire dataset instead of having to iterate through each row in order to generate the appropriate values (lookback period, then indicator values). That means ideally I would create the 'lookback period' column on the entire dataset through a homodyne discriminator method/class and subsequently apply all of my indicator classes to the same dataset, most of which may depend on the lookback period.
Given the current implementation of ewm/rolling which seemingly accept only a static integer/float, all of my calculations will be off.
#### Ideal code which generates the error:
```python
import pandas as pd
import numpy as np
# Initialize dataframe with source data
df = pd.DataFrame()
df['source'] = pd.Series(range(100, 200))
# Typically 'period' is generated by some class
# such as a homodyne discriminator for which
# other classes depend on for lookback length
df['period'] = np.nan
df.period.iloc[0] = 1
df.period.iloc[-1] = 10
df.period.interpolate(inplace=True)
df.period = df.period.astype('int64', copy=False)
print(df)
# A given class that depends on the lookback
# length determined by the discriminator
df['dependant'] = df.source.ewm(span=df.period, adjust=False).mean()
print(df)
```
#### Error:
> ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1084-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26921/events | https://github.com/pandas-dev/pandas/issues/26921 | 457,500,347 | MDU6SXNzdWU0NTc1MDAzNDc= | 26,921 | pandas.DataFrame.plot error for time series data | {
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... | open | false | null | [] | null | 3 | 2019-06-18T14:00:13Z | 2020-05-31T08:18:13Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
idx = pd.date_range(start="2018", freq=pd.DateOffset(days=1), periods=2)
ts = pd.DataFrame({"A": [1, 2]}, idx)
ts.plot()
```
#### Problem description
I created the index of a time series using pd.date_range and provided the frequency as a `DateOffset` object. This index was passed to the `DataFrame` constructor to create a time series. When calling the `plot` method on this time series, pandas is not able to parse the frequency correctly:
```
233 if isinstance(freq, DateOffset):
--> 234 freq = freq.rule_code
235 else:
236 freq = get_base_alias(freq)
~/venv/lib/python3.6/site-packages/pandas/tseries/offsets.py in rule_code(self)
372 @property
373 def rule_code(self):
--> 374 return self._prefix
375
376 @cache_readonly
~venv/lib/python3.6/site-packages/pandas/tseries/offsets.py in _prefix(self)
368 @property
369 def _prefix(self):
--> 370 raise NotImplementedError('Prefix not defined')
371
372 @property
```
#### Expected Output
When passing the frequency to `pd.date_range` as a string, everything works fine, however both indices are equal.
```python
idx2 = pd.date_range(start="2018", freq="D", periods=2)
ts = pd.DataFrame({"A": [1, 2]}, idx2)
ts.plot()
idx.equals(idx2)
```
Output: `True`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-10-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.6
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26922/events | https://github.com/pandas-dev/pandas/issues/26922 | 457,506,889 | MDU6SXNzdWU0NTc1MDY4ODk= | 26,922 | Link from, for e.g., version/0.22/generate, to stable/reference | {
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] | open | false | null | [] | null | 2 | 2019-06-18T14:12:02Z | 2019-06-18T19:10:34Z | null | NONE | null | Google often drops me in specific documentation from a single release, rather than the stable reference. For example the google search [pandas groupby aggregate](https://www.google.com/search?safe=active&rlz=1C1CHBD_en-GBIE841IE842&ei=4eMIXbCFKaXD8gKrzJLoBg&q=pandas+groupby+aggregate&oq=pandas+groupby+aggregat&gs_l=psy-ab.3.0.0i67j0l9.4663.11142..13900...1.0..0.52.506.10......0....1..gws-wiz.......0i71j35i39j0i20i263.BPBJmUg8WD8) had these two as top results:
- https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.core.groupby.DataFrameGroupBy.agg.html
- https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.core.groupby.DataFrameGroupBy.agg.html
Neither of those pages include an easy way to get back to the main version of the page (is it https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html ?)
It would be useful if it were easier to get from a specific version of the docs to the stable reference version. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26923/events | https://github.com/pandas-dev/pandas/pull/26923 | 457,511,195 | MDExOlB1bGxSZXF1ZXN0Mjg5MzA1MjMz | 26,923 | Calculate normalized freqs in value_counts correctly when bins is not None | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26924/events | https://github.com/pandas-dev/pandas/issues/26924 | 457,512,284 | MDU6SXNzdWU0NTc1MTIyODQ= | 26,924 | DOC: Disallow crawlers in development docs | {
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} | 1 | 2019-06-18T14:21:51Z | 2019-06-18T23:57:08Z | 2019-06-18T23:57:08Z | MEMBER | null | (continiung the discussion in https://github.com/pandas-docs/pandas-docs-travis/pull/3#issuecomment-503148283)
Afaik we're currently not telling search engine which is the copy of the documentation they should be indexing. They seem to be smart enough, and they've been returning the right restults so far, but given that we're now changing the dev documentation to dev.pandas.io, it's probably worth to tell them not to index that copy.
It should be as easy as adding a `robots.txt` in the root of the dev docs repo with this content:
```
User-agent: *
Disallow: /
```
See: https://en.wikipedia.org/wiki/Robots_exclusion_standard
This can be easily done adding another line similar to the one used for the `CNAME`:
https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml#L148
@TomAugspurger @jorisvandenbossche does this make sense? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26925/events | https://github.com/pandas-dev/pandas/issues/26925 | 457,520,817 | MDU6SXNzdWU0NTc1MjA4MTc= | 26,925 | Index dtype is changed to object after left join | {
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} | 11 | 2019-06-18T14:36:38Z | 2021-07-10T05:02:48Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
left = pd.DataFrame(
columns=['A'],
index=pd.Index([], name='id', dtype=np.int64)
)
right = pd.DataFrame(
[[5]],
columns=['B'],
index=pd.Index([42], name='id', dtype=np.int64)
)
res_idx_dtype = left.join(right).index.dtype
assert res_idx_dtype == np.int64
```
#### Problem description
When executing a left join on two dataframes I would expect the resulting index type to be of the same type my left dataframe was.
I ran into this issue while using dask. I executed another join on the resulting dataframe which resulted in an error, because the index was of type object instead of np.int64.
#### Expected Output
```
>>> left.join(right).index.dtype
dtype('int64')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.3
pytest: None
pip: 9.0.3
setuptools: 40.4.3
Cython: 0.29.10
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.9
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.1
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26926/events | https://github.com/pandas-dev/pandas/issues/26926 | 457,523,713 | MDU6SXNzdWU0NTc1MjM3MTM= | 26,926 | STY: use black | {
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} | 11 | 2019-06-18T14:41:47Z | 2019-07-07T11:23:58Z | 2019-07-07T11:23:58Z | CONTRIBUTOR | null | I have seen a number of other project adopt: https://pypi.org/project/black/, as well as use in my company.
This basically is a style formatter that plays nice with flake8 and is completely opionated, the only option is line-length. This is a good thing.
This would involve a one-time updating of code (though we can do it somewhat incrementally). | {
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} | 8 | 2019-06-18T15:28:57Z | 2021-11-13T17:32:21Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({
'A': [0, 1, 2], 'B': ['a', 'b', 'c'], 'C': [4, 5, 6]
})
df.mean(axis=0, numeric_only=False, skipna=False)
```
#### Problem description
Instead of outputting a `NaN` for a non-numeric column of strings when trying to take the mean, it instead throws `TypeError: could not convert string 'abc' to float`.
#### Expected Output
I would expect this to output a series with values `[1, NaN, 5]`.
My work-around is currently `df.apply(pd.to_numeric, args=['coerce']).mean(axis=0, skipna=False)`, which outputs the expected result.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.16.0
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details> | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26928/events | https://github.com/pandas-dev/pandas/issues/26928 | 457,591,064 | MDU6SXNzdWU0NTc1OTEwNjQ= | 26,928 | ENH: Add circular statistics methods for DataFrames and Series | {
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} | [] | closed | false | null | [] | null | 3 | 2019-06-18T16:52:26Z | 2019-06-18T18:51:16Z | 2019-06-18T18:51:16Z | NONE | null | Let me know if this is out of scope, but it would be nice to perform typical statistics calculations on circular quantities with a similar interface as, e.g., `mean` --> `circmean`, including the `level` arg. It would also be great to be able to note that the input is in degrees to avoid having to convert back and forth from radians.
```python
mean_dir = df.direction_deg.circmean(degrees=True, level="time")
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26929/events | https://github.com/pandas-dev/pandas/issues/26929 | 457,608,165 | MDU6SXNzdWU0NTc2MDgxNjU= | 26,929 | BUG: DataFrame.shift with axis=1 shifts object columns to the next column with object dtype | {
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"description": "Non-arithmetic algos: va... | closed | false | null | [] | null | 8 | 2019-06-18T17:32:55Z | 2020-09-26T22:58:41Z | 2020-09-26T22:58:41Z | NONE | null | #### Code Sample
```python2.7
import pandas as pd
import StringIO
# Store the csv string in a variable
gps_string = """
"2010-01-12 18:00:00","$GPGGA","180439","7249.2150","N","11754.4238","W","2","10","0.9","-8.1","M","-12.4","M","","*57","","",""
"2010-01-12 17:30:00","$GPGGA","173439","7249.2160","N","11754.4233","W","2","11","0.8","-4.5","M","-12.4","M","","*5B","","",""
"2010-01-12 17:00:00","$GPGGA","170439","7249.2152","N","11754.4235","W","2","11","0.8","-3.1","M","-12.4","M","","*5C","","",""
"2010-01-12 16:30:00","N","11754.4210","W","2","09","1.1","-13.1","M","-12.4","M","","*6C","","","","","",""
"2010-01-12 16:00:00","N","11754.4229","W","2","10","0.9","-2.9","M","-12.4","M","","*53","","","","","",""
"2010-01-12 15:30:00","N","11754.4269","W","2","09","0.8","-4.3","M","-12.4","M","","*54","","","","","",""
"2010-01-12 15:00:00","N","11754.4267","W","2","10","0.8","-1.6","M","-12.4","M","","*56","","","","","",""
"2010-01-12 14:30:00","$GPGGA","143439","7249.2152","N","11754.4253","W","2","11","0.7","-4.3","M","-12.4","M","","*56","","",""
"2010-01-12 14:00:00","N","11754.4245","W","2","10","0.9","-7.0","M","-12.4","M","","*50","","","","","",""
"2010-01-12 13:30:00","$GPGGA","133439","7249.2134","N","11754.4243","W","2","11","0.7","-10.7","M","-12.4","M","","*61","","",""
"2010-01-12 13:00:00","N","11754.4245","W","2","10","0.8","-5.5","M","-12.4","M","","*56","","","","","",""
"2010-01-12 12:30:00","N","11754.4226","W","2","10","0.9","-7.1","M","-12.4","M","","*59","","","","","",""
"2010-01-12 12:00:00","N","11754.4238","W","2","10","0.8","-6.5","M","-12.4","M","","*51","","","","","",""
"2010-01-12 11:30:00","N","11754.4227","W","2","10","0.8","0.1","M","-12.4","M","","*73","","","","","",""
"2010-01-12 11:00:00","-7.4","M","-12.4","M","","*5F","","","","","","","","","","","",""
"2010-01-12 10:30:00","N","11754.4271","W","2","08","1.1","-8.4","M","-12.4","M","","*5A","","","","","","" """
# Read the csv string into a dataframe.
gps_df = pd.read_csv(StringIO.StringIO(gps_string), header=None, index_col=0)
rows_to_shift = gps_df[gps_df[15].isnull()].index
gps_df.loc[rows_to_shift] = gps_df.loc[rows_to_shift].shift(periods=1, axis=1)
# create this file to see the outcome
gps_df.to_csv("f.csv")
```
#### Problem description
I am trying to use the ```Dataframe. shift()``` function to move certain rows of data into their correct columns, and the ```Dataframe.shift()``` function is doing some weird things, it is creating an empty column of ```null(s)``` and moves one of the columns to the end of the ```dataframe```.
**Screenshots**
Original data:
<img width="1268" alt="Screen Shot 2019-06-18 at 4 09 22 PM" src="https://user-images.githubusercontent.com/24297785/59721139-3c332080-91e6-11e9-9468-fc876234ecaa.png">
Output after execution of the code
<img width="1250" alt="Screen Shot 2019-06-18 at 4 31 15 PM" src="https://user-images.githubusercontent.com/24297785/59721303-9d5af400-91e6-11e9-9cdb-6988a32e9720.png">
As seen above, the data that was originally in column 10 has been move to column 15 for some reason. Also a column with ```null``` values has been created in column 6.
I expect that the data should be moved to the right by one step, that is the data in each column should move to the column to the left of it, and the current behaviour is confusing, based on the documentation of what this function should do.
My expected output:
<img width="1227" alt="Screen Shot 2019-06-18 at 4 16 40 PM" src="https://user-images.githubusercontent.com/24297785/59721366-ced3bf80-91e6-11e9-9330-7889d4afffc2.png">
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.final.0
python-bits: 32
OS: Windows
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26930/events | https://github.com/pandas-dev/pandas/issues/26930 | 457,609,179 | MDU6SXNzdWU0NTc2MDkxNzk= | 26,930 | CI: Write azure failed builds summary as a GitHub comment | {
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"description": "Continuo... | open | false | null | [] | null | 0 | 2019-06-18T17:35:19Z | 2021-07-10T05:04:16Z | null | MEMBER | null | I think it'd safe us a significant amount of time, if when a build fails, we get a nice github comment with the problems. The same idea as what pep8speaks does for linting, but for most things.
I think in some cases it'll save us from having to navigate to the logs (which in azure is even more complex than needed, see #26895).
In the cases when we still need to go to the logs for full tracebacks or whatever, we should be able to have direct links to the log in the comment.
For first-time contributors, the comment will generate an email with the exact problem, and they should be able to follow up and leave the CI green without our intervention. This can be very useful for changes to the docstrings. So, when we go and review PRs, there are no formatting mistakes...
Ideally, azure-pipelines should provide it, for them would be quite trivial to implement, since they can get the permissions to add comments as a GitHub app. I created https://github.com/microsoft/azure-pipelines-tasks/issues/10665 and ask PMs couple of times, but doesn't seem this will happen.
The alternative is a bit trickier, which is having a small web app (like 100 lines long flask script) in a server, and add a webhook that calls it after the build finishes (this can be done from either github or azure). The app can do a REST call to azure to get the log content, and the rest should be a simple regex to get the errors.
Let me know if anyone thinks this is not a good idea, or you know of a better solution. For Travis there seems to be an app that does this (or something similar), https://www.travisbuddy.com/, but I'm not sure if there is something for azure. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26931/events | https://github.com/pandas-dev/pandas/pull/26931 | 457,679,198 | MDExOlB1bGxSZXF1ZXN0Mjg5NDM5MzI2 | 26,931 | DOC: Disallowing search engines in the development docs | {
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} | 3 | 2019-06-18T20:25:05Z | 2019-06-19T08:20:58Z | 2019-06-18T23:57:08Z | MEMBER | null | - [X] closes #26924
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"d... | closed | false | null | [] | null | 11 | 2019-06-18T21:17:54Z | 2019-06-20T10:41:28Z | 2019-06-20T10:41:27Z | MEMBER | null | - [X] closes #26887
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
With some luck this should make the clipboard tests work in azure. @jorisvandenbossche @jreback can you have a look once the CI finishes? | {
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Adjusted titles in Docs to have consistent first letter only capitalization, unless proper noun in title.
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} | 4 | 2019-06-19T01:25:47Z | 2019-07-17T11:50:47Z | 2019-07-17T11:50:47Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame({'Int': [1, 2, 3], 'Period': pd.period_range(start="2019-01", end="2019-03", freq="M")})
df.to_csv("PeriodDtype.csv")
pd.read_csv("PeriodDtype.csv", dtype={"Int": np.int64, "Period": pd.PeriodDtype("M")})
```
#### Problem description
Using pandas 0.24.2, I wrote a simple data frame with the following dtypes into a csv file,
```
Int int64
Period period[M]
dtype: object
```
When I tried to read it back in, I found that `read_csv()` could not parse `PeriodDtype("M")`. I got the following error message:
```
NotImplementedError: Extension Array: <class 'pandas.core.arrays.period.PeriodArray'>
must implement _from_sequence_of_strings in order to be used in parser methods
```
I saw a similar issue #24542 raised for Datetime dtype. It seems that `_from_sequence_of_strings()` is also not defined for `PeriodArray`, which prevents parsing columns with `PeriodDtype`.
I think adding `_from_sequence_of_strings()` for `PeriodArray` would be a good enhancement. If that is the case I would be interested in making that change.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.8.0
pip: 10.0.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 5 | 2019-06-19T02:25:25Z | 2019-08-02T15:44:13Z | 2019-07-08T01:49:52Z | NONE | null | What I'd like to have is:
1. a single, unified interface for dispatching Series methods
2. which supports EA
3. for all methods
4. and allows every method to be overridden by EA, as-needed
5. supports NA
6. doesn't require EA authors to implement some methods to signature-compatible with numpy (`round`), and signature compatible with a competing pandas version in other (`sum` via `_reduce`)
7. Clearly namespaces pandas functions, which also indicates cooperation/opt-in (perhaps via duck typing), so that
7a. its clear which methods implements pandas interface
7b. there are no methods which are named like a numpy method, but with a pandas signature, or vice versa
7c. when reading an EA's code, you don't have to guess or figure out which code is
a pandas method and which is an internal method for the EA (like the poorly named `_reduce`)
8. provides good error messages for unimplemented methods. | {
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} | 4 | 2019-06-19T02:31:44Z | 2019-06-21T04:11:23Z | 2019-06-21T01:48:23Z | CONTRIBUTOR | null | - [x] xref #26792
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-06-19T03:39:41Z | 2019-06-28T18:59:24Z | 2019-06-20T16:15:54Z | NONE | null | This supercedes #26894, which made `Interval()` raise an exception on attempts to create of the form `[x,x)` or `(x,x]`.
- [X] closes #26893
- [x] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Discussion in #26893 suggests we may want to treat empty intervals as Singletons in the future. But for now, just fix overlaps.
*update*: `IntervalArray` has its own vectorized implementation of `overlaps`, fixed and now shares the new test cases I added for `Interval.overlaps`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26938 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26938/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26938/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26938/events | https://github.com/pandas-dev/pandas/issues/26938 | 457,907,635 | MDU6SXNzdWU0NTc5MDc2MzU= | 26,938 | pivot_table raises DataError: No numeric types to aggregate with Timedelta | {
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... | closed | false | null | [] | null | 4 | 2019-06-19T09:35:32Z | 2021-02-11T15:12:20Z | 2019-06-19T10:26:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"Name": ["A", "B", "C", "B", "A", "C"], "Laptime": [48.0,48.0,48.0,48.0,48.0,48.0], "Time": [12.0, 14, 16, 62, 60, 64]})
In [3]: df.dtypes
Out[3]:
Name object
Laptime float64
Time float64
dtype: object
In [4]: pd.pivot_table(df, values="Laptime", index="Time", columns="Name")
```
returns
```
Name A B C
Time
12.0 48.0 NaN NaN
14.0 NaN 48.0 NaN
16.0 NaN NaN 48.0
60.0 48.0 NaN NaN
62.0 NaN 48.0 NaN
64.0 NaN NaN 48.0
```
but when columns and/or index are `Timedelta`, `pivot_table` raises an exception
```
In [5]: df = pd.DataFrame({"Name": ["A", "B", "C", "B", "A", "C"], "Laptime": [48.0,48.0,48.0,48.0,48.0,48.0], "Time": [12.0, 14, 16, 62, 60, 64]})
In [6]: for colname in ["Laptime", "Time"]:
df[colname] = pd.to_timedelta(df[colname], unit='s')
In [7]: df.dtypes
Out[7]:
Name object
Laptime timedelta64[ns]
Time timedelta64[ns]
dtype: object
In [8]: pd.pivot_table(df, values="Laptime", index="Time", columns="Name")
```
it raises
DataError: No numeric types to aggregate
#### Problem description
raises:
DataError: No numeric types to aggregate
#### Expected Output
shouldn't raises exception but should return
```
Name A B C
Time
00:00:12 00:00:48 NaT NaT
00:00:14 NaT 00:00:48 NaT
00:00:16 NaT NaT 00:00:48
00:00:60 00:00:48 NaT NaT
00:00:62 NaT 00:00:48 NaT
00:00:64 NaT NaT 00:00:48
```
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
/Users/scls/anaconda3/lib/python3.7/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: 0.9.3
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26939 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26939/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26939/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26939/events | https://github.com/pandas-dev/pandas/issues/26939 | 457,939,980 | MDU6SXNzdWU0NTc5Mzk5ODA= | 26,939 | BUG: cannot set Series with reverse slicer | {
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"description": "Related to indexing on s... | closed | false | null | [] | null | 2 | 2019-06-19T10:46:29Z | 2019-08-28T23:01:47Z | 2019-08-28T23:01:47Z | CONTRIBUTOR | null | Minimal example:
```
>>> import pandas as pd
>>> s = pd.Series(index=range(2010, 2020))
>>> s.loc[2015:2010:-1] = [6, 5, 4, 3, 2, 1]
Traceback (most recent call last):
[...]
ValueError: cannot set using a slice indexer with a different length than the value
```
I see no reason why this shouldn't work, as setting with the forward slicer works without problems, and *getting* with the reverse slicer also works without issue:
```
>>> # turn list around because slicer is (not) reversed compared to above
>>> s.loc[2010:2015] = [6, 5, 4, 3, 2, 1][::-1]
>>> s
2010 1.0
2011 2.0
2012 3.0
2013 4.0
2014 5.0
2015 6.0
2016 NaN
2017 NaN
2018 NaN
2019 NaN
dtype: float64
>>> s.loc[2015:2010:-1] == [6, 5, 4, 3, 2, 1] # comparison, not assignment
2015 True
2014 True
2013 True
2012 True
2011 True
2010 True
dtype: bool
```
PS: For the failure, it does not matter if the RHS is a np.array, etc.
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} | 5 | 2019-06-19T11:44:17Z | 2019-07-11T15:47:45Z | 2019-06-21T01:19:27Z | CONTRIBUTOR | null | - [x] fixes MemoryError issue in #26005
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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... | null | 12 | 2019-06-19T13:16:37Z | 2020-03-07T20:06:26Z | 2020-03-07T20:06:26Z | MEMBER | null | In #26933, we're making the capitalization of the title sections consisten. We use to have many titles capitalized as `This is the Section Title`, and we changed all them (probably few were forgotten) to `This is the section title`.
To keep this consistency, we should validate that the capitalization is correct in the CI. This can be done by extracting all the titles, and making sure that only the first letter of the sentence is uppercase, or words defined in a short list, like `Series`, `DataFrame`,...
I think this can be done in two ways:
- As a sphinx extension that validates the titles as they are processed, and generates warnings if they are not (this will automatically fail the CI).
- As an independent script
The first option should be simpler if sphinx can implement this as extension, but not sure if that's the case. | {
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} | 4 | 2019-06-19T14:05:43Z | 2019-06-20T21:26:07Z | 2019-06-20T20:45:41Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26943 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26943/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26943/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26943/events | https://github.com/pandas-dev/pandas/issues/26943 | 458,039,800 | MDU6SXNzdWU0NTgwMzk4MDA= | 26,943 | pandas.read_hdf leaves file open when requested data key does not exist | {
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```python
import pandas
data = pandas.read_hdf(<h5_file_name>, mode='r', key=key)
```
#### Problem description
When trying to read HDF5 file and the requested key object does not exist in the file, pandas pytables raises `KeyError('No object named {key} in the file'.)` and does not close the opened file. I would expect that the HDF5 file is closed after trying to read the key object, whether it was successful or not.
Trying to write to the file which is opened in read mode fails with exception.
The problematic code is in `pandas.io.pytables.py` file, method `select`, line 712.
#### Workaround
Check if the key exists before trying to read it from file.
```python
import h5py
key_exists = True
with h5py.File(<h5_file_name>, mode='r') as file:
group = file.get(key)
if not group:
key_exists = False
if key_exists:
...
```
Should you need more details, please let me know. | {
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} | 5 | 2019-06-19T14:26:43Z | 2019-07-01T18:19:17Z | 2019-07-01T18:19:17Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
idx = pd.IndexSlice
dt_index = pd.date_range(start="1jan2019 00:15:33",periods=100,freq="h") #this will fail
#dt_index = pd.date_range(start="1jan2019",periods=100,freq="d") #this works
zf = pd.DataFrame(index=dt_index)
zf['foo']=10
zf['bar']="squat"
zf['zaa']=range(len(dt_index))
zf.index.name="date"
zf = zf.reset_index().set_index(["date", "bar", "zaa"])
zf.loc[idx['2019-1-1':'2019-1-3',"squat",:],:] # This works ok
zf.loc[idx['2019-1-1':,"squat",:],:] # This fails AttributeError: 'int' object has no attribute 'stop'
zf.loc[idx['2019-1-1':None,'squat',:],:] # AttributeError: 'int' object has no attribute 'stop'
```
#### Problem description
The same slice of a multindex fails if the index is DateTimes but works if the index is Dates-only.
This occurs in pandas 0.24.2
A [related-sounding ticket ](https://github.com/pandas-dev/pandas/issues/10331)said if I have a reproducible example, post a new ticket.
#### Expected Output
same output as if I'd sliced a dates-only index (provided in the example above)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 12 | 2019-06-19T14:35:50Z | 2019-07-23T20:46:44Z | 2019-07-23T20:46:28Z | CONTRIBUTOR | null | Should this align before doing the ufunc?
```python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: s1 = pd.Series([1, 2, 3])
In [4]: s2 = s1.iloc[[0, 2, 1]]
In [5]: np.add(s1, s2)
Out[5]:
0 2
1 5
2 5
dtype: int64
```
IMO, the expected output should be
```
In [6]: np.add(*s1.align(s2))
Out[6]:
0 2
1 4
2 6
dtype: int64
```
cc @shoyer, @jbrockmendel, @jorisvandenbossche | {
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In [1]: import pandas as pd; import numpy as np
In [2]: sparse = pd.SparseArray([0.0, 3.5], fill_value=0.0)
...: r1, r2 = np.modf(sparse)
...:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-1f85037f7dc7> in <module>
1 sparse = pd.SparseArray([0.0, 3.5], fill_value=0.0)
----> 2 r1, r2 = np.modf(sparse)
~/sandbox/pandas/pandas/core/arrays/sparse.py in __array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1700 return self._simple_new(sp_values,
1701 self.sp_index,
-> 1702 SparseDtype(sp_values.dtype, fill_value))
1703
1704 result = getattr(ufunc, method)(*[np.asarray(x) for x in inputs],
AttributeError: 'tuple' object has no attribute 'dtype'
```
PR coming soon. | {
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} | 3 | 2019-06-19T15:19:48Z | 2019-06-19T18:40:58Z | 2019-06-19T17:57:41Z | CONTRIBUTOR | null | Closes #26946
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] | closed | false | null | [] | null | 2 | 2019-06-19T16:25:13Z | 2020-01-21T04:28:47Z | 2020-01-21T04:28:47Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
c = a.apply(fixsub, axis=1)
c = c.fillna("nan")
c.to_csv('solution2.csv', header=False, index=False)
```
#### Problem description
Even after putting header=False, header keeps coming in the csv
#### Expected Output
no header should come in the csv file
#### Output of ``pd.show_versions()``
0.24.2
### How to fix
culprit code in pandas/io/formats/csvs.py
```python
def _save_header(self):
writer = self.writer
obj = self.obj
index_label = self.index_label
cols = self.cols
has_mi_columns = self.has_mi_columns
header = self.header
encoded_labels = []
has_aliases = isinstance(header, (tuple, list, np.ndarray,
ABCIndexClass))
if not (has_aliases or self.header): ## BUG HERE FIX NEEDED
return
```
[paste the output of ``pd.show_versions()`` here below this line]
0.24.2
</details>
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"d... | closed | false | null | [] | null | 9 | 2019-06-19T17:12:24Z | 2019-09-08T15:59:55Z | 2019-09-08T15:59:55Z | MEMBER | null | Follow up of the conversation in #26932, and fixing some of the things that #24106 tried.
The main change is **call pytest once** instead of once for `single` and once for `multi`. This shouldn't be a problem since I add the parameter `--dist=loadfile` which should prevent tests in the same file running in parallel. I don't think running `serial` tests in parallel with tests in other files should be a problem.
Other things cleaned in the PR:
- Setting always the proxy (shouldn't hurt for not network tests, and makes things simpler)
- Using as many processes as cores, not 2
- Removed hack for when no tests were found (since we don't call only for `single` tests, there will always be tests to run)
So far testing, let's see how does this work in the CI. | {
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} | 7 | 2019-06-19T17:24:26Z | 2019-06-27T21:25:13Z | 2019-06-27T21:25:03Z | CONTRIBUTOR | null | - [X] closes #26830
This superseeds #26933, and should contain all the fixes requested there.
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} | 10 | 2019-06-19T18:02:20Z | 2019-06-21T15:58:34Z | 2019-06-21T15:58:29Z | CONTRIBUTOR | null | This adds a set of tests for ufuncs on Series. The goal is to
establish the correct behavior prior to implementing `Series.__array_ufunc__`.
There are two kinds of xfails right now
1. Series[Sparse] fails because `Series.__array_ufunc__` doesn't
yet dispatch to `Series.array.__array_ufunc__`
2. `ufunc(series, series)` when the two series are unaligned. It's
been determined that these should align, but isn't currently
implemented.
LMK if there's anything I can do to make these tests clearer. The heavy parameterization is a bit unfortunate, but I think the best option.
xref https://github.com/pandas-dev/pandas/pull/23293 | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-19T22:00:22Z | 2019-06-20T11:48:14Z | 2019-06-20T11:48:14Z | CONTRIBUTOR | null | #### Problem description
In the pull request #26940, I have changed a piece of code in `pandas/_libs/window.pyx`. Tests pass successfully (including those in tests/api) on my local but not on CI. It seems that `hard_dependencies, dependency, missing_dependencies` are on the pd namespace and not deleted as supposed (see [here](https://github.com/pandas-dev/pandas/blob/master/pandas/__init__.py#L17)). But I don't think changes in #26940 cause this assertion error.
#### Error ([Here](https://travis-ci.org/pandas-dev/pandas/jobs/547916861#L2443))
```python
=================================== FAILURES ===================================
______________________________ TestPDApi.test_api ______________________________
[gw1] linux -- Python 3.7.3 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.api.test_api.TestPDApi object at 0x7f2145f522e8>
def test_api(self):
self.check(pd,
self.lib + self.misc +
self.modules + self.deprecated_modules +
self.classes + self.deprecated_classes +
self.deprecated_classes_in_future +
self.funcs + self.funcs_option +
self.funcs_read + self.funcs_to +
self.deprecated_funcs_in_future +
self.deprecated_funcs + self.private_modules,
> self.ignored)
pandas/tests/api/test_api.py:113:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/api/test_api.py:18: in check
tm.assert_almost_equal(result, expected)
pandas/util/testing.py:325: in assert_almost_equal
**kwargs)
pandas/_libs/testing.pyx:65: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, '{0} length are different'.format(obj),
E AssertionError: Iterable are different
E
E Iterable length are different
E [left]: 132
E [right]: 129
E [diff]: ['dependency', 'missing_dependencies', 'hard_dependencies']
pandas/_libs/testing.pyx:163: AssertionError
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26953 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26953/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26953/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26953/events | https://github.com/pandas-dev/pandas/pull/26953 | 458,294,353 | MDExOlB1bGxSZXF1ZXN0Mjg5OTMzNjQw | 26,953 | TST: Fix flaky import test | {
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"d... | closed | false | null | [] | null | 6 | 2019-06-20T01:38:08Z | 2019-06-20T11:48:18Z | 2019-06-20T11:48:14Z | CONTRIBUTOR | null | I'm not sure why, but the missing dependency test is
causing issues. Now we check that things work by running
it in a subprocess with site-packages disabled.
Closes https://github.com/pandas-dev/pandas/issues/26952 | {
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"description": "Extendin... | closed | false | null | [] | null | 24 | 2019-06-20T01:52:41Z | 2021-11-20T23:23:22Z | 2019-07-01T20:57:14Z | MEMBER | null | [ci skip]
Companion to #26914 trying out a way to implement 2D methods for EA subclasses that don't do it natively.
Chose the arrow EA as the example to start with, then discovered that equality checks return a scalar instead of operating elementwise, so mostly just comparing the shapes. | {
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"description": "__repr__ of pandas ob... | open | false | null | [] | null | 3 | 2019-06-20T02:57:46Z | 2021-07-10T05:08:10Z | null | NONE | null | Beginning at line `In [93]:` in getting_started/10min.html, the column headers of the output data frames go out of alignment.
Is this meant to have a semantic meaning that is not addressed by the tutorial commentary?
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
df.groupby('A').sum()
```
#### Problem description
Output column headers do not line up as expected
C D
A
bar -2.802588 2.42611
foo 3.146492 -0.63958
#### Expected Output
A C D
bar -2.802588 2.42611
foo 3.146492 -0.63958
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 2 | 2019-06-20T06:18:12Z | 2019-06-20T06:59:23Z | 2019-06-20T06:58:53Z | MEMBER | null | From discussion in https://github.com/pandas-dev/pandas/pull/26914. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26958 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26958/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26958/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26958/events | https://github.com/pandas-dev/pandas/issues/26958 | 458,410,904 | MDU6SXNzdWU0NTg0MTA5MDQ= | 26,958 | ENH: provide nunique method on Rolling object | {
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"description": "rolling,... | open | false | null | [] | null | 0 | 2019-06-20T07:58:10Z | 2019-06-20T12:26:39Z | null | MEMBER | null | GroupBy objects expose a `nunique` method of Series/DataFrame (and also for `resample` it works), but Rolling does not:
```
In [89]: values = np.random.choice(np.arange(10), 100)
In [90]: s = pd.Series(values, index=pd.date_range("2019-01-01", freq='H', periods=100))
In [92]: s.groupby(pd.Grouper(freq='D')).nunique()
Out[92]:
2019-01-01 10
2019-01-02 8
2019-01-03 10
2019-01-04 10
2019-01-05 4
Freq: D, dtype: int64
```
vs
```
In [93]: s.rolling('D').apply(pd.Series.nunique, raw=False)
Out[93]:
2019-01-01 00:00:00 1.0
2019-01-01 01:00:00 2.0
2019-01-01 02:00:00 3.0
...
# or
In [95]: s.rolling('D').apply(lambda x: len(pd.unique(x)), raw=True)
Out[95]:
2019-01-01 00:00:00 1.0
2019-01-01 01:00:00 2.0
2019-01-01 02:00:00 3.0
...
```
I think it would be nice to also have this method available on `rolling` objects.
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https://api.github.com/repos/pandas-dev/pandas/issues/26959 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26959/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26959/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26959/events | https://github.com/pandas-dev/pandas/issues/26959 | 458,421,410 | MDU6SXNzdWU0NTg0MjE0MTA= | 26,959 | API? how to do a "rolling groupby" or groupby with overlapping groups? | {
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... | open | false | null | [] | null | 11 | 2019-06-20T08:21:41Z | 2020-06-17T10:24:22Z | null | MEMBER | null | Sparked by @betatim's question on twitter: https://twitter.com/betatim/status/1141321049918906368
Suppose you have the following data:
```
repo_id = np.random.choice(np.arange(1000), 10000)
index = pd.Timestamp("2019-01-01") + pd.to_timedelta(np.random.randint(0, 24*30*6, size=10000), unit='H')
df = pd.DataFrame({'repo_id': repo_id}, index=index).sort_index()
```
```
In [111]: df.head()
Out[111]:
repo_id
2019-01-01 01:00:00 162
2019-01-01 01:00:00 850
2019-01-01 01:00:00 414
2019-01-01 02:00:00 753
2019-01-01 02:00:00 125
```
Data at somewhat randomly points in time (here hourly, but precision doesn't matter much).
Assume now you want to calculate a rolling 30D (monthly) statistic for each day.
For example the rolling number of unique values over a period of time (the elegant but somewhat verbose python code to do this: https://gist.github.com/betatim/c59039682d92fab89859358e8c585313)
A rolling operation does not exactly give what you want, because `df.rolling()` will calculate this 24H mean for each row, while there might be many rows for a single day, and you are only interested in 1 value for that day.
A groupby/resample operation can also not achieve this goal, as that could give monthly number of unique values, but it cannot shift this month period one day at a time, since the groups cannot be overlapping.
With rolling you can do something like `df.rolling("30D").nunique().resample('D').last()`. But this is 1) very slow, as the `rolling` in the first step is doing way too many calculations that you afterwards throw away with `resample().last()`, and 2) I am not sure the binning for timestamps within the same day happens fully as desired.
I found this an interesting use case, to think about if there are ways to better handle this in pandas, or how such an API could look like. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26960 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26960/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26960/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26960/events | https://github.com/pandas-dev/pandas/issues/26960 | 458,558,039 | MDU6SXNzdWU0NTg1NTgwMzk= | 26,960 | Why do I get strange aggregation result from DataFrame groupby()? | {
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] | closed | false | null | [] | null | 1 | 2019-06-20T09:55:57Z | 2019-06-20T12:22:00Z | 2019-06-20T12:22:00Z | NONE | null | #### Pandas version: 0.22
#### Problem
I try to use the numpy.size() to count the group size for the groups from pandas Dataframe groupby(), and I get strange result.
Can any guys explain how the aggregation of groupby works?
```python
>>> df=pd.DataFrame({'A':[1,1,2,2], 'B':[1,2,3,4],'C':[0.11,0.32,0.93,0.65],'D':["This","That","How","What"]})
>>> df
A B C D
0 1 1 0.11 This
1 1 2 0.32 That
2 2 3 0.93 How
3 2 4 0.65 What
>>> df.groupby('A',as_index=False).agg(np.size)
A B C D
0 1 2 2.0 2
1 2 2 2.0 2
>>> df.groupby('A',as_index=False)['C'].agg(np.size)
A C
0 1 8
1 2 8
>>> df.groupby('A',as_index=False)[['C']].agg(np.size)
A C
0 1 2.0
1 2 2.0
>>> grouped=df.groupby('A', as_index=False)
>>> grouped['C','D'].agg(np.size)
A C D
0 1 2.0 2
1 2 2.0 2
```
In the code, if we use groupby() following **['C']**, the group size is 8, equal to the correct group size * field number, that is 2 * 4; if we use groupby() following column **[['C']] or ['C','D']**, the group size is right. If setting **as_index** to **True**, I can get a Series with correct value. But I want the groupby field **'A'** included also, so **as_index** is set to **False**,
#### Expected Output
My expected output is that I can still get a DataFrame which should include column 'A' and 'C', and value of column 'C' is the group size, should be 2 in this code example.
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https://api.github.com/repos/pandas-dev/pandas/issues/26961 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26961/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26961/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26961/events | https://github.com/pandas-dev/pandas/pull/26961 | 458,574,773 | MDExOlB1bGxSZXF1ZXN0MjkwMTU1MjAw | 26,961 | BUG: Fix TypeError by adding line_width argument to to_html() (#26956) | {
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Added in the line_width argument to the to_html() function to prevent TypeError being thrown when argument used.
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https://api.github.com/repos/pandas-dev/pandas/issues/26962 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26962/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26962/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26962/events | https://github.com/pandas-dev/pandas/issues/26962 | 458,596,594 | MDU6SXNzdWU0NTg1OTY1OTQ= | 26,962 | CI: Fix Azure Pipelines problem with PATH | {
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"description": ... | closed | false | null | [] | null | 4 | 2019-06-20T11:18:22Z | 2019-08-07T09:38:42Z | 2019-08-07T09:38:42Z | MEMBER | null | We should be able to set the environment variable `PATH` in an Azure Pipelines job with `echo '##vso[task.prependpath]$HOME/miniconda3/bin'`. See https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml#L27
But that didn't work when we implemented it, so we ended up setting the path manually in every step (e.g. https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml#L48).
This is adding noise to our CI configuration, making it harder to see what every step is doing. We should try to understand what's the problem with setting the `PATH`, and fix it.
This also applies to `ci/azure/posix.yml` (besides `azure-pipelines.yml` in the root). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26963/events | https://github.com/pandas-dev/pandas/issues/26963 | 458,613,913 | MDU6SXNzdWU0NTg2MTM5MTM= | 26,963 | qcut fails with labels=True | {
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} | 3 | 2019-06-20T11:57:26Z | 2020-04-05T02:10:08Z | null | NONE | null | The docs say that `qcut()` takes either a list or a boolean as its `labels` argument. The argument is `None` by default. This, which I would have expected to give me the standard set of labels -- like None -- errors:
```python
pd.qcut(range(5), 4, labels=True)
```
This may well be expected behavior, but it does strike me as semantically weird. | {
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] | closed | false | null | [] | null | 5 | 2019-06-20T12:48:53Z | 2019-07-07T20:36:38Z | 2019-07-05T03:14:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
s = pd.Series([1, 2, np.nan, np.nan, np.nan]).astype('M8[ns]')
print(s)
```
0 1970-01-01 00:00:00.000000001
1 1970-01-01 00:00:00.000000002
2 1970-01-01 00:00:00.000000000
3 1970-01-01 00:00:00.000000000
4 1970-01-01 00:00:00.000000000
dtype: datetime64[ns]
#### Problem description
This problem is on the master branch as of commit baeb1bf92b763861212d0dee951bd8ea658deadb
All 'nan' like entries, when converted to datetime should be NaT.
Several tests are failing, the example above is from pandas/tests/test_algos.py
#### Expected Output
0 1970-01-01 00:00:00.000000001
1 1970-01-01 00:00:00.000000002
2 NaT
3 NaT
4 NaT
dtype: datetime64[ns]
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: baeb1bf92b763861212d0dee951bd8ea658deadb
python: 3.7.3.final.0
python-bits: 32
OS: Linux
OS-release: 3.18.0-19095-g86596f58eadf
machine: aarch64
processor:
byteorder: little
LC_ALL: en_US.utf8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+761.gbaeb1bf92.dirty
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.3
scipy: 1.3.0
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: 2.0.1
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: 1.8.1
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 0.9999999
sqlalchemy: 1.3.4
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.2.2
</details>
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"color": "6138b... | closed | false | null | [] | null | 11 | 2019-06-20T12:58:31Z | 2019-08-28T16:46:05Z | 2019-08-28T16:46:04Z | MEMBER | null | - [ ] closes #26837
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [n/a ] whatsnew entry
cc @jorisvandenbossche
changes to tests for spacing before Datetime64TZ, which now have additional space. | {
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} | 6 | 2019-06-20T14:00:58Z | 2019-06-21T01:11:56Z | 2019-06-21T01:11:56Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/22698
Specifically, see
https://github.com/pandas-dev/pandas/issues/22698#issuecomment-458968300.
I don't think we need to worry about the others in https://github.com/pandas-dev/pandas/issues/22698#issuecomment-422446071 datetimearray and integer array. I couldn't reproduce the returning a scalar behavior with those. | {
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} | 4 | 2019-06-20T14:24:01Z | 2019-06-26T19:51:12Z | 2019-06-26T19:51:03Z | NONE | null | It may be helpful for quicker debugging to provide information of data and index lengths in the error message in sanitize_index function:
https://github.com/pandas-dev/pandas/blob/2115bf3d58ecc20aa2b10e05574eaa06de1e56ec/pandas/core/internals/construction.py#L516
The modified code could be:
raise ValueError(f'Length of values {len(data)} does not match length of index {len(index)}')
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"d... | closed | false | null | [] | null | 7 | 2019-06-20T14:34:50Z | 2019-06-29T09:14:09Z | 2019-06-29T09:14:08Z | MEMBER | null | Checking how long every test file takes to run in the CI. In #26949 I'd like to run tests constraining that a whole file must be run in a single worker (so we can run `single` tests in parallel).
This means that files that will take a lot of time won't be parallelized well, and should be splitted for this to work. So, I want to know how long each takes. | {
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... | open | false | null | [] | null | 6 | 2019-06-20T15:36:48Z | 2021-07-10T05:09:21Z | null | NONE | null | I am seeing a ValueError when running the following example code
```
import pandas as pd
import numpy as np
if __name__ == '__main__':
print(pd.__version__)
print(np.__version__)
df = pd.DataFrame({'ID': [1,1,2,3,3], 'VALUE': [110,110,200,300,301]})
dfg = df.groupby('ID')['VALUE']
print(dfg.get_group(1))
df['MODE_VALUE'] = df.groupby('ID')['VALUE'].transform(lambda x: x.mode())
print(df['MODE_VALUE'])
```
the output is
```
0.24.2
1.15.4
0 110
1 110
Traceback (most recent call last):
...
File "..../models/testing/test_env.py", line 13, in <module>
df['MODE_VALUE'] = df.groupby('ID')['VALUE'].transform(lambda x: x.mode())
File "..../lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 941, in transform
s = klass(res, indexer)
File "..../lib/python3.6/site-packages/pandas/core/series.py", line 255, in __init__
.format(val=len(data), ind=len(index)))
ValueError: Length of passed values is 1, index implies 2
```
however the index and data being checked in series.py (line 255) is
```
Name: VALUE, dtype: int64
[110]
Int64Index([0, 1], dtype='int64')
```
There only seems to be one value [110] in the data array, not two [110,110]. I would have expected the transform to operate on a series with the two values.
The code works fine (as I expected) in my previous version of pandas 0.22.0. I noticed the issue only after updating to pandas 0.24.2
```
0.22.0
1.15.4
0 110
1 110
Name: VALUE, dtype: int64
0 110
1 110
2 200
3 300
4 301
Name: MODE_VALUE, dtype: int64
```
I am not sure if this is expected behaviour or a bug, just posting here to get some help! There are features in 0.24.2 I would like (e.g. DataFrame.to_numpy()) to use but am stuck on this breaking code issue!
Thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/26970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26970/events | https://github.com/pandas-dev/pandas/issues/26970 | 458,762,085 | MDU6SXNzdWU0NTg3NjIwODU= | 26,970 | pprint_thing for sequences | {
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"color": "00980... | closed | false | null | [] | null | 5 | 2019-06-20T16:42:24Z | 2020-07-03T08:50:52Z | 2019-06-24T09:49:49Z | CONTRIBUTOR | null | I am using a custom dataclass as in the index of a DataFrame, which works fine. However, since it implements the Sequence protocol, pprint_thing is rendering every element, rather than the object itself. I would rather use the __str__ method that I have written. I know I can avoid this by changing "pprint_nest_depth" option to 0, but it would be nice to have a direct way of achieving this.
My main question is why the presence of a "__next__" attribute is used as the indicator that the correct output is the result of "str" (
https://github.com/pandas-dev/pandas/blob/cfd65e98e694b2ad40e97d06ffdd9096a3dea909/pandas/io/formats/printing.py#L207)
This is related to #17695, #18843 and #22333. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26971/events | https://github.com/pandas-dev/pandas/issues/26971 | 458,768,119 | MDU6SXNzdWU0NTg3NjgxMTk= | 26,971 | Code sample on pandas.Series.eq page is for pandas.Series.add | {
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] | closed | false | null | [] | null | 1 | 2019-06-20T16:57:17Z | 2019-06-20T18:45:47Z | 2019-06-20T18:45:42Z | NONE | null | #### Problem description
The code sample shown in the "Examples" section on the [doc page for pandas.Series.eq](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.eq.html) appears to be for pandas.Series.add instead of eq.
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 1 | 2019-06-20T20:21:19Z | 2021-07-10T05:10:12Z | null | CONTRIBUTOR | null | ```python
In [2]: pd.Index(pd.SparseArray([1, 2]))
Out[2]: Index([1, 2], dtype='object')
```
Should this be an Int64Index? | {
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... | open | false | null | [] | null | 2 | 2019-06-20T20:34:50Z | 2020-05-16T20:38:32Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import tables as tb
import pandas as pd
# write with pytables
arr = np.zeros(4, dtype={'names':('size', 'age', 'weight'),
'formats':('i2', 'i4', 'u8')})
arr[0] = 2
arr[2] = (1, 2, 3)
fn = 'myfile2'
fileh = tb.open_file(fn+'.h5', mode='w')
filters = tb.Filters(complevel=9, complib='blosc', fletcher32=True)
table = fileh.create_table(fileh.root, 'mytable', description=arr.dtype, filters=filters)
for i in range(5):
table.append(arr)
fileh.close()
# read with pandas (with where)
s = pd.HDFStore(fn+'.h5', mode='r')
s.select('mytable', where='age=2')
>> TypeError: Cannot compare 2 of type <class 'int'> to None column
s.close()
# read with pandas (without where)
s = pd.HDFStore(fn+'.h5', mode='r')
s.select('mytable')
>> <pd.DataFrame>
s.close()
```
#### Problem description
When the where is specified, you get `TypeError: Cannot compare 2 of type <class 'int'> to None column` but the data is read correctly when where is not given.
#### Expected Output
```
size age weight
0 2 2 2
2 1 2 3
4 2 2 2
6 1 2 3
8 2 2 2
10 1 2 3
12 2 2 2
14 1 2 3
16 2 2 2
18 1 2 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.6.2
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: None
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 21 | 2019-06-20T22:26:36Z | 2020-12-22T23:45:29Z | 2020-12-22T23:45:29Z | CONTRIBUTOR | null | ```python
In [4]: pd.Index([1, 2], abc=1)
Out[4]: Int64Index([1, 2], dtype='int64')
```
This should raise, since `abc` isn't a valid keyword. | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-06-21T01:43:05Z | 2019-06-27T18:33:20Z | 2019-06-27T18:33:20Z | MEMBER | null | https://github.com/pandas-dev/pandas/blob/master/pandas/core/algorithms.py#L1528
In core.algorithms.take:
```
if allow_fill:
# Pandas style, -1 means NA
validate_indices(indices, len(arr))
result = take_1d(arr, indices, axis=axis, allow_fill=True,
fill_value=fill_value)
```
It looks to me like the `validate_indices` call should be `validate_indices(indices, arr.shape[axis])`.
Found while attempting to simplify #26914. | {
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"descript... | closed | false | null | [] | null | 5 | 2019-06-21T02:17:44Z | 2019-06-26T02:11:37Z | 2019-06-22T01:46:36Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
arrays = [['a', 'b', 'c', 'd'],
[1,2,3,4]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
index1 = index.copy()
index1.difference(index)
#This should be empty results but I got following results
Out[65]:
MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3, 4]],
codes=[[], []],
names=['first', 'second'])
index1 = index[:index.size-1]
index1.difference(index)
#This results is empty as I expected
Out[67]:
MultiIndex(levels=[[], []],
codes=[[], []],
names=['first', 'second'])
```
#### Problem description
MultiIndex difference returns all indexes if two indexes are same.
I guess it should be empty.
SingleIndex difference returns empty if two indexes are same.
Refer to following example
```
idx1 = pd.Index([2, 1, 3, 4])
idx2 = idx1.copy()
idx1.difference(idx2)
Out[60]: Int64Index([], dtype='int64')
```
#### Expected Output
#### Output of ``pd.show_versions()``
NSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: 1.8.1
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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] | closed | false | null | [] | null | 9 | 2019-06-21T04:54:00Z | 2019-07-29T03:14:34Z | 2019-07-11T16:07:57Z | CONTRIBUTOR | null | - [x] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
**Code Sample**
```python
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'), ('bird', 'parrot'), ('mammal', 'lion'), ('mammal', 'monkey')], names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'), ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'), ( 24.0, 'fly'), ( 80.5, 'run'), (np.nan, 'jump')], index=index, columns=columns)
>>> df
```
```console
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
```
```python
>>> df.reset_index(level=['class', 'name'], col_level=[1, 0], col_fill=['genus', 'pet'])
```
```console
genus name speed species
class pet max type
0 bird falcon 389.0 fly
1 bird parrot 24.0 fly
2 mammal lion 80.5 run
3 mammal monkey NaN jump
```
**Explanation**
For DataFrames having MultiIndex Index and MultiIndex columns,
the `col_level` parameter can also accept a list / tuple so that each
index level's name can be mapped to a specific column level.
if not enough `col_level` / `col_fill` values are given, then the last
given value maps to the remaining levels. | {
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} | 5 | 2019-06-21T07:08:27Z | 2019-06-28T12:32:45Z | 2019-06-28T12:32:41Z | MEMBER | null | Some possible clean-up that I encountered when doing https://github.com/pandas-dev/pandas/pull/26848/ | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26980 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26980/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26980/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26980/events | https://github.com/pandas-dev/pandas/issues/26980 | 459,074,079 | MDU6SXNzdWU0NTkwNzQwNzk= | 26,980 | rolling method: use time window and win_type | {
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} | 2 | 2019-06-21T08:21:17Z | 2020-05-08T19:44:05Z | null | NONE | null | I have the same problem with this [StackOverflow post](https://stackoverflow.com/questions/47377581/pandas-rolling-specifying-time-window-and-win-type), I reproduce it here. The goal is to calculate weighted time-based moving average over an irregular time series
#### Code Sample
```python
import pandas as pd
from pandas.tseries.frequencies import to_offset
dft = pd.DataFrame(
{'B': [0, 1, 2, 3, 4]},
index = pd.Index(
[
pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')
],
name = 'foo'
)
)
dft.rolling('2s').sum() # worked
dft.rolling('2s', win_types='triang').sum() # error: Invalid window
dft.rolling(to_offset('2s'), win_types='triang').sum() # error: Invalid window
dft.rolling(pd.Timedelta('2s'), win_types='triang').sum() # error: Invalid window
```
#### Problem description
the `.rolling` method doesn't accept a time window and not-default window type
I look at the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html) and try with offset window but still have the same problem
If it's not possible to use time window, could you please update the documentation
#### Output of ``pd.show_versions()``
<details>
```
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.4
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26981 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26981/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26981/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26981/events | https://github.com/pandas-dev/pandas/issues/26981 | 459,086,197 | MDU6SXNzdWU0NTkwODYxOTc= | 26,981 | DOC: Do not mention private classes in the documentation | {
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"id": 7171206... | closed | false | null | [] | null | 5 | 2019-06-21T08:50:03Z | 2019-06-23T11:58:07Z | 2019-06-23T11:58:07Z | MEMBER | null | The public documentation shouldn't mention private classes, which the users are not expected to know about, and they could be removed in the future without proper deprecation.
We've got a script to find them, it can be run with `./scripts/validate_docstrings.py --errors=GL04`
Currently there are those cases:
```
pandas.Series.pipe: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.where: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.mask: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.first_valid_index: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.last_valid_index: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.tshift: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.Series.to_dense: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.core.resample.Resampler.get_group: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.where: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.mask: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.pipe: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.tshift: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.first_valid_index: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.last_valid_index: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.DataFrame.to_dense: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.core.groupby.GroupBy.get_group: Private classes (NDFrame) should not be mentioned in public docstrings
pandas.core.groupby.DataFrameGroupBy.tshift: Private classes (NDFrame) should not be mentioned in public docstrings
```
I'd say in most cases we can replace `NDFrame` by `Series or DataFrame` but may be we can rephrase sentences too.
After all them are corrected, we should add to `ci/code_checks.sh` the code `GL04` to the part where we validate the documentation, so the CI checks for it, and mentions to `NDFrame` or other private classes are not reintroduced. Also in the same PR we can add `GL05`, which is not validated but there are no errors in the code for it, so we can start validating it. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26982 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26982/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26982/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26982/events | https://github.com/pandas-dev/pandas/issues/26982 | 459,113,587 | MDU6SXNzdWU0NTkxMTM1ODc= | 26,982 | DOC: Fix docstring quotes in pandas.tseries | {
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} | 4 | 2019-06-21T09:51:24Z | 2020-09-28T15:39:32Z | 2020-09-28T15:39:32Z | MEMBER | null | In pandas we keep the quotes in the docstrings standardized in the next way:
```python
def foo():
"""
This is correct.
"""
```
But for historical reasons we still have many of this form:
```python
def foo():
"""This is incorrect."""
```
We have a script that is able to detect the wrong cases, that gives the next errors in `pandas.tseries`:
```
$ ./scripts/validate_docstrings.py --errors=GL01,GL02 --prefix=pandas.tseries
pandas.tseries.offsets.DateOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.DateOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessHour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessHour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessHour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessHour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Week.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Week.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.WeekOfMonth.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.WeekOfMonth.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.LastWeekOfMonth.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.LastWeekOfMonth.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BQuarterEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BQuarterEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BQuarterBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BQuarterBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BYearEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BYearEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BYearBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BYearBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.FY5253.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.FY5253.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.FY5253Quarter.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.FY5253Quarter.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Easter.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Easter.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Tick.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Tick.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Day.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Day.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Hour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Hour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Minute.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Minute.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Second.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Second.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Milli.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Milli.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Micro.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Micro.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Nano.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Nano.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CBMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CBMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CBMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CBMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
```
We should fix all them, in preparation to validate in the continuous integration that all docstrings in pandas follow our standard. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26983 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26983/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26983/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26983/events | https://github.com/pandas-dev/pandas/issues/26983 | 459,154,912 | MDU6SXNzdWU0NTkxNTQ5MTI= | 26,983 | BLD: wheel building is failing | {
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} | 4 | 2019-06-21T11:38:44Z | 2019-06-24T14:32:23Z | 2019-06-24T14:32:22Z | CONTRIBUTOR | null | after the latest round of merges, seems wheel buidling is failing:
https://travis-ci.org/MacPython/pandas-wheels/builds/548526771
```
=================================== FAILURES ===================================
_______________________ test_missing_required_dependency _______________________
[gw1] linux -- Python 3.5.5 /venv/bin/python
def test_missing_required_dependency():
# GH 23868
# use the -S flag to disable site-packages
call = ['python', '-S', '-c', 'import pandas']
with pytest.raises(subprocess.CalledProcessError) as exc:
subprocess.check_output(call, stderr=subprocess.STDOUT)
output = exc.value.stdout.decode()
> assert all(x in output for x in ['numpy', 'pytz', 'dateutil'])
E assert False
E + where False = all(<generator object test_missing_required_dependency.<locals>.<genexpr> at 0x7f6e59bd4c50>)
/venv/lib/python3.5/site-packages/pandas/tests/test_downstream.py:145: AssertionError
=============================== warnings summary ===============================
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26984 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26984/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26984/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26984/events | https://github.com/pandas-dev/pandas/issues/26984 | 459,182,703 | MDU6SXNzdWU0NTkxODI3MDM= | 26,984 | Use pytest tmp file management fixtures instead of ensure_clean[_path|_dir] | {
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"description": "pandas t... | open | false | null | [] | null | 5 | 2019-06-21T12:53:01Z | 2021-07-10T05:11:06Z | null | CONTRIBUTOR | null | Opened as of discussion in https://github.com/pandas-dev/pandas/pull/26818.
`tests/io/test_pytables.py` uses a context manager called `ensure_clean_path`. It might be worth using the pytest `tmp_path` fixture instead which I think provides everything we need with less code (also easier to understand for new contributors familiar with pytest). And it provides even a little more functionality, comparison:
- it guarantees file system isolation between tests (as does `ensure_clean_path`).
- it makes it obvious which file belongs to which test via expressive naming (as opposed to `ensure_clean_path`).
- it retains files for later inspection as opposed to `ensure_clean_path`. That can be useful for debugging after test failure.
Note: the pytest-managed temp files are removed in a rolling fashion across test runner invocations so that storage space requirement does not grow indefinitely with the number of invocations.
Cf. comment https://github.com/pandas-dev/pandas/pull/26818#issuecomment-504368606 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26985 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26985/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26985/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26985/events | https://github.com/pandas-dev/pandas/issues/26985 | 459,258,706 | MDU6SXNzdWU0NTkyNTg3MDY= | 26,985 | Performance drop and MemoryError during insert and _consolidate_inplace | {
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} | 7 | 2019-06-21T15:39:48Z | 2020-12-11T02:35:42Z | 2020-12-11T02:35:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
n = 2000000
data = pd.DataFrame({'a' : range(n)})
for i in range(1, 100):
data['col_' + str(i)] = np.random.choice(['a', 'b'], n)
for i in range(1, 600):
data['test_{}'.format(i)] = i
print(str(i))
```
#### Problem description
Following [this StackOverflow question](https://stackoverflow.com/questions/56690909/python-is-facing-an-overhead-every-98-executions).
I run this code sample on Ubuntu 18.04 LTS machine with 16 GB of RAM and 2 GB Swap. Execution produces following stacktrace:
```shell
294
295
296
297
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 2657, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'test_298'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 1053, in set
loc = self.items.get_loc(item)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 2659, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'test_298'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "py-memory-test.py", line 12, in <module>
data['test_{}'.format(i)] = i
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3370, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3446, in _set_item
NDFrame._set_item(self, key, value)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py", line 3172, in _set_item
self._data.set(key, value)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 1056, in set
self.insert(len(self.items), item, value)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 1184, in insert
self._consolidate_inplace()
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 929, in _consolidate_inplace
self.blocks = tuple(_consolidate(self.blocks))
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 1899, in _consolidate
_can_consolidate=_can_consolidate)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py", line 3149, in _merge_blocks
new_values = new_values[argsort]
MemoryError
```
I have found [following code](https://github.com/pandas-dev/pandas/blob/224362951942e1f4e05fb8948596620aedac26d9/pandas/core/internals/managers.py#L1179) inside `pandas` core:
```python
def insert(self, loc, item, value, allow_duplicates=False):
...
self._known_consolidated = False
if len(self.blocks) > 100:
self._consolidate_inplace()
```
It seems that this consolidation process takes place every ~100th iteration and substantially affects performance and memory usage. In order to proof this hypothesis I have tried to modify `100` to `1000000` and it worked just fine, no performance gaps, no `MemoryError`.
It looks quite weird from my perspective, since 'consolidation' sounds like it should reduce memory usage. Probably `pandas` should allocate some private Swap files (e.g. via `mmap`) if it is running out RAM+SystemSwap in order to be able to successfully complete consolidation process.
#### Expected Output
```shell
1
2
3
4
5
...
599
```
Without substantial freezes every ~100th iteration and `MemoryError`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: 2.7.3.1 (dt dec pq3 ext lo64)
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-06-21T16:09:09Z | 2019-06-21T17:55:34Z | 2019-06-21T17:55:28Z | CONTRIBUTOR | null | This is a follow-up to [this comment](https://github.com/pandas-dev/pandas/pull/26818#issuecomment-504443559).
This moves two test modules (`test_pytables.py` and `test_pytables_missing.py`) into the new `tests/io/pytables` directory introduced in https://github.com/pandas-dev/pandas/pull/26818.
@jreback let me know if I should create an issue for this first. | {
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} | 4 | 2019-06-21T16:38:08Z | 2019-07-06T21:45:47Z | 2019-07-06T21:45:47Z | CONTRIBUTOR | null | Strange one. This works,
```python
In [39]: a = pd.Series([1, 1])
In [40]: divmod(a, pd.Series([1, 2]));
```
This raises
```pytb
In [41]: divmod(a, pd.Series([0, 2]));
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-41-04277abe6f5a> in <module>
----> 1 divmod(a, pd.Series([0, 2]));
~/sandbox/pandas/pandas/core/ops.py in wrapper(left, right)
1714 rvalues = rvalues.values
1715
-> 1716 result = safe_na_op(lvalues, rvalues)
1717 return construct_result(left, result,
1718 index=left.index, name=res_name, dtype=None)
~/sandbox/pandas/pandas/core/ops.py in safe_na_op(lvalues, rvalues)
1660 try:
1661 with np.errstate(all='ignore'):
-> 1662 return na_op(lvalues, rvalues)
1663 except Exception:
1664 if is_object_dtype(lvalues):
~/sandbox/pandas/pandas/core/ops.py in na_op(x, y)
1640 result = masked_arith_op(x, y, op)
1641
-> 1642 result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
1643 return result
1644
~/sandbox/pandas/pandas/core/missing.py in fill_zeros(result, x, y, name, fill)
544 mask = ((y == 0) & ~np.isnan(result)).ravel()
545
--> 546 shape = result.shape
547 result = result.astype('float64', copy=False).ravel()
548
AttributeError: 'tuple' object has no attribute 'shape'
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} | 0 | 2019-06-21T17:17:01Z | 2019-11-16T21:54:02Z | 2019-11-16T21:54:02Z | MEMBER | null | ```
dti = pd.date_range('2016-01-01', periods=3, tz='US/Pacific')
ser = pd.Series(dti)
cat = ser.astype('category')
>>> cat
0 2016-01-01 00:00:00-08:00
1 2016-01-02 00:00:00-08:00
2 2016-01-03 00:00:00-08:00
dtype: category
Categories (3, datetime64[ns, US/Pacific]): [2016-01-01 00:00:00-08:00, 2016-01-02 00:00:00-08:00,
2016-01-03 00:00:00-08:00]
>>> cat.replace(cat[1], 'foo')
0 1451635200000000000
1 1451721600000000000
2 1451808000000000000
dtype: int64
``` | {
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} | 5 | 2019-06-21T17:30:57Z | 2020-05-25T22:07:51Z | 2020-05-25T22:07:50Z | NONE | null | #### Code Sample
```python
# Your code here
import pandas as pd
s = pd.Series(index=pd.MultiIndex.from_tuples([('a', 0)]))
s.loc['a', 0] # loc function works fine, no complaints here
s.at['a', 0] # raises TypeError
```
#### Stack Trace:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py", line 2270, in __getitem__
return self.obj._get_value(*key, takeable=self._takeable)
TypeError: _get_value() got multiple values for argument 'takeable'
```
#### Problem description
I would expect the `at` function to either return the values given by the indexers or raise a `KeyError`. In the above example, the index value exists, so `at` should return the value from the series at that index (`nan`).
The `at` function works fine for series with normal indexes. There is nothing in the documentation indicating it should not work for multi-indexes.
#### Expected Output
```
nan
nan
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-generic
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.4.2
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 4 | 2019-06-21T19:25:24Z | 2019-06-22T19:19:38Z | 2019-06-22T19:19:38Z | CONTRIBUTOR | null | It would be useful to be able to change a column dtype inline.
For example with a DataFrame-level astype() overload:
Assuming `df` has a column called `str_col` that is a string (object),
`df.astype(col='str_col', dtype='int').head()`
The use case that brought me here is to numerically sort a string column without permanently changing it. What I was hoping to do is
`df.astype(col='str_col', dtype='int').sort_values(by='str_col')`
Instead, I had to do this:
`df = df.assign(int_col = lambda x: x['str_col'].astype('int')).sort_values(by='int_col').drop('int_col', axis = 'columns')`
The method would also support dictionaries for multiple column type changes:
`df.astype({'col1':'type1', 'col2':'type2'})` | {
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variable = pandas.read_csv(r"C://Users/zialecuyermorison/Desktop/data/iris.csv")
header.sepal_length( )
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-61-ef9a5393d1d5> in <module>
----> 1 variable = pandas.read_csv(r"C://Users/zialecuyermorison/Desktop/data/iris.csv")
/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
700 skip_blank_lines=skip_blank_lines)
701
--> 702 return _read(filepath_or_buffer, kwds)
703
704 parser_f.__name__ = name
/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
427
428 # Create the parser.
--> 429 parser = TextFileReader(filepath_or_buffer, **kwds)
430
431 if chunksize or iterator:
/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
893 self.options['has_index_names'] = kwds['has_index_names']
894
--> 895 self._make_engine(self.engine)
896
897 def close(self):
/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1120 def _make_engine(self, engine='c'):
1121 if engine == 'c':
-> 1122 self._engine = CParserWrapper(self.f, **self.options)
1123 else:
1124 if engine == 'python':
/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1851 kwds['usecols'] = self.usecols
1852
-> 1853 self._reader = parsers.TextReader(src, **kwds)
1854 self.unnamed_cols = self._reader.unnamed_cols
1855
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
FileNotFoundError: [Errno 2] File b'C://Users/zialecuyermorison/Desktop/data/iris.csv' does not exist: b'C://Users/zialecuyermorison/Desktop/data/iris.csv'
```
#### Problem description
I inputted the following code into JupyterLab and I know the path is right and I've tried switching / for \ using // and adding r in front of the C. I still get the following error. I also tried reloading the pandas library.
#### Expected Output
to pull up the data on Jupyterlab notebook in a table like format hopefully.
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] | closed | false | null | [] | null | 8 | 2019-06-21T20:18:46Z | 2019-09-08T16:41:51Z | 2019-09-08T16:41:44Z | CONTRIBUTOR | null | - [x] closes #26792
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-06-21T20:58:02Z | 2020-04-28T20:42:11Z | 2019-06-21T22:10:44Z | CONTRIBUTOR | null | See https://github.com/MacPython/pandas-wheels/pull/50. I can't get this test to work reliably. | {
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} | 5 | 2019-06-22T01:05:34Z | 2019-06-22T19:41:24Z | 2019-06-22T19:19:38Z | CONTRIBUTOR | null | Closes #26990
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} | 9 | 2019-06-22T02:47:40Z | 2019-06-27T21:22:38Z | 2019-06-27T21:22:38Z | MEMBER | null | Categorical.copy passes `values=self._codes.copy()`, and doesn't have a `deep=False` kwarg (which the base ExtensionArray does). This is causing trouble in #26954. Is this intentional @TomAugspurger?
Pandas-internal EAs with `copy` behavior not respecting `deep` kwarg:
- [ ] Categorical (no kwarg at all)
- [ ] DatetimeLike (ignores kwarg)
- [ ] PandasArray
IntegerArray, IntervalArray, and SparseArray look correct.
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} | 1 | 2019-06-22T05:08:59Z | 2020-01-26T00:29:40Z | 2020-01-26T00:29:40Z | NONE | null | Using current d39a6de668
This may be two separate issues, I can't tell.
### Case 1.
```python
import pandas as pd
ser=pd.Series(range(10))
ser[:5]=None
print(ser)
pd.Series(ser).rolling(3, min_periods=3).count()
```
returns
```
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 1.0
6 2.0
7 3.0
8 3.0
9 3.0
dtype: float64
```
I expected the first few results to be NA. perhaps related:
```
pd.Series(ser).rolling(3, min_periods=999).count()
```
doesn't raise an exception.
### Case 2.
for series with DatetimeIndex and rolling with an offset, the behavior is also strange, but different.
```python
import pandas as pd
import random
from itertools import chain
dates = ["2001-01-01"]*2 + ["2001-01-02"]*2 + ["2001-01-03"]*2 + ["2001-01-04"]*2 + ["2001-01-05"]*2+ ["2001-01-06"]*2
ser=pd.Series(index=pd.DatetimeIndex(dates))
ser[0]=111
ser[2]=222
ser[6]=333
res=ser.rolling("2D", min_periods=1).count()
df=pd.DataFrame(dict(data=ser,count=res))
print(df)
```
result
```
data count
2001-01-01 111.0 1.0 # OK. The day has one non-NA value,
2001-01-01 NaN 1.0 # and min_period is satisfied
2001-01-02 222.0 2.0 # OK
2001-01-02 NaN 2.0 # OK
2001-01-03 NaN 1.0 # why is this 1.0? this date has no values in it.
2001-01-03 NaN 1.0 # again
2001-01-04 333.0 1.0 # OK
2001-01-04 NaN 1.0 # OK
2001-01-05 NaN 1.0 # why is this 1.0? this date has no values in it.
2001-01-05 NaN 1.0 # again
2001-01-06 NaN NaN # why is this NaN? min_periods is satisfied
2001-01-06 NaN NaN # and non-NA count should 0.
```
encountered in the course of #26959 | {
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] | closed | false | null | [] | null | 3 | 2019-06-22T09:30:32Z | 2019-06-23T11:58:07Z | 2019-06-23T11:58:07Z | CONTRIBUTOR | null | - [x] closes #26981
- [x] tests `python validate_docstrings.py --errors=GL04` passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"description": "Relating to inplace para... | open | false | null | [] | null | 1 | 2019-06-22T12:03:22Z | 2021-10-29T02:22:14Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
a = pd.DataFrame(
np.random.random(12).reshape([4,3]),
columns = ['A', 'B', 'C'],
index = pd.MultiIndex.from_product([['p','q'],['x','y']], names=['A','D']),
)
print(a)
# A B C
# A D
# p x 0.809155 0.181919 0.957133
# y 0.625364 0.802854 0.065737
# q x 0.288116 0.570645 0.944983
# y 0.926358 0.684897 0.724123
try:
a.reset_index(inplace=True)
except ValueError as e:
print(e) # cannot insert A, already exists
print(a)
# D A B C
# A D
# p x x 0.809155 0.181919 0.957133
# y y 0.625364 0.802854 0.065737
# q x x 0.288116 0.570645 0.944983
# y y 0.926358 0.684897 0.724123
```
#### Problem description
when there are columns with the same name in data-body and multi-index on calling `DataFrame.reset_index()`, `ValueError` is thrown. however, if `inplace=True` option is specified, this operation is not atomic and leaves DataFrame itself incomplete.
I think keeping original data is better and consistent when such exceptions are occurred.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.3.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.0.2
pip: 10.0.1
setuptools: 39.0.1
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 4 | 2019-06-22T12:57:10Z | 2019-06-28T12:38:32Z | 2019-06-28T12:38:32Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
writer = pd.ExcelWriter(Path().resolve() / 'output' / 'out.xlsx')
for dftup in exportlist:
dftup[0].to_excel(writer, sheet_name=dftup[2])
```
#### Problem description
Note that a similar issue was covered in in #19242, but with a different data type.
The above code takes a list of tuples which contain the DataFrames (in position 0) and the desired sheet names (in position 2) I would like to write. These DataFrames have MultiIndex columns whose levels are type PeriodIndex. The first level is an index of yearly Periods (freq='Y') and the second level an index of Quarterly periods (freq='Q').
When I try to write the DataFrames to excel, I get the following error (I can post the whole trace if needed). Any ideas?
```
TypeError: Unsupported type <class 'pandas._libs.tslibs.period.Period'> in write()
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.8
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 8 | 2019-06-22T13:41:00Z | 2019-07-03T21:59:48Z | 2019-07-03T21:59:48Z | MEMBER | null | There haven been previous attempts to reduce the length of the Series/DataFrame repr (`pandas.options.display.max_rows`), eg https://github.com/pandas-dev/pandas/pull/20514. Related pandas-dev email: https://mail.python.org/pipermail/pandas-dev/2018-March/000732.html
In that discussion, I once made the following proposal to introduce two thresholds:
- We have 2 thresholds instead of 1 (the current 'max_rows'): a number of
rows to show *in* a truncated repr, and a max number of rows to show
without truncating
- For 'big' dataframes, we show a truncated repr. And then I would go even
lower than 20 and only show first/last 5 (so like a max_rows of 10)
- For 'small' dataframes, we show the full dataframe without truncating, up
to the threshold.
We would still need to define those two thresholds. But for example, using the current max_rows of 60: we could show a full repr up to 60 rows, and once the number of rows > 60, we only show 10 (first/last 5).
You can then still set both thresholds at the same number (like 20, as in the linked PR above) to not get this variable behaviour.
This is actually similar to what numpy arrays do (but with a bigger threshold: eg np.random.randn(1000) shows all 1000 elements, np.random.randn(1001) shows the first/lst 3).
And it is also very similar to what R tibbles do: they have a "print_min" and "print_max" options with exactly this behaviour, only their "print_max" is lower (it's 10 and 20, respectively):
> options(tibble.print_max = n, tibble.print_min = m): if there are more than
> n rows, print only the first m rows. Use options(tibble.print_max = Inf)
> to always show all rows.
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} | 4 | 2019-06-22T13:44:00Z | 2019-07-09T15:02:19Z | 2019-06-25T00:47:47Z | MEMBER | null | Currently, the 'Series' name is hardcoded in the empty repr. This small patch ensures it uses the name of the class. | {
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"description": "__repr__... | closed | false | null | [] | null | 6 | 2019-06-22T14:13:03Z | 2019-06-24T02:27:07Z | 2019-06-24T02:27:07Z | NONE | null | Add the following options:
- Draw borders: specify border characters and which ones to draw _(similar to [texttable](https://pypi.org/project/texttable/))_
- Allow multi-line cells for long strings instead of truncation (they are visually separated by the borders)
- ~~Use [Markdown table format](https://help.github.com/en/articles/organizing-information-with-tables). This could also be implemented in a separate `to_markdown()` function.~~ _(Duplicate of #11052)_ | {
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} | 6 | 2019-06-22T19:44:45Z | 2021-07-10T05:23:24Z | 2021-07-10T05:23:23Z | NONE | null | https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/frame.py#L1430-L1556
I passed a list of data with 5 columns but a column header list of 6 items. The documentation for `pd.DataFrame.from_records()` says:
> any names not found in the data will become all-NA columns
However, instead I get:
```python
AssertionError: 6 columns passed, passed data has 5 columns
```
If I am interpreting this wrong or using this method the wrong way, I'm welcome to any suggestions too. Code to reproduce:
```python
>>> import pandas as pd
>>> data = [list(range(1,6))]
>>> data
[[1, 2, 3, 4, 5]]
>>> col_headers = 'A B C D E F'.split()
>>> col_headers
['A', 'B', 'C', 'D', 'E', 'F']
>>> df = pd.DataFrame.from_records(data=data, columns=col_headers)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dtorkelson/miniconda3/envs/branker/lib/python3.7/site-packages/pandas/core/frame.py", line 1512, in from_records
coerce_float=coerce_float)
File "/Users/dtorkelson/miniconda3/envs/branker/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 404, in to_arrays
dtype=dtype)
File "/Users/dtorkelson/miniconda3/envs/branker/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 436, in _list_to_arrays
coerce_float=coerce_float)
File "/Users/dtorkelson/miniconda3/envs/branker/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 492, in _convert_object_array
con=len(content)))
AssertionError: 6 columns passed, passed data had 5 columns
>>> col_headers.pop(5)
'F'
>>> df = pd.DataFrame.from_records(data=data, columns=col_headers)
>>> df
A B C D E
0 1 2 3 4 5
```
The extra column name is passed because this code is being used to create many dataframes in a loop, which are being appended to each other. Many have data in the sixth column, but for those that don't, I was hoping (and thinking) that pandas would fill it in with nulls. | {
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] | closed | false | null | [] | null | 3 | 2019-06-22T23:28:15Z | 2019-06-23T16:10:38Z | 2019-06-23T09:05:27Z | MEMBER | null | One of the tests that uses this fixture modifies its value, so the `scope` kwarg is incorrect. The failure depends on the order in which the test is run. AFAICT only showing up on OSX.
Hits one of the checkboxes in #26823. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27005 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27005/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27005/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27005/events | https://github.com/pandas-dev/pandas/pull/27005 | 459,540,874 | MDExOlB1bGxSZXF1ZXN0MjkwODY2ODMy | 27,005 | Add informal definition of "label" in note | {
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] | closed | false | null | [] | null | 8 | 2019-06-23T06:12:16Z | 2019-06-30T08:40:10Z | 2019-06-24T14:23:57Z | NONE | null | The term "label" is used extenisively, but (AFAICT) it is never explained what a "label" is. Is an integer offset a label? Do labels apply to rows and columns, multi-indexes? What's the relation between an "index" "axis" and "label" ... etc.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 13 | 2019-06-23T06:12:41Z | 2019-06-28T23:11:27Z | 2019-06-28T12:38:32Z | CONTRIBUTOR | null | The XlsxWriter `write_cells` method takes a `cells` parameter that is itself derived from a generator of the various cells in the DataFrame. It calls the `_value_with_format()` to convert any numpy types to Python types for the Excel writer.
In the section of code that deals with writing merged cells, the original passed `cell.val` parameter was being passed into the writer, rather than the `val` returned from the `_value_with_format()` function. This
caused incompatible numpy or Pandas types to get passed into the writer when the condition of being in a 'merged' (grouped) DataFrame cell was met.
In my case, I had a Pandas `Period` object in the MultiIndex of a grouped DataFrame . All that needed to be done to fix this was simply removing the `cell.` from `cell.val` that was being passed into `wks.merge_range()` function, and replacing it with the correct `val`, which is 'type safe' for the Excel writer.
- [x] closes #26999
- [x] tests added / passed
- [x] whatsnew entry
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} | 6 | 2019-06-23T07:46:10Z | 2019-06-24T15:56:10Z | 2019-06-24T15:52:42Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 10 | 2019-06-23T11:14:10Z | 2019-06-29T20:54:28Z | 2019-06-29T20:54:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
In [14]: import pandas as pd
In [14]: ew = pd.ExcelWriter('test.xlsx')
In [15]: df = pd.DataFrame([pd.to_datetime('2019-06-22 01:11'), pd.to_datetime('2019-06-22 01:11
...: Z'), pd.to_datetime('2019-06-22 01:11MSK')])
In [16]: df
Out[16]:
0
0 2019-06-22 01:11:00
1 2019-06-22 01:11:00+00:00
2 2019-06-22 01:11:00+03:00
In [17]: df.to_excel(ew, sheet_name='sh1')
In [18]: ew.save()
In [19]: ew.close()
```
(MSK is +03:00)
#### Problem description
ExcelWriter seems to incorrectly convert timestamps with timezones. Time part seems to be converted correctly, however date part looks like it was unchanged.
#### Expected excel sheet content
0 | 2019-06-22 01:11:00
1 | 2019-06-22 01:11:00
2 | 2019-06-21 22:11:00
#### Actual excel sheet content
0 | 2019-06-22 01:11:00
1 | 2019-06-22 01:11:00
2 | 2019-06-**22** 22:11:00
pandas is 0.24.2
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} | 25 | 2019-06-23T12:18:33Z | 2019-07-03T18:28:35Z | 2019-07-03T18:21:02Z | MEMBER | null | - [X] xref #26747
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The main changes are:
The API that backends need to implement is simply a function `plot(data, kind, **kwargs)`. After testing different approaches (abstract class previously mentioned and others), I think this is what makes things simpler in both ends pandas and the backends. For `hvplot` `hvPlot.__call__` is already what needs to be implemented, and this change will mainly require to delete a bit of code. For `pdvega` a bit more work will be needed, but this will help remove a significant amount of repetitive code.
Second thing, more in the pandas side, I unify the accessors of `Series` and `DataFrame`. There will be minor changes, like unified documentation (which I think it's great, but docstrings will have to be generic), and also `Series.plot.scatter` will exist (which now doesn't) but will generate an exception if called. Not a big deal I'd say, because now it's already possible to do `Series.plot(kind='scatter')`, so it'll just make both cases consistent. Same things apply to `hexbin`.
CC: @pandas-dev/pandas-core @philippjfr @jakevdp | {
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} | 4 | 2019-06-23T19:27:36Z | 2019-06-27T21:31:35Z | 2019-06-27T21:27:18Z | MEMBER | null | Addresses one of the checkboxes in #26823 | {
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} | 1 | 2019-06-23T20:23:59Z | 2019-07-24T11:42:49Z | 2019-07-24T11:42:49Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
On master:
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+783.g2b9b58dad'
In [2]: ts_list = [pd.Timestamp('2018-01-01'), np.nan]
...: tstz_list = [pd.Timestamp('2018-01-01', tz='UTC'), np.nan]
In [3]: pd.core.indexes.base.ensure_index(ts_list)
Out[3]: DatetimeIndex(['2018-01-01', 'NaT'], dtype='datetime64[ns]', freq=None)
In [4]: pd.core.indexes.base.ensure_index(tstz_list)
Out[4]: Index([2018-01-01 00:00:00+00:00, nan], dtype='object')
```
#### Problem description
`Out[4]` does not coerce `np.nan` to `pd.NaT` and results in an `Index` with `object` dtype instead of a `DatetimeIndex`.
This causes downstream issues with `IntervalIndex`/`IntervalArray` as it can cause a valid `IntervalIndex`/`IntervalArray` to not be roundtripable from it's equivalent `list`/`np.array` representation:
```python
In [5]: left = pd.DatetimeIndex(['2018-01-01', pd.NaT], tz='UTC')
...: right = pd.DatetimeIndex(['2018-01-02', pd.NaT], tz='UTC')
...: ii = pd.IntervalIndex.from_arrays(left, right)
...: ii
Out[5]:
IntervalIndex([(2018-01-01, 2018-01-02], nan],
closed='right',
dtype='interval[datetime64[ns, UTC]]')
In [6]: pd.IntervalIndex(ii.tolist())
---------------------------------------------------------------------------
TypeError: category, object, and string subtypes are not supported for IntervalIndex
In [7]: pd.IntervalIndex(ii.to_numpy())
---------------------------------------------------------------------------
TypeError: category, object, and string subtypes are not supported for IntervalIndex
```
Under the hood the `list`/`np.array` is being converted to `left`/`right` components, which are then passed to `ensure_index`, resulting in an `Index` with `object` dtype, hence the error message.
Note that the equivalent roundtrip without a tz works fine, as expected based on the inconsistency noted in the `ensure_index` example:
```python
In [8]: left = pd.DatetimeIndex(['2018-01-01', pd.NaT])
...: right = pd.DatetimeIndex(['2018-01-02', pd.NaT])
...: ii = pd.IntervalIndex.from_arrays(left, right)
...: ii
Out[8]:
IntervalIndex([(2018-01-01, 2018-01-02], nan],
closed='right',
dtype='interval[datetime64[ns]]')
In [9]: pd.IntervalIndex(ii.tolist())
Out[9]:
IntervalIndex([(2018-01-01, 2018-01-02], nan],
closed='right',
dtype='interval[datetime64[ns]]')
In [10]: pd.IntervalIndex(ii.to_numpy())
Out[10]:
IntervalIndex([(2018-01-01, 2018-01-02], nan],
closed='right',
dtype='interval[datetime64[ns]]')
```
#### Expected Output
I'd expect `Out[4]` to be coerced to a `DatetimeIndex` with `pd.NaT` and the appropriate tz:
```python
Out[4]: DatetimeIndex(['2018-01-01 00:00:00+00:00', 'NaT'], dtype='datetime64[ns, UTC]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2b9b58dadf8a4e02b94747b6c8b22bec4b6eeefd
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0.dev0+783.g2b9b58dad
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.8.0
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
matplotlib : 3.1.0
numexpr : 2.6.9
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27012 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27012/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27012/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27012/events | https://github.com/pandas-dev/pandas/issues/27012 | 459,617,860 | MDU6SXNzdWU0NTk2MTc4NjA= | 27,012 | TST: fixture names for REPL usability | {
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] | closed | false | null | [] | null | 1 | 2019-06-23T21:05:35Z | 2019-11-05T22:11:20Z | 2019-11-05T22:11:20Z | MEMBER | null | The pytest docs (https://docs.pytest.org/en/latest/deprecations.html#calling-fixtures-directly) suggest a pattern that would make fixturized tests a zillion times less frustrating to troubleshoot when they fail.
Instead of
```
@pytest.fixture
def whatever():
```
We use
```
def whatever():
@pytest.fixture(name="whatever")
def whatever_fixture():
return whatever()
```
This way, when a test fails, I can recreate it in the repl with
```
from pandas.tests.extensions.test_categorical import *
data = data()
```
instead of having to copy-and-paste the contents of the `data` fixture like a savage. | {
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