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MDExOlB1bGxSZXF1ZXN0NDIxOTg4NTkw
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BUG: GH29461 strftime() with nanoseconds for Timestamp
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| 2020-06-09T14:41:34Z
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|
CONTRIBUTOR
| null |
- [x] closes #29461
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The new method displays %f to the full precision, including trailing zeroes. I could add a check that only displays nanoseconds if they are non-zero.
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BUG: "IndexingError: Too many indexers" when accessing a None value using .loc through a MultiIndex
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| 2020-06-03T18:02:23Z
| 2020-06-03T18:02:23Z
|
NONE
| null |
#### Steps to reproduce
```python
print(pd.Series(
[None],
pd.MultiIndex.from_arrays([['Level1'], ['Level2']]))
.loc[('Level1', 'Level2')])
```
#### Expected output
```
None
```
#### Actual output
```
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in __getitem__(self, key)
1760 except (KeyError, IndexError, AttributeError):
1761 pass
-> 1762 return self._getitem_tuple(key)
1763 else:
1764 # we by definition only have the 0th axis
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
1275
1276 # no multi-index, so validate all of the indexers
-> 1277 self._has_valid_tuple(tup)
1278
1279 # ugly hack for GH #836
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
699 for i, k in enumerate(key):
700 if i >= self.ndim:
--> 701 raise IndexingError("Too many indexers")
702 try:
703 self._validate_key(k, i)
IndexingError: Too many indexers
```
#### Additional information
If any value other than `None` (even `np.nan`) is used, the code behaves correctly.
If a single index level is used, the code behaves correctly.
#### Workaround
Seems to work if `.loc(axis=0)[('Level1', 'Level2')]` is used instead.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 5.6.0-1-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : 4.6.9
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.6.9
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MjMzNzg1MjM=
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|
BUG: Fillna with timedelta min/max overflow
|
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[] | null | 1
| 2020-05-22T17:35:32Z
| 2020-05-23T21:31:20Z
| 2020-05-23T21:31:20Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
print(pd.to_timedelta(np.full(10, pd.NaT),unit='seconds').fillna(pd.Timedelta.max))
```
#### Problem description
Output of the code:
```python
TimedeltaIndex(['-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224',
'-106752 days +00:12:43.145224'],
dtype='timedelta64[ns]', freq=None)
```
The returned Series is filled with the value for pd.Timedelta.min and not the max value as expected. The opposite happens when trying to fillna with pd.Timedelta.min, which returns a series of the max values instead.
This doesn't seem to occur when filling a series that isn't converted to timedelta:
```python
pd.Series(pd.NaT, index=np.arange(10)).fillna(pd.Timedelta.max)
```
And it seems to be an overflow problem because the following produces a number close to pd.Timedelta.max, as expected:
```python
pd.to_timedelta(np.full(10, pd.NaT),unit='seconds').fillna(pd.Timedelta.max - pd.Timedelta('1 s'))
```
#### Expected Output
```python
0 106751 days 23:47:16.854775
1 106751 days 23:47:16.854775
2 106751 days 23:47:16.854775
3 106751 days 23:47:16.854775
4 106751 days 23:47:16.854775
5 106751 days 23:47:16.854775
6 106751 days 23:47:16.854775
7 106751 days 23:47:16.854775
8 106751 days 23:47:16.854775
9 106751 days 23:47:16.854775
dtype: timedelta64[ns]
```
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.1
</details>
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|
ENH: `name` attribute for pd.Interval
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| 2020-05-22T18:10:56Z
| 2020-05-23T06:29:33Z
| 2020-05-23T06:29:33Z
|
NONE
| null |
#### Is your feature request related to a problem?
No
#### Describe the solution you'd like
`pd.Interval(x, y, name="Feature")`
#### API breaking implications
Not that I know of.
#### Describe alternatives you've considered
Tried `setattr`
|
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CI: Numpy compat
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| 2020-05-22T23:39:58Z
| 2020-05-22T23:39:58Z
|
CONTRIBUTOR
| null |
- [x] closes #34306
Likely related to: https://github.com/pandas-dev/pandas/pull/32510.
Seems doc builds have started using:
numpy 1.17.5 py38h95a1406_0 conda-forge
https://github.com/pandas-dev/pandas/pull/34317/checks?check_run_id=700337761
(Previous pipelines where using 1.18)
In numpy 1.17.5 BitGenerator path is:
`np.random.bit_generator.BitGenerator`
In numpy 1.18.0 path is
`np.random.BitGenerator`
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REF: make FY523Mixin a cdef class
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REF: make SingleConstructorOffset a cdef class
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| 2020-05-23T01:11:15Z
| 2020-05-22T23:44:11Z
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MEMBER
| null |
Bite the bullet on importing cache_readonly into liboffsets
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REF: get str_rep in numexpr code
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ENH: pd.DataFrame.plot.hist() does not use sharex argument
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| null |
CONTRIBUTOR
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The command
df.plot.hist(subplots=True, layout=(4, 4), figsize=(15,10), sharex=False, sharey=False)
produces a grid of histograms. Unfortunately, sharex and y arguments are not taken into account and the plots all share the same axis. This only makes sense if all columns have the similar scales. But in general this behaviour is not very useful. Apparently, `df.plot.density()` accepts the keywords and uses them correctly.
I would hope the function could be extended to provide the functionality as well as
df.hist(...)
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ENH: Add hue functionality to df.hist()
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CONTRIBUTOR
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The following command:
df.hist(layout=(4, 4), figsize=(15,10), bins=30)
produces a grid of histograms. I would like to group the data by a column value and plot the corresponding histograms in the same plot with different colors. Unfortunately, the `by=` argument generates subplots. And plots all the data regardless the scale in as many subplots as unique values in the dataframe.
I wonder if either an argument `hue=` as used in seaborn could be added so to produce the desired result.
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ENH: Don't omit columns with categorical data in df.hist()
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CONTRIBUTOR
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For some strange reason `df.hist` can plot categorical features where features are given as string values.
df.cat.hist()
If the dataframe in column "cat" contains string type values the histogram is generated correctly.
But when called like this `df.hist()` the corresponding plot is left out. Along the lines, it would be nice if `df.bool.hist()`, where "bool" contains boolean values, would not throw an error but would handle it as binary category value and label it True and False. I am realizing there are many improvements possible with df.hist(). Not sure if I should open a new issue for each one.
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Fix MultiIndex melt when col_level is used
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CONTRIBUTOR
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- [x] closes #34129
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2020-05-23T01:45:15Z
| 2020-12-29T20:42:50Z
| 2020-12-29T20:42:50Z
|
CONTRIBUTOR
| null |
- [x] closes #34066
- [x] tests added/passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU2MjM1OTAyMTA=
| 34,331
|
BUG: writing dataframe to excel if one of the rows has name "render" fails
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| 2
| 2020-05-23T06:02:33Z
| 2020-05-26T22:21:44Z
| 2020-05-26T22:21:44Z
|
NONE
| null |
I try to write an dataframe to excel with excel. It fails if a row is called "render".
Pandas version 1.0.3
Reproduce with:
s1 = pandas.Series(("1","2"),index=["a","b"])
s2 = pandas.Series(("3","4"),index=["b","b"])
df = pandas.DataFrame({"render":s1,"huhu":s2})
df.to_excel("/tmp/out.xlsx")
gives:
AttributeError Traceback (most recent call last)
<ipython-input-44-02936207a82a> in <module>
----> 1 df.to_excel("/tmp/out.xlsx")
/usr/local/anaconda3/envs/exoplanets/lib/python3.7/site-packages/pandas/core/generic.py in to_excel(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes)
2171 index_label=index_label,
2172 merge_cells=merge_cells,
-> 2173 inf_rep=inf_rep,
2174 )
2175 formatter.write(
/usr/local/anaconda3/envs/exoplanets/lib/python3.7/site-packages/pandas/io/formats/excel.py in __init__(self, df, na_rep, float_format, cols, header, index, index_label, merge_cells, inf_rep, style_converter)
384 if hasattr(df, "render"):
385 self.styler = df
--> 386 df = df.data
387 if style_converter is None:
388 style_converter = CSSToExcelConverter()
/usr/local/anaconda3/envs/exoplanets/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5273 return self[name]
-> 5274 return object.__getattribute__(self, name)
5275
5276 def __setattr__(self, name: str, value) -> None:
AttributeError: 'DataFrame' object has no attribute 'data'
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MDU6SXNzdWU2MjM1OTE1NTk=
| 34,332
|
ENH: A keep_fields type argument for merge operation
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| 2020-05-23T06:12:49Z
| 2020-08-21T17:05:41Z
| 2020-05-23T06:18:42Z
|
NONE
| null |
#### Is your feature request related to a problem?
It would increase user convenience if there were an argument where the user could specify the fields from the two data frames to be be merged which should be kept(or alternatively, a discard_fields column to specify which ones should be discarded)
This would make the merge operation more analogous to its SQL counterpart
#### Describe the solution you'd like
For two data frames A and B where A has fields 'c' and 'd' and B has fields 'e' and 'f' respectively,
it would simply result in the merge API looking like:
`pd.merge(A,B,left_on = 'c', right_on = 'e', keep_fields = ['c','d','e'])
`
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DOC: fix deprecated groupby squeeze example in v0.12.0 whatsnew
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| 1
| 2020-05-23T10:06:22Z
| 2020-05-23T11:52:01Z
| 2020-05-23T11:51:56Z
|
MEMBER
| null |
This should fix failing CI, due to https://github.com/pandas-dev/pandas/pull/33218
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BUG: xticks unnecessarily rotated
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| 6
| 2020-05-23T10:48:10Z
| 2020-09-14T07:25:10Z
| 2020-09-13T20:46:09Z
|
MEMBER
| null |
- [x] closes #29460
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
On `master`, xticklabels are rotated if
```python
not self._use_dynamic_x()
and data.index.is_all_dates
and not self.subplots
or (self.subplots and self.sharex)
```
There's a comment saying
```python
if condition:
# irregular TS rotated 30 deg. by default
# probably a better place to check / set this.
if not self._rot_set:
self.rot = 30
```
, and it seems that the _intention_ was to rotate ticklabels if there's an irregular timeseries.
With the condition as it is written, they will also be rotated if the following is true:
```python
(self.subplots and self.sharex)
```
I don't see why that should warrant rotating the ticklabels, so I'd like to suggest that there was a bracketing error.
Also, the condition `data.index.is_all_dates` is (as far as I can tell) only relevant if `use_index=True`, so I've changed that.
Here's what the OP's plot looks like with these changes:
Here's what it looks like on master:
Irregular time series still have their ticklabels rotated:
Regular time series' ticklabels don't rotate:
(unless you pass `xcompat=True`, but that's unaffected by this change)
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MDExOlB1bGxSZXF1ZXN0NDIyMjYyMjM1
| 34,335
|
BUG: Fix render as column name in to_excel
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| 3
| 2020-05-23T11:44:13Z
| 2020-05-27T16:30:02Z
| 2020-05-26T22:21:44Z
|
MEMBER
| null |
- [x] closes #34331
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Should I add a whats new enty? Under which section?
https://github.com/phofl/pandas/blob/34331_render_column_name_raises_error_in_to_excel/pandas/io/formats/excel.py#L389
The previous code checked if the `df` had a attribute `render`. This check was True, if there was a `DataFrame` given as input with a column `render`. I think that this check should only be True if a `Styler` object is given, so I added an `isinstace` check for `Styler`.
Does the `Styler` object always an attribute `render`? If not, we would run into problems, if we get a `Styler` object with a column named `render` but without the `render` attribute. If a `Styler` object has always an attribute `render`, we could delete the `hasattr` check.
Edit: I changed the check to `not isinstance(df, DataFrame)` to avoid the jinja issues.
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REGR: concat of Sparse with incompatible dtype now gives Sparse[object] instead of object
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| 1
| 2020-05-23T12:00:32Z
| 2020-05-29T16:03:30Z
| 2020-05-29T16:03:30Z
|
MEMBER
| null |
Regression on master related to the `common_dtype` mechanism, I suppose:
```
In [5]: pd.__version__
Out[5]: '1.0.3'
In [6]: s1 = pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0))
In [7]: s2 = pd.Series(["a", "b", "c"], dtype="category")
In [8]: pd.concat([s1, s2])
Out[8]:
0 1
1 0
2 2
0 a
1 b
2 c
dtype: object
In [9]: pd.concat([s2, s1])
Out[9]:
0 a
1 b
2 c
0 1
1 0
2 2
dtype: object
```
(and the same on v0.25.3)
But on master:
```
# raising in SparseDtype._get_common_dtype
In [3]: pd.concat([s1, s2])
...
TypeError: data type not understood
In [4]: pd.concat([s2, s1])
Out[4]:
0 a
1 b
2 c
0 1
1 0
2 2
dtype: Sparse[object, 0]
```
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MDExOlB1bGxSZXF1ZXN0NDIyMjY0NTM4
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don't need len around shape
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| 2020-05-23T12:02:59Z
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|
NONE
| null |
#Fix #34274
- [x] closes #34274
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: fix concat of Sparse with non-sparse dtypes
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| 2
| 2020-05-23T13:02:59Z
| 2020-06-07T09:37:06Z
| 2020-05-29T16:03:31Z
|
MEMBER
| null |
Closes #34336
This adds tests with the behaviour as it was on 0.25.3 / 1.0.3, and some changes to get back to that behaviour.
(but whether this behaviour is fully "sane", I am not sure ..)
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BUG: Fix concat of frames with extension types (no reindexed columns)
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| 9
| 2020-05-23T13:11:22Z
| 2020-09-07T10:52:48Z
| 2020-06-19T09:16:11Z
|
MEMBER
| null |
This PR tries to fix the case of concatting dataframes with extension dtypes and non-extension dtypes, like this example (giving object dtype on master, instead of preserving Int64 dtype):
```
In [15]: df1 = pd.DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
...: df2 = pd.DataFrame({"a": np.array([4, 5, 6])})
In [16]: pd.concat([df1, df2])
Out[16]:
a
0 1
1 2
2 3
0 4
1 5
2 6
In [17]: pd.concat([df1, df2]).dtypes
Out[17]:
a object
dtype: object
```
Up to now, the `_get_common_dtype` machinery of the ExtensionArrays was only used for concatting Series (`axis=0` in `concat_compat`). Now I also try to make use of this for DataFrame columns, if there is any extension dtype in the to-concat arrays. This is checked in `_concatenate_join_units`. And we need to handle the dimension-mismach. I decided to handle that inside the internals code (reducing the dimension for non-EAs, and if the concat result is not an EA, adding back a dimension), so `concat_compat` does not need to be aware of this.
Partly overlapping with https://github.com/pandas-dev/pandas/pull/33522 (cc @TomAugspurger), but this PR does *not yet* fix the original case the other PR is fixing: concat with reindexed (thus all missing) columns becoming object instead of preserving object dtype.
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DOC: Fix reference, old file was separated last year
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| 2
| 2020-05-23T13:13:23Z
| 2020-05-27T16:30:04Z
| 2020-05-26T23:03:19Z
|
MEMBER
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The comment referenced a file deleted last year after separating the tests into a few files. I referenced the correct folder again
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|
BUG: Failing clipboard tests
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| 2020-05-23T15:25:42Z
| 2020-08-21T17:05:41Z
| 2020-05-23T20:17:21Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
>>> pd.__version__
'1.1.0.dev0+1655.g1132aba3e'
pytest pandas/tests/io/test_clipboard.py
```
#### Problem description
Final three tests fail. Looks like the test is designed for Windows, I'm running a Mac. The test references [PR#25040](https://github.com/pandas-dev/pandas/pull/25040)
**EDIT:** Just realized I'm actually running it in a Docker container. From Pyperclip documentation, looks like I need either gtk or PyQt4 (for Linux) or pbcopy and pbpaste (for Mac) installed. I suppose that should be included as a dependency in the Dockerfile?
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REF: cdef BusinessHourMixin
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Added missing import in boxplot_frame_groupby example
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BUG: don't plot colorbar if c is column containing colors
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BUG: Clipboard tests failing in Dockerfile
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| 2020-05-27T19:26:09Z
| 2020-05-27T19:26:09Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pytest pandas/tests/io/test_clipboard.py
```
#### Problem description
Last three clipboard tests are failing in a Docker image running on Mac. Dug into the pyperclip code and the issue appears to be that the Docker container does not have a DISPLAY environment variable set correctly. Pyperclip code does not recognize my machine as a Mac, so it tries to proceed as if its a Linux.
I've tried manually setting it, and the tests run through completion but the assertions fail and I get `Could not open display`. Does anyone have an idea how to fix this? Or is Dockerfile not supported for Macs?
|
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MDExOlB1bGxSZXF1ZXN0NDIyMzgzODUy
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|
DOC: Fix error in pandas.Index.to_series
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| 7
| 2020-05-24T08:56:13Z
| 2020-05-25T16:25:46Z
| 2020-05-25T16:13:44Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.Index.to_series`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
Notes:
While working with the documentation, I noticed that index parameter in `pandas.Index.to_series` has different behaviour with `pandas.Index.to_frame`. You can't specify index parameter to False in `pandas.Index.to_series` to achieve the same index result with `pandas.Index.to_frame`. Is this intentional?
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retrieve unwanted things later
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| 2020-05-24T09:40:51Z
| 2020-08-21T17:05:41Z
| 2020-05-24T13:22:09Z
|
NONE
| null |
If I'm trying to make some data processing and I want to hide unwanted symbols or html codes during processing stage and I want to popup those unwanted things again on their origin place within the text after finishing.
My question is : is that doable ? if yes .. how to do that ?
Thanks
|
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BUG: set Index parameter to False in Index.to_series doesn't work as expected
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| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
In [1]: import pandas as pd
In [2]: idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
In [3]: idx.to_frame(index=False)
Out[3]:
animal
0 Ant
1 Bear
2 Cow
In [4]: idx.to_series(index=False)
```
#### Traceback
<details>
```python
TypeError Traceback (most recent call last)
<ipython-input-4-bc071128e429> in <module>
----> 1 idx.to_series(index=False)
pandas\core\indexes\base.py in to_series(self, index, name)
1053 name = self.name
1054
-> 1055 return Series(self.values.copy(), index=index, name=name)
1056
1057 def to_frame(self, index: bool = True, name=None):
pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
239
240 if index is not None:
--> 241 index = ensure_index(index)
242
243 if data is None:
pandas\core\indexes\base.py in ensure_index(index_like, copy)
5557 index_like = copy_func(index_like)
5558
-> 5559 return Index(index_like)
5560
5561
pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
403
404 elif data is None or is_scalar(data):
--> 405 raise cls._scalar_data_error(data)
406 elif hasattr(data, "__array__"):
407 return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs)
TypeError: Index(...) must be called with a collection of some kind, False was passed
```
</details>
#### Problem description
You can't specify index parameter to False in `pandas.Index.to_series` to achieve the same index result with `pandas.Index.to_frame`. It would be more intuitive if we could also specify index to False in `pandas.Index.to_series` and get default index, to match its counterpart.
#### Expected Output
```python
In [4]: idx.to_series(index=False)
Out[4]:
0 Ant
1 Bear
2 Cow
Name: animal, dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 6ea1855489f5b1cc885ba1163451ccb717686ceb
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.16299
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.1.0.dev0+1650.g6ea185548
numpy : 1.17.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0.post20200518
Cython : 0.29.19
pytest : 5.4.2
hypothesis : 5.15.1
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.2
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.1
</details>
If possible, I would really love to work on this issue.
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| 623,840,147
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MDExOlB1bGxSZXF1ZXN0NDIyMzk1NDUz
| 34,352
|
Use repr of fill_value in SparseDtype repr
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| 7
| 2020-05-24T10:51:24Z
| 2020-05-26T06:36:09Z
| 2020-05-25T19:47:13Z
|
CONTRIBUTOR
| null |
This proposes changing the StringDtype repr to show the fill_value's repr rather than its string representation:
```python
>>> arr = pd.arrays.SparseArray([0, 1])
>>> arr.dtype
Sparse[int64, 0] # master & this PR, unchanged
>>> arr.astype(str).dtype
Sparse[object, 0] # master
Sparse[object, '0'] # this PR, notice quotes
```
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ENH: should users be able to adjust subplot parameters?
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| 2020-05-28T15:38:20Z
| 2020-05-28T15:38:20Z
|
MEMBER
| null |
Noticed this while reviewing #34343
Currently, the example in the doc would look like this:
IMO there's insufficient separation between the subplots. If we change [this line](https://github.com/pandas-dev/pandas/blob/cb35d8a938c9222d903482d2f66c62fece5a7aae/pandas/plotting/_matplotlib/boxplot.py#L427) to
```python
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2, hspace=.5)
```
then this example would look like this
It seems these subplot adjustments aren't adjustable by the user.
So, should the user be allowed to pass in these parameters (e.g. `hspace`), or should the fix here be to just hardcode `hspace=.5` into the line mentioned above, and if users want extra customisability they can use matplotlib directly?
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34297 sub slow in first call
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| 2
| 2020-05-24T14:09:46Z
| 2020-05-27T16:30:00Z
| 2020-05-26T22:52:51Z
|
MEMBER
| null |
- [x] closes #34297
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
@jbrockmendel
I added a lenght check as suggested by you. I could not find another method to check the indices (nlevels for example does not work). I'm open to recommendations about adding additional checks before running into the values call.
I have added a test, which measures the execution time of both calls to `sub`. Is there a better way to test, if they are equally fast?
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TST: sparse and dense concat test GH30667
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| 1
| 2020-05-24T16:49:09Z
| 2020-05-25T12:41:33Z
| 2020-05-25T12:41:13Z
|
CONTRIBUTOR
| null |
- [x] closes #30668
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] Adds testing to ensure concat between dense and sparse
|
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MDU6SXNzdWU2MjM5NTMzNTg=
| 34,356
|
BUG: int64 and uint64 values converted to float64 when concatenated
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| 14
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| 2020-05-26T06:27:40Z
| 2020-05-26T06:27:33Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
pd.concat([
pd.Series([1,2], dtype='uint64'),
pd.Series([3,4], dtype='int64')
])
# Output:
0 1.0
1 2.0
0 3.0
1 4.0
dtype: float64
```
#### Problem description
The output datatype becomes float when the input datatypes are different integer types.
Since there are no NaN-values or decimals involved, it can be confusing that you suddenly get float output.
#### Expected Output
```py
0 1
1 2
0 3
1 4
dtype: int64
```
*union(int64, uint64) ⊆ int65, which is the same as int64 unless you have numbers close to "the edge".*
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.125-linuxkit
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 : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.6
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU2MjM5NTU2NTk=
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BUG: merge drops index components sometimes, when join is empty
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| 2020-05-24T22:06:32Z
| 2020-09-05T20:42:22Z
| null |
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
left = pd.DataFrame({'a': [1], 'i': [1]}).set_index(['a', 'i'])
right = pd.DataFrame({'i': [1]}).set_index(['i'])
df = pd.merge(
left,
right,
left_on="i",
right_on="i",
how="left")
print(df)
print(df.reset_index())
df = pd.merge(
left,
right,
right_index=True,
left_on="i",
how="left")
print(df)
print(df.reset_index())
```
#### Problem description
The first merge changes the index of the left `DataFrame` in a way, that the index component `a` is dropped.
```
Empty DataFrame
Columns: []
Index: [1]
i
0 1
```
The second merge returns the original index respectively `DataFrame` after resetting the index. I think this is the expected output.
#### Expected Output
```
Empty DataFrame
Columns: []
Index: [(1, 1)]
a i
0 1 1
```
#### Output of ``pd.show_versions()``
Was tested on master.
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MDU6SXNzdWU2MjM5NTgyNDY=
| 34,358
|
BUG: reset_index() and set_index() expands uint32 to uint64
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[] | null | 3
| 2020-05-24T22:23:19Z
| 2020-05-26T00:26:14Z
| 2020-05-24T22:36:00Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
xy = pd.Series(
index=pd.Index([1,2,3], dtype='uint32', name='x'),
data=[4,5,6], dtype='uint32', name='y'
)
>>> xy.reset_index().dtypes
x uint64
y uint32
dtype: object
>>> xy.reset_index().set_index('y').index.dtype
dtype('uint64')
```
#### Problem description
When using reset_index() or set_index(), the datatype of the affected array is expanded from uint32 to uint64. The dtype should remain unaffected.
#### Expected Output
```py
>>> xy.reset_index().dtypes
x uint32
y uint32
dtype: object
>>> xy.reset_index().set_index('y').index.dtype
dtype('uint32')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.125-linuxkit
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 : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.6
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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|
BUG: inplace parameter will not perform the operation in place on multi column-sliced DataFrame
|
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| 2020-05-25T07:43:01Z
| 2020-05-25T07:43:01Z
|
NONE
| null |
Environment: Python 3.7.7 / NumPy 1.18.4 / Pandas 1.0.3
I have been testing the `inplace=True` parameters for a couple of functions, including `DataFrame.fillna()` and `DataFrame.clip()`.
When I use the inplace parameter on a multi-column slicing of a DataFrame, it will not alter the original DataFrame.
For instance, using the simple DataFrame below,
```python
df = pd.DataFrame({'A':[1,None,5],'B':[3,4,-1]})
print(df)
Out:
A B
0 1.0 3
1 NaN 4
2 5.0 -1
```
the code
```python
df.loc[:, ['A','B']].fillna(0, inplace=True)
# or df.loc[:, 'A':'B'].fillna(0, inplace=True)
# or df.iloc[:,[0,1]].fillna(0, inplace=True)
# or df.iloc[:,0:2].fillna(0, inplace=True)
print(df)
```
will produce
```python
Out:
A B
0 1.0 3
1 NaN 4
2 5.0 -1
```
as opposed to
```python
Out:
A B
0 1.0 3
1 0.0 4
2 5.0 -1
```
Strangely enough, the parameter works perfectly when I'm slicing out just a single column
```python
df[:, 'A'].fillna(0, inplace=True)
print(df)
Out:
A B
0 1.0 3
1 0.0 4
2 5.0 -1
```
or when I'm using the position-based slicing method `.iloc` without specifying the first column
```python
df.iloc[:,:2].fillna(0, inplace=True)
print(df)
Out:
A B
0 1.0 3
1 0.0 4
2 5.0 -1
```
Let me know if this is the accepted/expected behavior or if there is a workaround.
Thanks!
|
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ENH: Correct use of `longtable`, `label`, and `caption` with LaTeX
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| 5
| 2020-05-25T02:31:13Z
| 2020-09-19T22:05:12Z
| 2020-09-19T22:05:12Z
|
NONE
| null |
#### Is your feature request related to a problem?
Currently `DataFrame.to_latex()` has a `longtable` argument, which writes the DataFrame to latex using the syntax of the longtable package. When used with the `label` and `caption` arguments, it writes a latex table with all of these features.
However, the caption and label are both placed between a `\begin{longtable}` line and `\endhead`. As a consequence, the table appears twice in the `listoftables` and leads to warnings about the reference being multiply defined
#### Describe the solution you'd like
the `.to_latex()` method when `longtable=True` and `caption=X` and `label=Y` should print out as follows
```latex
\begin{longtable}{...}% alignment characters
\caption{Caption here}\label{label-here}
\endfirsthead
\caption{Caption here}
\endhead
```
This will prevent a separate label from being defined multiple times in latex (once for each page the caption is printed on). See page 5 of http://www.texdoc.net/texmf-dist/doc/latex/tools/longtable.pdf.
#### API breaking implications
None
#### Describe alternatives you've considered
An alternative is to set the caption and label to False, and to add them myself. This works but defeats the purpose of calling the `.to_latex()` API
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BUG: Series.value_counts(normalize=True) fails with Int64Dtype
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| 2
| 2020-05-25T05:05:43Z
| 2020-06-03T08:14:21Z
| 2020-05-25T06:10:23Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.Series([1,2,3, None], dtype=pd.Int64Dtype()).value_counts(normalize=True)
```
Traceback:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/.pyenv/versions/pandas-test/lib/python3.8/site-packages/pandas/core/base.py", line 1229, in value_counts
result = value_counts(
File "/home/xxx/.pyenv/versions/pandas-test/lib/python3.8/site-packages/pandas/core/algorithms.py", line 731, in value_counts
result = result / float(counts.sum())
AttributeError: 'IntegerArray' object has no attribute 'sum'
```
#### Problem description
An error occurd when I called `Series.value_counts(normalize = True)` with Int64Dtype.
#### Expected Output
The result probably should be:
```
3 0.333333
2 0.333333
1 0.333333
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-6-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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DOC: Fix EX02 and SA01 in DataFrame.select_dtypes
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| 1
| 2020-05-25T08:04:59Z
| 2020-05-27T04:37:08Z
| 2020-05-27T04:37:02Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.DataFrame.select_dtypes`:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No extended summary found
```
Notes:
It seems like passing `int` to the parameter do not include/exclude all integer dtypes. But, passing `integer` would do otherwise. I think it is related to this issue https://github.com/pandas-dev/pandas/issues/29394. It would be more convenient if passing `int` as an input would match the result with passing `integer`.
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|
CONTRIBUTOR
| null |
- [x] closes #34256
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Unexpected behaviour
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[] | null | 6
| 2020-05-25T11:58:18Z
| 2021-08-07T22:20:01Z
| null |
NONE
| null |
Hello !
I discovered an unexpected behaviour while using pandas
## Code Sample
```
import pandas as pd
def process(df):
df.index = df["A"]
df.loc['first', 'A'] = 'first'
print("Before")
print(df)
print()
df.loc['first', 'B'] = 99
print("After")
print(df)
print("First processing : without csv file :\n")
df = pd.DataFrame({'A': ["first", "second"], 'B': [3,4]})
process(df)
print("\n\nSecond processing : via a csv file :\n")
df = pd.DataFrame({'A': ["first", "second"], 'B': [3,4]})
df.to_csv("df.csv",index=False)
df = pd.read_csv("df.csv")
process(df)
```
## Problem description
The 2 output that `process(df)` produces are different if `df` have been previously exported to a csv file or not.
## Expected output
```
First processing : without csv file :
Before
A B
A
first first 3
second second 4
After
A B
A
first first 99
second second 4
Second processing : via a csv file :
Before
A B
A
first first 3
second second 4
After
A B
A
first first 99
second second 4
```
## Actual output
```
First processing : without csv file :
Before
A B
A
first first 3
second second 4
After
A B
A
first first 99
second second 4
Second processing : via a csv file :
Before
A B
A
first first 3
second second 4
After
A B
A
first NaN 99.0
second second 4.0
first NaN 99.0
```
## Mea culpa
I guess that writing `df.index = df["A"]` is a bad practice because comumn `A` is still linked to the index. So when we modify column `A`, the index is changed as well. It could be good to raise a Warning to tell silly people like me that I shouldn't do that.
Thank you, and have a cute day !
[show_versions.txt](https://github.com/pandas-dev/pandas/files/4677020/show_versions.txt)
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ENH: Make DataFrame.insert more flexible
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| 2020-05-25T14:09:51Z
| 2020-05-25T23:34:25Z
| null |
CONTRIBUTOR
| null |
``DataFrame.insert`` is very inflexible today and can not really be used in pipes. I'd like to do several changes to the method:
* Add a inplace parameter, so the result from the op can be returned.
* allow a callable for ``value``, so the new values can be computed from existing column values.
* allow a dict for ``value``, so the several columns can be inserted simultaneously,
* add ``insert_after`` and ``insert_before`` parameters to allow label-based insertion location.
* move ``loc`` to the after ``value`` and let it have a default value of None, i.e. insert the new column at the end of the frame if location is not specified.
* deprecate ``DataFrame.assign``. It's functionality would be covered by the changed ``DataFrame.insert`` method.
The above change would allow us to be quite flexible when creating new columns in pipes. For example we could do
```python
df.insert("formal_name", lambda: "Mr. " + x["last_name"], insert_after="last_name")
.pipe(...)
```
The above is obviously quite a large change. It could be discussed if it would be better make it a new method instead of changing an existing one...
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DOC - Moving Tips and Trick from wiki to Style Guide - added Reading …
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|
CONTRIBUTOR
| null |
This changes referring to https://github.com/pandas-dev/pandas/issues/30964 is for removing tips and tricks section page from the [wiki](https://github.com/pandas-dev/pandas/wiki/Tips-&-Tricks-for-pandas-dev) to the [Code Style guide](https://pandas.io/docs/development/code_style.html).
Since this is my first contribution, I've tried to keep this as simple as possible. @simonjayhawkins mentioned that the conventions for urlopen and ZipFile should be captured under our contributing style guide, testing section. I've seen that there is no Testing section and have created a new section and added these two sections from the Wiki post here. Enabling the Travis-CI and Azure CI pipeline build seemed a bit tough for me. I will try to do the build from my side in my next PR.
Let me know if any corrections are needed.
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| 2
| 2020-05-25T18:06:00Z
| 2020-05-28T17:19:47Z
| 2020-05-28T17:19:47Z
|
MEMBER
| null |
```
In [7]: arr = np.random.randint(0, 1000, (100, 10))
In [8]: df1 = pd.DataFrame(arr)
In [9]: df2 = df1.copy()
...: df2.iloc[0, [1, 3, 7]] = np.nan
In [10]: df1 + df2
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-10-fa4784095cc3> in <module>
----> 1 df1 + df2
~/scipy/pandas/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
663 if isinstance(other, ABCDataFrame):
664 # Another DataFrame
--> 665 new_data = self._combine_frame(other, na_op, fill_value)
666
667 elif isinstance(other, ABCSeries):
~/scipy/pandas/pandas/core/frame.py in _combine_frame(self, other, func, fill_value)
5734 return func(left, right)
5735
-> 5736 new_data = ops.dispatch_to_series(self, other, _arith_op)
5737 return new_data
5738
~/scipy/pandas/pandas/core/ops/__init__.py in dispatch_to_series(left, right, func, axis)
283
284 array_op = get_array_op(func)
--> 285 bm = left._mgr.operate_blockwise(right._mgr, array_op)
286 return type(left)(bm)
287
~/scipy/pandas/pandas/core/internals/managers.py in operate_blockwise(self, other, array_op)
358 Apply array_op blockwise with another (aligned) BlockManager.
359 """
--> 360 return operate_blockwise(self, other, array_op)
361
362 def apply(self: T, f, align_keys=None, **kwargs) -> T:
~/scipy/pandas/pandas/core/internals/ops.py in operate_blockwise(left, right, array_op)
34 right_ea = not isinstance(rblk.values, np.ndarray)
35
---> 36 lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)
37
38 res_values = array_op(lvals, rvals)
~/scipy/pandas/pandas/core/internals/ops.py in _get_same_shape_values(lblk, rblk, left_ea, right_ea)
84
85 # Require that the indexing into lvals be slice-like
---> 86 assert rblk.mgr_locs.is_slice_like, rblk.mgr_locs
87
88 # TODO(EA2D): with 2D EAs pnly this first clause would be needed
AssertionError: BlockPlacement([0 2 4 5 6 8 9])
```
cc @jbrockmendel I suppose caused by the frame-frame blockwise PR (https://github.com/pandas-dev/pandas/pull/32779), but didn't yet look into detail
Just removing the assertion seems to still work (for this case), but I don't know if that `_get_same_shape_values` functino relies on the `is_slice_like` characteristic for its implementation to be correct
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| 34,368
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BUG: new method for join with multiindex and single index (#34292)
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[] | null | 3
| 2020-05-25T19:19:58Z
| 2020-11-29T04:47:14Z
| 2020-11-29T04:47:14Z
|
CONTRIBUTOR
| null |
- [x] closes #34292
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Tests still to be added.
I replaced the method to join a multiindex & a single index because outer joins didn't include data from the single index. The new method is similar to a join between 2 multiindexes.
I'm not sure what the keep_order parameter was, but it was never used and in other _join-methods it didn't exist either. So I suggest removing it all together.
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Backport PR #32479 on branch 1.0.x (BUG: Fix issue with datetime[ns, tz] input in Block.setitem)
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| 0
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|
MEMBER
| null |
xref #32479, #32395
regression in 1.0.0
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CLN Replace old string formatting syntax with f-strings #29547: 2 files w…
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| 2
| 2020-05-25T20:32:04Z
| 2020-05-28T21:44:46Z
| 2020-05-26T13:00:16Z
|
CONTRIBUTOR
| null |
Hello, i'm new to github / opensource. Let me know if I'm doing things correctly
pandas/_libs/tslibs/timedeltas.pyx
pandas/_libs/tslibs/timestamps.pyx
Replace old string formatting syntax with f-strings #29547
- [ x ] passes `black pandas`
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DOC: GH4607 added clarification to business DateOffset
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| 1
| 2020-05-25T21:01:34Z
| 2020-05-27T01:00:57Z
| 2020-05-26T13:07:03Z
|
CONTRIBUTOR
| null |
- [x] closes #4607
Added clarifying details to Business Year, Quarter, and Month, End and Begin docs
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CLN: Unify signatures in _libs.groupby
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| 10
| 2020-05-25T22:11:25Z
| 2020-07-11T16:01:52Z
| 2020-06-18T22:59:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/33630#discussion_r428737327
The main goal is to allow _get_cythonized_result to be used with the cython implementation of var. In order to do this, it was best to modify the signatures in _libs.groupby of
- group_any_all;
- _group_var; and
- group_quantile
so that the order of the arguments matches other cython functions (e.g. those used in ops.BaseGrouper.aggregate or transform).
Note this PR has the fix for #33955 that is also in #33988 - that PR should be merged first.
Tests using %timeit on dataframes of various shapes (code for generating is at the bottom):
I looked into the last row. 26.7% of the time is spent in just iterating over the columns, 12.8% in the cython call, and 35.7% in the call to ``_wrap_aggregated_output``.
While working on this, it seemed best to implement ``ddof != 1`` in cython for var. The following code takes 26.5ms on master and 3.88ms in this PR.
````
ncol, order = 1, 6
df = pd.DataFrame(np.random.uniform(low=0.0, high=10.0, size=(10**order, ncol)))
df['key'] = np.random.randint(0, 50, (10**order, 1))
g = df.groupby('key')
t = %timeit -o g.std(ddof=4)
````
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BUG: test_finalize.py tests failing on master with pytest-randomly
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MEMBER
| null |
On Ubuntu18 im seeing a variable number of tests in test_binops failing with empty `.attrs`
cc @TomAugspurger
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CLN: remove _combine_series_frame
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| 2
| 2020-05-25T22:50:33Z
| 2020-05-26T01:21:43Z
| 2020-05-26T01:17:04Z
|
MEMBER
| null |
Following this we can consolidate some dispatch_to_series calls, saving that for a separate step for clarity of exposition.
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BUG:#29928 Fix to_json output 'table' orient for single level MultiIndex.
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NONE
| null |
`dataframe.to_json()` was writing incorrect index field name, so applying read_json resulted in NaN index values.
`dataframe.to_json()` now converts single level MultiIndex into single Index before encoding.
- [x] closes #29928
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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DOC: Add D-Tale to Visualizations section of Pandas Ecosystem
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| 2
| 2020-05-26T01:31:32Z
| 2020-05-27T18:28:32Z
| 2020-05-27T18:28:32Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[Pandas Ecosystem - Visualization](https://pandas.pydata.org/docs/ecosystem.html#visualization)
#### Documentation problem
This isn't a problem, just a suggestion for improving the current ecosystem listing.
#### Suggested fix for documentation
[D-Tale](https://github.com/man-group/dtale) is a open-source tool for visualizing pandas data structures and it has really started to gain some traction in the data science community. 1K+ stars since 9/2019. I think it would be nice addition to the visualization section of "Pandas Ecosystem".
Here's an example of the listing for it, but feel free to write your own (assuming you think it should be added):
### [D-Tale](https://github.com/man-group/dtale)
D-Tale is the combination of a Flask back-end and React front-end to bring you an easy way to view, edit & analyze Pandas data structures. Also, includes a fully-featured chart builder by way of Plotly Dash. It integrates seamlessly with jupyter notebooks, python terminals, kaggle. Currently this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex. A nice demo is located [here](http://alphatechadmin.pythonanywhere.com/).
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MDExOlB1bGxSZXF1ZXN0NDIyOTQ5MDQw
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BUG: pd.DataFrame.transform recursively loops in some cases #34224
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| 2020-05-26T02:02:16Z
| 2020-06-03T18:05:44Z
| 2020-06-03T18:05:38Z
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CONTRIBUTOR
| null |
- [x] closes #34224
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Error handling within try/except in aggregate function of frame.py file
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REF: Week, Semi-Month, FY5253 offsets to liboffsets
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| 1
| 2020-05-26T03:22:19Z
| 2020-05-26T22:20:00Z
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MEMBER
| null |
Bigger chunk than usual.
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BUG/API: to_timedelta unit-argument ignored for string input
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CONTRIBUTOR
| null |
Supersedes #23025
- [x] closes #12136
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [x] merge master
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BUG:
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| 15
| 2020-05-26T06:14:40Z
| 2020-08-21T17:05:42Z
| 2020-06-10T15:58:18Z
|
NONE
| null |
From [SO](https://stackoverflow.com/q/62015410/2901002):
---
After specified column after groupby output is incorrect.
df = pd.DataFrame({'token': [12345.0, 12345.0, 12345.0, 12345.0, 12345.0, 12345.0, 6789.0, 6789.0, 6789.0, 6789.0, 6789.0, 6789.0], 'name': ['abc', 'abc', 'abc', 'abc', 'abc', 'abc', 'xyz', 'xyz', 'xyz', 'xyz', 'xyz', 'xyz'], 'ltp': [2.0, 5.0, 3.0, 9.0, 5.0, 16.0, 1.0, 5.0, 3.0, 13.0, 9.0, 20.0], 'change': [np.nan, 1.5, -0.4, 2.0, -0.44444399999999995, 2.2, np.nan, 4.0, -0.4, 3.333333, -0.307692, 1.222222]})
print (df)
token name ltp change
0 12345.0 abc 2.0 NaN
1 12345.0 abc 5.0 1.500000
2 12345.0 abc 3.0 -0.400000
3 12345.0 abc 9.0 2.000000
4 12345.0 abc 5.0 -0.444444
5 12345.0 abc 16.0 2.200000
6 6789.0 xyz 1.0 NaN
7 6789.0 xyz 5.0 4.000000
8 6789.0 xyz 3.0 -0.400000
9 6789.0 xyz 13.0 3.333333
10 6789.0 xyz 9.0 -0.307692
11 6789.0 xyz 20.0 1.222222
---
Correct using named aggregation:
df0 = df.groupby('name').agg(pos=pd.NamedAgg(column='change',aggfunc=lambda x: x.gt(0).sum()),\
neg = pd.NamedAgg(column='change',aggfunc=lambda x:x.lt(0).sum()))
print (df0)
pos neg
name
abc 3.0 2.0
xyz 3.0 2.0
Wrong output:
df1 = df.groupby('name')['change'].agg(pos = pd.NamedAgg(column='change',aggfunc=lambda x:x.gt(0).sum()),\
neg = pd.NamedAgg(column='change',aggfunc=lambda x:x.lt(0).sum()))
print (df1)
pos neg
name
abc 2.0 2.0
xyz 2.0 2.0
Correct using column after groupby:
df2 = df.groupby('name')['change'].agg(pos = lambda x:x.gt(0).sum(),\
neg = lambda x:x.lt(0).sum())
print (df2)
pos neg
name
abc 3.0 2.0
xyz 3.0 2.0
In my opinion instead wrong output it should raise error/warning.
Version of pandas:
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
None
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MDU6SXNzdWU2MjQ2NzI1MDI=
| 34,381
|
BUG: pd.tseries.frequencies.to_offset() does not really provide a DateOffset object
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open
| false
| null |
[] | null | 4
| 2020-05-26T07:50:29Z
| 2021-08-07T22:22:08Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from datetime import datetime
data=pd.DataFrame([[430., 630.],
[540., 660.],
[610., 720.]],
columns=[1, 2],
index=pd.date_range(datetime(2019, 1, 1, 12),
datetime(2019, 1, 1, 12, 30),
freq='15min'))
pd.tseries.frequencies.to_offset(pd.infer_freq(data.index))
>> <15 * Minutes>
pd.tseries.frequencies.to_offset(pd.infer_freq(data.index)) / 2
>> <450 * Secondes>
pd.tseries.offsets.DateOffset(minutes=15)
>> <DateOffset: minutes=15>
pd.tseries.offsets.DateOffset(minutes=15) / 2
>> TypeError: unsupported operand type(s) for /: 'DateOffset' and 'int'
```
#### Problem description
The function `pd.tseries.frequencies.to_offset` does not really provide a `DateOffset`object. As I understand the code right there are several types of DateOffsets e.g. Minutes, Seconds that are not compareable with the DateOffset object because they are having different properties.
#### Expected Output
One Unified pd.DateOffset Object with same properties for all inherit Objects like Minutes and Seconds
#### Output of ``pd.show_versions()``
<details>
[INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-99-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.16.0
pytz : 2018.9
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : 0.28.3
pytest : 4.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.3.0
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 2.2.2
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 4.3.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.4.2
tabulate : 0.8.5
xarray : 0.10.9
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
numba : 0.42.0
]
</details>
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MDExOlB1bGxSZXF1ZXN0NDIzMDU5MDQx
| 34,382
|
Backport PR #33644 on branch 1.0.x (BUG: Groupby quantiles incorrect bins)
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| 0
| 2020-05-26T08:26:15Z
| 2020-05-26T09:14:57Z
| 2020-05-26T09:14:50Z
|
MEMBER
| null |
xref #33644, #33200, #33569
regression in #20405 (0.25.0)
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|
DOC: Indexing and selecting data and the use of mask/where
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| 2
| 2020-05-26T09:18:32Z
| 2020-06-02T22:57:43Z
| 2020-06-02T22:57:43Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#evaluation-order-matters
#### Documentation problem
The documentation talks about how **not** to use `dfb['c'][dfb['a'].str.startswith('o')] = 42` for assignment of multiple values to a `pandas.Series` or `pandas.DataFrame` column. it does however fail to make a concrete suggestion how to do it given the example.
#### Suggested fix for documentation
I suggest to mention the `pandas.Series.where` and `pandas.Series.mask` methods here in order to show how the assignment can be done, i.e. by adding an example like:
```python
dfb['c'].mask(dfb['a'].str.startswith('o'), 42)
```
which performs the desired assigment of rows matching `o` to `42`. I am happy to contribute this as a PR myself.
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|
TST: use ensure_clean rather than explicit os.remove
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| 2
| 2020-05-26T13:04:56Z
| 2020-10-12T15:11:12Z
| 2020-10-12T15:11:12Z
|
CONTRIBUTOR
| null |
```
~/pandas-dev$ grep -r os.remove pandas/tests/
pandas/tests/io/excel/test_openpyxl.py: os.remove(filename)
pandas/tests/io/excel/test_writers.py: # os.remove(filename)
pandas/tests/io/excel/test_writers.py: # os.remove(filename)
pandas/tests/io/pytables/common.py: os.remove(path)
```
we should also create a rule in code_checks.sh to avoid doing this in the future
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use ensure_clean rather than explicit os.remove #34384
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| 2020-05-26T13:53:08Z
| 2020-10-13T07:32:40Z
| 2020-10-12T15:11:12Z
|
CONTRIBUTOR
| null |
closes #34384
I have made a start at this issue #34384. Please let me know if I am along the right lines (beginner contributor).
I've left a space in code_checks.sh where I expect to implement the check for instances of 'os.remove' throughout the code.
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CLN: de-duplicate dispatch_to_series calls
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| 0
| 2020-05-26T14:16:03Z
| 2020-05-26T22:20:34Z
| 2020-05-26T22:12:46Z
|
MEMBER
| null |
Unwrap td64/dt64 so those operate blockwise.
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REF: is_subperiod, is_superperiod out of libfreqs
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| 2
| 2020-05-26T14:39:51Z
| 2020-05-26T22:10:25Z
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MEMBER
| null |
When we first moved these functions from tseries.frequencies to libfrequencies a couple years ago there was a question of it was really necessary, and the answer is now an unambiguous "no"
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BUG: pd.DataFrame.add() does not broadcast across levels, when level is None
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| 7
| 2020-05-26T15:01:16Z
| 2021-10-21T01:50:26Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# create dataframe
df1 = pd.DataFrame({'A': [0, 1, 2], 'B': [1, 2, 3]})
df1.columns.set_names('L1', inplace=True)
# create dataframe with multiindex in column
df2 = pd.DataFrame({('A', 'C'): [0, 0, 0], ('A', 'D'): [0, 0, 0]})
df2.columns.set_names(['L1', 'L2'], inplace=True)
# returns dataframe of nans
df1.add(df2)
# returns dataframe with valid values
df1.add(df2, level=0)
```
#### Problem description
In pandas 0.23.2 the behavior was correct where level=None was treated as level=0 and being broadcasted. However in pandas 1.0.3, it appears as though a bug got introduced.
#### Expected Output
Out[1]:
A
C D
0 0 0
1 1 1
2 2 2
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.13-1.el7.elrepo.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.49.1
</details>
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CLN: _consolidate_inplace less
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BUG: Result of rolling mean depends on more observations than are in window
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| 2020-05-26T16:31:34Z
| 2020-05-28T16:39:15Z
| 2020-05-28T16:39:14Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
import datetime as dt
data = np.array([0.1552, 0.1746, 0.1932, 0.234 , np.nan, 0.2423, 0.1648,
np.nan, 0.2148, 0.2081, 0.2313, 0.2011, np.nan, 0.2076,
0.2096, np.nan, 0.1801, 0.1872, 0.1878, 0.1949, np.nan,
0.1608, np.nan, np.nan, 0.1793, np.nan, 0.1689, 0.1631,
np.nan, 0.1586, 0.1531, np.nan, 0.149 , 0.1434, np.nan,
0.1526, np.nan, 0.1293, 0.1268, np.nan])
dates_pandas = pd.date_range(start=dt.date(2020, 3, 24), periods=data.shape[0])
ser = pd.Series(data, index=dates_pandas)
full_mean = ser.rolling(window="20D",
min_periods=2,
center=False,
closed='both').mean()
shorter_mean = ser['2020-03-25':].rolling(window="20D",
min_periods=2,
center=False,
closed='both').mean()
print(full_mean['2020-05-02'], full_mean['2020-05-02'].round(4))
print(shorter_mean['2020-05-02'], shorter_mean['2020-05-02'].round(4))
```
##### Output
```
0.15664999999999998 0.1566
0.15665000000000004 0.1567
```
#### Problem description
The rolling mean in the example should only take the last 20 values into account. The output for the last day `2020-05-02` does however depend on the inclusion of a value on `2020-03-24`.
This is especially visible if we round the output to the 4 digit precision that the input data has.
#### Expected Output
Both running means should be the same value.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.12-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191101
Cython : 0.29.13
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.0
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
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REF: BusinessDay, BusinessHour to liboffsets
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MDExOlB1bGxSZXF1ZXN0NDIzMzgyNTAz
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ENH: Set index of returned dataframe in DatetimeIndex.isocalendar
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| 5
| 2020-05-26T18:36:43Z
| 2021-10-29T14:22:50Z
| 2020-05-27T12:36:13Z
|
CONTRIBUTOR
| null |
This is a followup on #33595, (specifically [here](https://github.com/pandas-dev/pandas/pull/33595#issuecomment-614888200) and [here](https://github.com/pandas-dev/pandas/pull/33595#issuecomment-614897268)), where it was noted that `DatetimeIndex.isocalendar` should set the index of the
returned dataframe.
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|
DOC: add a semicolon to hide long matplotlib axes return value
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CONTRIBUTOR
| null |
add a semicolon to hide debug messages like:
Out[85]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0ad6e3d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0ad4a350>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0aa10250>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a6367d0>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d088c2d50>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d089ea310>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d08d89890>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a76ae10>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a756090>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a8dd750>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d096aaed0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0937d490>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d09f49a10>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a0551d0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a418550>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f3d0a6f0ad0>]],
dtype=object)
which you can see here:
https://pandas.pydata.org/docs/user_guide/visualization.html
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: "Setting layouts" plots' label size GH34305
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| 2020-05-26T20:19:42Z
| 2020-06-07T21:23:16Z
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|
CONTRIBUTOR
| null |
- [X] closes #34305
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MjUyMTQ3OTA=
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|
BUG: OverflowError on to_json with numbers larger than sys.maxsize
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| 8
| 2020-05-26T21:36:29Z
| 2020-06-24T23:44:41Z
| 2020-06-24T23:44:41Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import sys
from pandas.io.json import dumps
dumps(sys.maxsize)
dumps(sys.maxsize + 1)
```
#### Problem description
The Pandas JSON dumper doesn't seem to handle number values larger than `sys.maxsize` (a word). I have a dataframe that I'm trying to write to_json, but it's failing with `OverflowError: int too big to convert`. There are some numbers larger than `9223372036854775807` in it.
Passing a `default_handler` doesn't help. It doesn't get called for the error.
```python
>>> dumps(sys.maxsize)
'9223372036854775807'
>>> dumps(sys.maxsize + 1, default_handler=str)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: int too big to convert
```
#### Expected Output
Python's built-in json module handles large numbers without issues.
```python
>>> import json
>>> json.dumps(sys.maxsize)
'9223372036854775807'
>>> json.dumps(sys.maxsize+1)
'9223372036854775808'
```
I expect Pandas to be able to output large numbers to JSON. An option to use the built-in `json` module instead of `ujson` would be fine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDIzNDg3NzA5
| 34,396
|
DOC: added D-Tale to the visualizations section of Ecosystem page
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| 2
| 2020-05-26T22:02:51Z
| 2020-05-27T18:34:13Z
| 2020-05-27T18:28:32Z
|
CONTRIBUTOR
| null |
- [x] closes #34376
Added [D-Tale](https://github.com/man-group/dtale) to the visualizations section of [Pandas Ecosystem](https://pandas.pydata.org/docs/ecosystem.html) doc
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CLN: GH29547 Replace old string formatting with f-strings
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| 2020-05-26T22:13:31Z
| 2020-05-27T02:11:48Z
| 2020-05-27T02:11:41Z
|
CONTRIBUTOR
| null |
Is it normal for all my old commits on other branches to appear here? Am I doing something wrong?
|
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REF: move remaining offsets into liboffsets
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| 3
| 2020-05-27T00:51:00Z
| 2020-05-27T17:32:54Z
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MEMBER
| null |
this _doesnt_ make them all cdef classes yet; need to get #34345 figured out before that is doable.
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CLN: GH29547 format with f-strings
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| 2
| 2020-05-27T02:20:12Z
| 2020-05-28T19:17:24Z
| 2020-05-28T17:15:01Z
|
CONTRIBUTOR
| null |
Sorry all, new PR. Didn't branch off master for the last one.
#29547
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CLN: de-duplicate paths in tslibs
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CI: Remove unused import in offsets.py
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BUG/API: other object type check in Series/DataFrame.equals
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MEMBER
| null |
xref https://github.com/geopandas/geopandas/issues/1420
First, this PR is fixing the "bug" that we shouldn't rely on `_constructor` being a class that can be used in `isinstance` (see https://github.com/pandas-dev/pandas/issues/32638 for the general discussion about this, this is the only place in our code where `_constructor` is used like this, AFAIK).
And even if `_constructor` would be a class, it wouldn't necessarily be the correct class to check with (or not more correct than `type(self)`)
But, so this also brings up the API question: what are the "requirements" we put on the type of `other` ? Should it be the same type? (as then could also change it to `if not type(self) is type(other): ...`)
Or is a subclass sufficient (with `isinstance`) ?
The problem with an isinstance checks with subclasses is that then the order matters (eg `subdf.equals(df)` would not necessarily give the same answer as `df.equals(subdf)`)
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MDU6SXNzdWU2MjU0OTU3MDE=
| 34,403
|
pandas.core.base.DataError: No numeric types to aggregate
|
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| 2020-05-27T08:37:00Z
| 2020-06-05T18:49:23Z
| 2020-05-28T16:40:28Z
|
NONE
| null |
aggregating a boolean fields doesn't allow averaging the data column in the latest version. Is there a new alternative of doing this for boolean attributes, or it should only be handled by transforming it to a int/float only?
data[ group_fields + [ bool_field ]].groupby( group_fields ).mean() produces the error mentioned in subject.
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MDU6SXNzdWU2MjU1OTAyNzI=
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|
BUG: Memory leak when calling an apply with datetime.fromtimestamp
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| 2020-05-27T13:09:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
import psutil
import os
from datetime import datetime
def process_samples(df):
df['time'] = df['time'].apply(lambda x: datetime.fromtimestamp(x//10**3))
df = df.rename(columns={'time':f'time_timestamp' })
df = pd.DataFrame({'time':np.random.randint(0,1000, size=30)})
process = psutil.Process(os.getpid())
beg = process.memory_info().rss/1024/1024
for i in range(1000000):
process_samples(df.copy())
end = process.memory_info().rss/1024/1024
if i % 500 ==0:
print(f'Memory after {i} iterations ', end, ', difference w.r.t. beggining',end-beg)
```
#### Problem description
We have identified a memory leak in our code when executing a combination of an apply and a rename. If we do any of these two steps without the other, there is no leak.
The apply simply uses datetime.fromtimestamp to convert POSIX timestamp to a date. We understand that we achieve similar results with the apply using time pd.to_datetime(), but still believe there could be a memory leak with the apply method in general that should be investigated
If we use datetime.fromtimestamp to convert the POSIX timestamps to dates without pandas there is no leak.
#### Expected Output
Memory consumption should not increase and stay constant
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.16.0
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 45.1.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.2.16
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.1
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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DOC: add link to benchmarks page in Development docs
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| 2020-05-27T12:55:18Z
| 2020-05-27T23:12:06Z
| 2020-05-27T23:12:06Z
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CONTRIBUTOR
| null |
link is: https://pandas.pydata.org/speed/pandas/#/
also could add somewhere: https://github.com/pandas-dev/pandas/blob/master/README.md
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PERF: asv runner is down
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| 2020-05-29T19:09:54Z
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CONTRIBUTOR
| null |
see https://github.com/pandas-dev/pandas/pull/34389#issuecomment-634641337
@TomAugspurger said the asv runner has segfaulted, this is a tracking issue.
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REGR: revert "CLN: _consolidate_inplace less" / fix regression in fillna()
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| 49
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MEMBER
| null |
Reverts pandas-dev/pandas#34389
Closes #36495
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DOC: add link to benchmarks page in developer docs
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| 2
| 2020-05-27T15:33:41Z
| 2020-05-27T23:12:11Z
| 2020-05-27T23:12:06Z
|
CONTRIBUTOR
| null |
- [x] closes #34405
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew
@jreback
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| 625,803,626
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MDExOlB1bGxSZXF1ZXN0NDIzOTM4MzU1
| 34,409
|
Initialize variables in pqyear, pquarter
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| 2020-05-27T15:38:05Z
| 2020-05-27T16:30:08Z
| 2020-05-27T16:25:05Z
|
CONTRIBUTOR
| null |
This resolves the `-Wmaybe-uninitialized` warning observed when building with the manylinux1 docker image
```
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/tslibs -I./pandas/_libs/tslibs -I/opt/python/cp38-cp38/lib/python3.8/site-packages/numpy/core/include -I/opt/python/cp38-cp38/include/python3.8 -c pandas/_libs/tslibs/period.c -o build/temp.linux-x86_64-3.8/pandas/_libs/tslibs/period.o
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pqyear’:
pandas/_libs/tslibs/period.c:12464:3: warning: ‘__pyx_v_year’ may be used uninitialized in this function [-Wmaybe-uninitialized]
return __pyx_r;
^
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pquarter’:
pandas/_libs/tslibs/period.c:12512:3: warning: ‘__pyx_v_quarter’ may be used uninitialized in this function [-Wmaybe-uninitialized]
return __pyx_r;
```
Hopefully this doesn't break anything. IIUC, these pointers are just used to set the result, so it shouldn't matter what the value is going in?
Closes https://github.com/pandas-dev/pandas/issues/34114
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32-bit compat issues in MacPython/pandas-wheels
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| 2020-05-27T16:29:45Z
| 2020-05-29T19:05:06Z
| 2020-05-29T19:05:06Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=36222&view=logs&j=c0130b29-789d-5a3c-6978-10796a508a7f&t=e120bc6c-1f5e-5a41-8f0a-1d992cd2fbfb&l=1400
A couple classes of failures
```
2020-05-27T03:46:00.4640450Z ______________ test_multiple_agg_funcs[rolling-2-expected_vals0] ______________
2020-05-27T03:46:00.4640874Z [gw0] win32 -- Python 3.7.7 D:\a\1\s\test_venv\Scripts\python.exe
2020-05-27T03:46:00.4641216Z
2020-05-27T03:46:00.4641446Z func = 'rolling', window_size = 2
2020-05-27T03:46:00.4641974Z expected_vals = [[nan, nan, nan, nan], [15.0, 20.0, 25.0, 20.0], [25.0, 30.0, 35.0, 30.0], [nan, nan, nan, nan], [20.0, 30.0, 35.0, 30.0], [35.0, 40.0, 60.0, 40.0], ...]
2020-05-27T03:46:00.4642426Z
2020-05-27T03:46:00.4642646Z @pytest.mark.parametrize(
2020-05-27T03:46:00.4642970Z "func,window_size,expected_vals",
2020-05-27T03:46:00.4643468Z [
2020-05-27T03:46:00.4643722Z (
2020-05-27T03:46:00.4643997Z "rolling",
2020-05-27T03:46:00.4644235Z 2,
2020-05-27T03:46:00.4644500Z [
2020-05-27T03:46:00.4644818Z [np.nan, np.nan, np.nan, np.nan],
2020-05-27T03:46:00.4645140Z [15.0, 20.0, 25.0, 20.0],
2020-05-27T03:46:00.4645482Z [25.0, 30.0, 35.0, 30.0],
2020-05-27T03:46:00.4645799Z [np.nan, np.nan, np.nan, np.nan],
2020-05-27T03:46:00.4646157Z [20.0, 30.0, 35.0, 30.0],
2020-05-27T03:46:00.4646503Z [35.0, 40.0, 60.0, 40.0],
2020-05-27T03:46:00.4646811Z [60.0, 80.0, 85.0, 80],
2020-05-27T03:46:00.4647113Z ],
2020-05-27T03:46:00.4647372Z ),
2020-05-27T03:46:00.4647589Z (
2020-05-27T03:46:00.4647855Z "expanding",
2020-05-27T03:46:00.4648104Z None,
2020-05-27T03:46:00.4648376Z [
2020-05-27T03:46:00.4648680Z [10.0, 10.0, 20.0, 20.0],
2020-05-27T03:46:00.4648983Z [15.0, 20.0, 25.0, 20.0],
2020-05-27T03:46:00.4649326Z [20.0, 30.0, 30.0, 20.0],
2020-05-27T03:46:00.4649670Z [10.0, 10.0, 30.0, 30.0],
2020-05-27T03:46:00.4649972Z [20.0, 30.0, 35.0, 30.0],
2020-05-27T03:46:00.4650322Z [26.666667, 40.0, 50.0, 30.0],
2020-05-27T03:46:00.4650683Z [40.0, 80.0, 60.0, 30.0],
2020-05-27T03:46:00.4650946Z ],
2020-05-27T03:46:00.4651208Z ),
2020-05-27T03:46:00.4651414Z ],
2020-05-27T03:46:00.4651646Z )
2020-05-27T03:46:00.4651997Z def test_multiple_agg_funcs(func, window_size, expected_vals):
2020-05-27T03:46:00.4652328Z # GH 15072
2020-05-27T03:46:00.4652605Z df = pd.DataFrame(
2020-05-27T03:46:00.4652839Z [
2020-05-27T03:46:00.4653114Z ["A", 10, 20],
2020-05-27T03:46:00.4653414Z ["A", 20, 30],
2020-05-27T03:46:00.4653669Z ["A", 30, 40],
2020-05-27T03:46:00.4653966Z ["B", 10, 30],
2020-05-27T03:46:00.4654266Z ["B", 30, 40],
2020-05-27T03:46:00.4654520Z ["B", 40, 80],
2020-05-27T03:46:00.4654822Z ["B", 80, 90],
2020-05-27T03:46:00.4655056Z ],
2020-05-27T03:46:00.4655354Z columns=["stock", "low", "high"],
2020-05-27T03:46:00.4655642Z )
2020-05-27T03:46:00.4655833Z
2020-05-27T03:46:00.4656116Z f = getattr(df.groupby("stock"), func)
2020-05-27T03:46:00.4656545Z if window_size:
2020-05-27T03:46:00.4656817Z window = f(window_size)
2020-05-27T03:46:00.4657104Z else:
2020-05-27T03:46:00.4657337Z window = f()
2020-05-27T03:46:00.4657591Z
2020-05-27T03:46:00.4657872Z index = pd.MultiIndex.from_tuples(
2020-05-27T03:46:00.4658235Z [("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)],
2020-05-27T03:46:00.4658625Z names=["stock", None],
2020-05-27T03:46:00.4658902Z )
2020-05-27T03:46:00.4659155Z columns = pd.MultiIndex.from_tuples(
2020-05-27T03:46:00.4659548Z [("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")]
2020-05-27T03:46:00.4659860Z )
2020-05-27T03:46:00.4660195Z expected = pd.DataFrame(expected_vals, index=index, columns=columns)
2020-05-27T03:46:00.4660526Z
2020-05-27T03:46:00.4660750Z result = window.agg(
2020-05-27T03:46:00.4661122Z > OrderedDict((("low", ["mean", "max"]), ("high", ["mean", "min"])))
2020-05-27T03:46:00.4661464Z )
2020-05-27T03:46:00.4661630Z
2020-05-27T03:46:00.4661965Z test_venv\lib\site-packages\pandas\tests\window\test_api.py:346:
2020-05-27T03:46:00.4662381Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-05-27T03:46:00.4662906Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2033: in aggregate
2020-05-27T03:46:00.4663339Z return super().aggregate(func, *args, **kwargs)
2020-05-27T03:46:00.4663730Z test_venv\lib\site-packages\pandas\core\window\rolling.py:603: in aggregate
2020-05-27T03:46:00.4664169Z result, how = self._aggregate(func, *args, **kwargs)
2020-05-27T03:46:00.4664599Z test_venv\lib\site-packages\pandas\core\base.py:417: in _aggregate
2020-05-27T03:46:00.4664941Z result = _agg(arg, _agg_1dim)
2020-05-27T03:46:00.4665315Z test_venv\lib\site-packages\pandas\core\base.py:384: in _agg
2020-05-27T03:46:00.4665658Z result[fname] = func(fname, agg_how)
2020-05-27T03:46:00.4666056Z test_venv\lib\site-packages\pandas\core\base.py:368: in _agg_1dim
2020-05-27T03:46:00.4666433Z return colg.aggregate(how)
2020-05-27T03:46:00.4666793Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2033: in aggregate
2020-05-27T03:46:00.4667225Z return super().aggregate(func, *args, **kwargs)
2020-05-27T03:46:00.4667657Z test_venv\lib\site-packages\pandas\core\window\rolling.py:603: in aggregate
2020-05-27T03:46:00.4668054Z result, how = self._aggregate(func, *args, **kwargs)
2020-05-27T03:46:00.4668471Z test_venv\lib\site-packages\pandas\core\base.py:475: in _aggregate
2020-05-27T03:46:00.4668902Z return self._aggregate_multiple_funcs(arg, _axis=_axis), None
2020-05-27T03:46:00.4669286Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-05-27T03:46:00.4669590Z
2020-05-27T03:46:00.4669874Z self = RollingGroupby [window=2,center=False,axis=0], arg = ['mean', 'max']
2020-05-27T03:46:00.4670218Z _axis = 0
2020-05-27T03:46:00.4670378Z
2020-05-27T03:46:00.4670690Z def _aggregate_multiple_funcs(self, arg, _axis):
2020-05-27T03:46:00.4671087Z from pandas.core.reshape.concat import concat
2020-05-27T03:46:00.4671345Z
2020-05-27T03:46:00.4671605Z if _axis != 0:
2020-05-27T03:46:00.4671974Z raise NotImplementedError("axis other than 0 is not supported")
2020-05-27T03:46:00.4672267Z
2020-05-27T03:46:00.4672562Z if self._selected_obj.ndim == 1:
2020-05-27T03:46:00.4672862Z obj = self._selected_obj
2020-05-27T03:46:00.4673151Z else:
2020-05-27T03:46:00.4673452Z obj = self._obj_with_exclusions
2020-05-27T03:46:00.4673697Z
2020-05-27T03:46:00.4673941Z results = []
2020-05-27T03:46:00.4674209Z keys = []
2020-05-27T03:46:00.4674412Z
2020-05-27T03:46:00.4674664Z # degenerate case
2020-05-27T03:46:00.4674921Z if obj.ndim == 1:
2020-05-27T03:46:00.4675282Z for a in arg:
2020-05-27T03:46:00.4675640Z colg = self._gotitem(obj.name, ndim=1, subset=obj)
2020-05-27T03:46:00.4675954Z try:
2020-05-27T03:46:00.4676277Z new_res = colg.aggregate(a)
2020-05-27T03:46:00.4676528Z
2020-05-27T03:46:00.4676805Z except TypeError:
2020-05-27T03:46:00.4677111Z pass
2020-05-27T03:46:00.4677359Z else:
2020-05-27T03:46:00.4677673Z results.append(new_res)
2020-05-27T03:46:00.4677958Z
2020-05-27T03:46:00.4678210Z # make sure we find a good name
2020-05-27T03:46:00.4678584Z name = com.get_callable_name(a) or a
2020-05-27T03:46:00.4678891Z keys.append(name)
2020-05-27T03:46:00.4679164Z
2020-05-27T03:46:00.4679405Z # multiples
2020-05-27T03:46:00.4679630Z else:
2020-05-27T03:46:00.4679957Z for index, col in enumerate(obj):
2020-05-27T03:46:00.4680401Z colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index])
2020-05-27T03:46:00.4680756Z try:
2020-05-27T03:46:00.4681075Z new_res = colg.aggregate(arg)
2020-05-27T03:46:00.4681407Z except (TypeError, DataError):
2020-05-27T03:46:00.4681737Z pass
2020-05-27T03:46:00.4682119Z except ValueError as err:
2020-05-27T03:46:00.4682422Z # cannot aggregate
2020-05-27T03:46:00.4682818Z if "Must produce aggregated value" in str(err):
2020-05-27T03:46:00.4683257Z # raised directly in _aggregate_named
2020-05-27T03:46:00.4683562Z pass
2020-05-27T03:46:00.4683915Z elif "no results" in str(err):
2020-05-27T03:46:00.4684337Z # raised directly in _aggregate_multiple_funcs
2020-05-27T03:46:00.4684653Z pass
2020-05-27T03:46:00.4684954Z else:
2020-05-27T03:46:00.4685220Z raise
2020-05-27T03:46:00.4685512Z else:
2020-05-27T03:46:00.4685823Z results.append(new_res)
2020-05-27T03:46:00.4686109Z keys.append(col)
2020-05-27T03:46:00.4686378Z
2020-05-27T03:46:00.4686628Z # if we are empty
2020-05-27T03:46:00.4686893Z if not len(results):
2020-05-27T03:46:00.4687219Z > raise ValueError("no results")
2020-05-27T03:46:00.4687514Z E ValueError: no results
2020-05-27T03:46:00.4687767Z
2020-05-27T03:46:00.4688056Z test_venv\lib\site-packages\pandas\core\base.py:540: ValueError
2020-05-27T03:46:00.4900327Z ________________ test_rolling_apply_args_kwargs[args_kwargs0] _________________
2020-05-27T03:46:00.4910069Z [gw0] win32 -- Python 3.7.7 D:\a\1\s\test_venv\Scripts\python.exe
2020-05-27T03:46:00.4913683Z
2020-05-27T03:46:00.4916587Z args_kwargs = [None, {'par': 10}]
2020-05-27T03:46:00.4920984Z
2020-05-27T03:46:00.4932595Z @pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]])
2020-05-27T03:46:00.4934841Z def test_rolling_apply_args_kwargs(args_kwargs):
2020-05-27T03:46:00.4935692Z # GH 33433
2020-05-27T03:46:00.4936381Z def foo(x, par):
2020-05-27T03:46:00.4937029Z return np.sum(x + par)
2020-05-27T03:46:00.4937700Z
2020-05-27T03:46:00.4938401Z df = DataFrame({"gr": [1, 1], "a": [1, 2]})
2020-05-27T03:46:00.4939052Z
2020-05-27T03:46:00.4939885Z idx = Index(["gr", "a"])
2020-05-27T03:46:00.4940561Z expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx)
2020-05-27T03:46:00.4941155Z
2020-05-27T03:46:00.4941805Z result = df.rolling(1).apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
2020-05-27T03:46:00.4942523Z tm.assert_frame_equal(result, expected)
2020-05-27T03:46:00.4943122Z
2020-05-27T03:46:00.4943684Z result = df.rolling(1).apply(foo, args=(10,))
2020-05-27T03:46:00.4945113Z
2020-05-27T03:46:00.4945760Z midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None])
2020-05-27T03:46:00.4946452Z expected = Series([11.0, 12.0], index=midx, name="a")
2020-05-27T03:46:00.4947078Z
2020-05-27T03:46:00.4947685Z gb_rolling = df.groupby("gr")["a"].rolling(1)
2020-05-27T03:46:00.4948255Z
2020-05-27T03:46:00.4949192Z > result = gb_rolling.apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
2020-05-27T03:46:00.4949774Z
2020-05-27T03:46:00.4950430Z test_venv\lib\site-packages\pandas\tests\window\test_apply.py:165:
2020-05-27T03:46:00.4951199Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-05-27T03:46:00.4951938Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2067: in apply
2020-05-27T03:46:00.4952625Z kwargs=kwargs,
2020-05-27T03:46:00.4953353Z test_venv\lib\site-packages\pandas\core\window\rolling.py:1391: in apply
2020-05-27T03:46:00.4954036Z kwargs=kwargs,
2020-05-27T03:46:00.4954689Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2196: in _apply
2020-05-27T03:46:00.4955365Z **kwargs,
2020-05-27T03:46:00.4956044Z test_venv\lib\site-packages\pandas\core\window\rolling.py:589: in _apply
2020-05-27T03:46:00.4956693Z result = calc(values)
2020-05-27T03:46:00.4957386Z test_venv\lib\site-packages\pandas\core\window\rolling.py:575: in calc
2020-05-27T03:46:00.4958198Z closed=self.closed,
2020-05-27T03:46:00.4958846Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-05-27T03:46:00.4959456Z
2020-05-27T03:46:00.4960053Z self = <pandas.core.window.indexers.GroupbyRollingIndexer object at 0x3A34FA30>
2020-05-27T03:46:00.4960788Z num_values = 2, min_periods = None, center = False, closed = None
2020-05-27T03:46:00.4961381Z
2020-05-27T03:46:00.4961909Z @Appender(get_window_bounds_doc)
2020-05-27T03:46:00.4962523Z def get_window_bounds(
2020-05-27T03:46:00.4963100Z self,
2020-05-27T03:46:00.4963652Z num_values: int = 0,
2020-05-27T03:46:00.4964290Z min_periods: Optional[int] = None,
2020-05-27T03:46:00.4964946Z center: Optional[bool] = None,
2020-05-27T03:46:00.4965552Z closed: Optional[str] = None,
2020-05-27T03:46:00.4966208Z ) -> Tuple[np.ndarray, np.ndarray]:
2020-05-27T03:46:00.4966908Z # 1) For each group, get the indices that belong to the group
2020-05-27T03:46:00.4967605Z # 2) Use the indices to calculate the start & end bounds of the window
2020-05-27T03:46:00.4968322Z # 3) Append the window bounds in group order
2020-05-27T03:46:00.4968991Z start_arrays = []
2020-05-27T03:46:00.4969583Z end_arrays = []
2020-05-27T03:46:00.4970143Z window_indicies_start = 0
2020-05-27T03:46:00.4970823Z for key, indicies in self.groupby_indicies.items():
2020-05-27T03:46:00.4971539Z if self.index_array is not None:
2020-05-27T03:46:00.4972196Z index_array = self.index_array.take(indicies)
2020-05-27T03:46:00.4972843Z else:
2020-05-27T03:46:00.4973467Z index_array = self.index_array
2020-05-27T03:46:00.4974075Z indexer = self.rolling_indexer(
2020-05-27T03:46:00.4974759Z index_array=index_array, window_size=self.window_size,
2020-05-27T03:46:00.4975407Z )
2020-05-27T03:46:00.4975981Z start, end = indexer.get_window_bounds(
2020-05-27T03:46:00.4976661Z len(indicies), min_periods, center, closed
2020-05-27T03:46:00.4977281Z )
2020-05-27T03:46:00.4977908Z # Cannot use groupby_indicies as they might not be monotonic with the object
2020-05-27T03:46:00.4978599Z # we're rolling over
2020-05-27T03:46:00.4979224Z window_indicies = np.arange(
2020-05-27T03:46:00.4979816Z window_indicies_start,
2020-05-27T03:46:00.4980472Z window_indicies_start + len(indicies),
2020-05-27T03:46:00.4981202Z dtype=np.int64,
2020-05-27T03:46:00.4981747Z )
2020-05-27T03:46:00.4982364Z window_indicies_start += len(indicies)
2020-05-27T03:46:00.4983051Z # Extend as we'll be slicing window like [start, end)
2020-05-27T03:46:00.4983789Z window_indicies = np.append(window_indicies, [window_indicies[-1] + 1])
2020-05-27T03:46:00.4984532Z > start_arrays.append(window_indicies.take(start))
2020-05-27T03:46:00.4985326Z E TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
2020-05-27T03:46:00.4986013Z
```
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MDU6SXNzdWU2MjU4Njg5NjE=
| 34,411
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BUG: pd.read_sql returns empty list if query has no results and chunksize is set
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| 7
| 2020-05-27T17:02:36Z
| 2021-01-14T18:52:47Z
| 2021-01-14T18:52:47Z
|
CONTRIBUTOR
| null |
- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [x ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import sqlite3
# Create empty test table in memory
conn = sqlite3.connect(':memory:')
conn.cursor().execute('CREATE TABLE test (column_1 INTEGER);')
# Run the query without chunksize, works as expected
pd.read_sql('select * from test', conn)
# Run the query with chunksize, returns generator as expected
pd.read_sql('select * from test', conn, chunksize=5)
# However, the generator is empty
list(pd.read_sql('select * from test', conn, chunksize=5))
# I would expect, that for all cases where chunksize isn't necessary,
# than the following two lines would return exactly the same
# result, but the second throws "ValueError: No objects to concatenate"
pd.read_sql('select * from test', conn)
pd.concat(pd.read_sql('select * from test', conn, chunksize=5))
```
#### Problem description
In many cases, returning zero rows is an expected result, and the code should run fine on the returned DataFrame (iterating over it, getting all values in a row, etc.).
The current behaviour instead returns an empty list, with no information about for example the columns in the dataframe.
#### Expected Output
The expected output would be a list containing a single empty dataframe, with the correct column metadata. I would expect that for all queries that run fine without chunkbite being set, the following equality should hold:
```
pd.testing.assert_frame_equal(
pd.read_sql(query, conn),
pd.concat(pd.read_sql(query, conn, chunksize=5))
)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 333db4b765f8e88c0c2392943cb7d6c6013dc6e8
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jan 23 06:52:12 PST 2020; root:xnu-4903.278.25~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+1685.g333db4b76.dirty
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0.post20200518
Cython : 0.29.19
pytest : 5.4.2
hypothesis : 5.16.0
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDU6SXNzdWU2MjU4OTMyMzQ=
| 34,412
|
DOC:/USAGE: Resulting Index when merging over indices
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[] | null | 1
| 2020-05-27T17:38:44Z
| 2020-09-04T13:30:36Z
| null |
MEMBER
| null |
#### Location of the documentation
https://dev.pandas.io/docs/reference/api/pandas.DataFrame.merge.html
#### Documentation problem
There is no documentation on this case
#### Suggested fix for documentation
I'm a bit confused about the behavior here, because I don't know what to expect. When I perform the merge as shown below, I would expect that both operations return the same output.
```
left = pd.DataFrame({'a': [1, 2], 'b': [1, 1], "l": [22, 23]}).set_index(['a', 'b'])
right = pd.DataFrame({'b': [1], "r": [12]}).set_index(['b'])
print(pd.merge(left, right, left_on=['b'], right_index=True, how="left"))
print(pd.merge(left, right, left_on=['b'], right_on=["b"], how="left"))
```
But the Index of both results differs. The first merge has the same index as the left `DataFrame` while the second merge has only `b` as index. As a result, the index from the second merge is no longer unique.
**First Merge:**
```
l r
a b
1 1 22 12
2 1 23 12
```
**Second Merge:**
```
l r
b
1 22 12
1 23 12
```
**If** this is the desired behavior, we should adjust the documentation to show this. If it is not, I would file an BUG issue. We should adjust the documentation nevertheless. I would add an example with an index join to show the right behavior.
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TST: slow test_nearest_upsample_with_limit with tzlocal
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| 3
| 2020-05-27T17:40:13Z
| 2020-06-08T02:01:41Z
| 2020-06-08T02:01:41Z
|
MEMBER
| null |
Doing `pytest pandas/tests --skip-slow --durations=10` the top two entries are
```
71.76s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzlocal()-30S-Y]
59.86s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzlocal()-30S-10M]
```
Because this uses the tz_aware_fixture, there isn't a trivial way to add a pytest.mark.slow to this, but figuring something out may be worthwhile.
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| 625,934,316
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MDExOlB1bGxSZXF1ZXN0NDI0MDQxOTQy
| 34,414
|
BUG: merge between partial index and index fails when result is empty
|
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| 4
| 2020-05-27T18:35:11Z
| 2021-01-03T23:23:12Z
| 2021-01-03T23:21:42Z
|
MEMBER
| null |
- [x] closes #33814
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The Issue was caused when setting the name of an numpy array.
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| 34,415
|
BUG: String series cat produces NaNs when left operand has a custom index
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[] | null | 2
| 2020-05-27T19:06:38Z
| 2020-08-21T17:05:43Z
| 2020-05-27T19:24:50Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.__version__)
x = pd.Series(['a','b','c','d','e'])
x.index = ['1', '2', '3', '4', '5']
pd_others = pd.Series(['f','g','h','i','j'])
print(x.str.cat(others=pd_others))
1.1.0.dev0+1690.g70d7c04ff
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
dtype: object
```
#### Problem description
Possibly related to https://github.com/pandas-dev/pandas/issues/33425. The trigger for this bug seems to be the custom index. I get the correct answer if the line where `x.index` is set is removed.
#### Expected Output
```
1.1.0.dev0+1690.g70d7c04ff
0 af
1 bg
2 ch
3 di
4 ej
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 70d7c04ff585de361622e4fe1788480a7a4526b5
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1690.g70d7c04ff
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI0MDg0Nzgz
| 34,416
|
Macpython 32 bit build fixup
|
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| 3
| 2020-05-27T20:00:26Z
| 2020-05-29T19:05:45Z
| 2020-05-29T19:05:41Z
|
CONTRIBUTOR
| null |
Closes #34114
cc @jbrockmendel and @WillAyd. I have no idea if this is correct, but it builds.
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