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} | 2 | 2020-09-04T14:30:02Z | 2020-09-04T15:59:48Z | 2020-09-04T15:17:10Z | MEMBER | null | Closes #35658
cc @jbrockmendel
(I suppose the long term way would rather to properly pickle the IntervalArray itself and restore from that, instead of restoring from the left/right arrays, but would rather leave such a change for 1.2) | {
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} | 1 | 2020-09-04T16:17:42Z | 2020-09-04T17:53:49Z | 2020-09-04T17:34:50Z | MEMBER | null | @simonjayhawkins are there any typing-related areas that you'd suggest I prioritize? | {
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} | 1 | 2020-09-04T16:42:54Z | 2020-09-05T19:04:08Z | 2020-09-05T03:18:13Z | MEMBER | null | - [x] closes #29764
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry´
Issue was fixed for sum and any with c9144ca54dcc924995acae3d9dcb890a5802d7c0.
So the only thing left was to add the index name in `_wrap_transformed_output`. I kept the tests from #33111, to show, that alle cases are now handled correctly.
I hope it is okay, to add a new PR for this? | {
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} | 2 | 2020-09-04T16:45:44Z | 2020-09-14T11:29:05Z | 2020-09-13T22:57:45Z | NONE | null | An OpenDocument spreadsheet with a single cell containing a superscript causes a `UnboundLocalError: local variable 'spaces' referenced before assignment` crash in Pandas.
I'm attaching the .ods file.
This used to work fine (one month ago on my machine). I tried upgrading to Pandas 1.1.1 and get the same bug. It looks like what's below:
```
In [2]: import pandas as pd
In [3]: pd.read_excel('/home/meuser/tmp/pandas-bug.ods', sheet_name='Sheet1', engine='odf')
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-3-0fec79f4321d> in <module>
----> 1 pd.read_excel('/home/meuser/tmp/pandas-bug.ods', sheet_name='Sheet1', engine='odf')
~/.local/lib/python3.8/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
294 )
295 warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
--> 296 return func(*args, **kwargs)
297
298 return wrapper
~/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols)
309 )
310
--> 311 return io.parse(
312 sheet_name=sheet_name,
313 header=header,
~/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, converters, true_values, false_values, skiprows, nrows, na_values, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
904 DataFrame from the passed in Excel file.
905 """
--> 906 return self._reader.parse(
907 sheet_name=sheet_name,
908 header=header,
~/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, dtype, true_values, false_values, skiprows, nrows, na_values, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
441 sheet = self.get_sheet_by_index(asheetname)
442
--> 443 data = self.get_sheet_data(sheet, convert_float)
444 usecols = _maybe_convert_usecols(usecols)
445
~/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py in get_sheet_data(self, sheet, convert_float)
89 for j, sheet_cell in enumerate(sheet_cells):
90 if sheet_cell.qname == table_cell_name:
---> 91 value = self._get_cell_value(sheet_cell, convert_float)
92 else:
93 value = self.empty_value
~/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py in _get_cell_value(self, cell, convert_float)
173 return float(cell_value)
174 elif cell_type == "string":
--> 175 return self._get_cell_string_value(cell)
176 elif cell_type == "currency":
177 cell_value = cell.attributes.get((OFFICENS, "value"))
~/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py in _get_cell_string_value(self, cell)
209 if fragment.qname == text_s:
210 spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
--> 211 value.append(" " * spaces)
212 return "".join(value)
UnboundLocalError: local variable 'spaces' referenced before assignment
> /home/meuser/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py(211)_get_cell_string_value()
208 elif isinstance(fragment, Element):
209 if fragment.qname == text_s:
210 spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
--> 211 value.append(" " * spaces)
212 return "".join(value)
```
[pandas-bug.zip](https://github.com/pandas-dev/pandas/files/5176030/pandas-bug.zip)
Versions:
In [2]: pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-45-generic
Version : #49-Ubuntu SMP Wed Aug 26 13:38:52 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.1.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.21
pytest : 4.6.9
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
numba : None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/36123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36123/events | https://github.com/pandas-dev/pandas/issues/36123 | 693,463,817 | MDU6SXNzdWU2OTM0NjM4MTc= | 36,123 | BUG: GroupBy.mean() is extremely slow with sparse arrays | {
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"description": "Memory or execu... | open | false | null | [] | null | 7 | 2020-09-04T18:18:44Z | 2021-08-13T02:18:25Z | null | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
np.random.seed(0)
df = pd.DataFrame(np.random.rand(1000, 1000) > 0.8, dtype=int)
df['g'] = ['A' if x else 'B' for x in np.random.rand(1000) > 0.5]
%timeit -n 3 -r 5 df.groupby('g').mean()
sdf = df.set_index('g').astype('Sparse[int]').reset_index()
%timeit -n 3 -r 5 sdf.groupby('g').mean()
```
Output:
Dense:
`11 ms ± 430 µs per loop (mean ± std. dev. of 5 runs, 3 loops each)`
Sparse:
`3.91 s ± 33.5 ms per loop (mean ± std. dev. of 5 runs, 3 loops each)`
or subset of a real-word dataset:
```python
import pandas as pd
df = pd.read_csv('https://github.com/pandas-dev/pandas/files/5176354/pbmc-sparse-df.csv.gz',
index_col=0).astype(int).reset_index()
%timeit -n 3 -r 5 df.groupby('Cell type').mean()
sdf = df.set_index('Cell type').astype(pd.SparseDtype(int, fill_value=0)).reset_index()
%timeit -n 3 -r 5 sdf.groupby('Cell type').mean()
```
Output:
Dense:
`9.2 ms ± 403 µs per loop (mean ± std. dev. of 5 runs, 3 loops each)`
Sparse:
`3.73 s ± 126 ms per loop (mean ± std. dev. of 5 runs, 3 loops each)`
#### Problem description
`GroupBy.mean()` with the SparseArray seems extremely slow compared to dense matrices (here around 355 times).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200802
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
I attached the file here for reproducibility.
[pbmc-sparse-df.csv.gz](https://github.com/pandas-dev/pandas/files/5176354/pbmc-sparse-df.csv.gz)
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... | null | 15 | 2020-09-04T19:16:21Z | 2020-10-02T23:30:22Z | null | NONE | null | #### Is your feature request related to a problem?
When displaying dataframes that contain IntEnums in the index, columns, or data, it would be nice if they displayed similar to Enum types by using the Enum names that correspond to the value rather then the integer values, which somewhat defeats the prupose of using IntEnums over just ints with alias names.
#### Describe the solution you'd like
I would like pandas to display IntEnums in the same way as Enums.
#### API breaking implications
Non that I am aware of, as this request is only for a display change.
#### Describe alternatives you've considered
I have considered two solution which both have drawbacks. First was to monkey patch Int Array Formatter. This ended up not working as Pandas appears to case IntEnums to regulat ints as soon as they are put into the frame. The second was to change the IntEnums to be categorical, which makes logical sense, but to get them to display properly I need to map the data to the names of the enums which changes the underlying data to strings.
#### Additional context
Here is a simple example.
```python
from enum import Enum, IntEnum
import pandas as pd
class Colors(Enum):
red = 1
blue = 2
class IntColors(IntEnum):
red = 1
blue = 2
df = pd.DataFrame({Colors.red:[1,2,3], Colors.blue:[5,6,6]})
int_df = pd.DataFrame({IntColors.red:[1,2,3], IntColors.blue:[5,6,6]})
print('This is how I want the dataframe to display')
print(df)
print()
print('This is how it is displayed')
print(int_df)
```
Which outputs:
```
This is how I want the dataframe to display
Colors.red Colors.blue
0 1 5
1 2 6
2 3 6
This is how it is displayed
1 2
0 1 5
1 2 6
2 3 6
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} | 2 | 2020-09-04T19:21:33Z | 2020-09-05T10:50:58Z | 2020-09-05T10:50:58Z | MEMBER | null | - [x] closes #35162
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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"de... | open | false | null | [] | null | 2 | 2020-09-04T20:22:25Z | 2021-08-13T02:26:37Z | null | CONTRIBUTOR | null | Currently ExtensionDtype.consruct_array_type is a classmethod. This makes it hard to use a single dtype with multiple array classes where the array type depends on some parameter of the dtype.
I'd like to change construct_array_type to be a regular method rather than a classmethod. AFAICT, this is fine for pandas since everywhere we use construct_array_type we have an instance rather than a type. It should be fine for 3rd party arrays to continue to use classmethods since you can call a classmethod on an instance. The API breaking change would be for 3rd-party code relying on `ExtensionDtype.construct_array_type()` working (i.e. calling it on the class).
My motivation is for allowing a parametrized `StringDtype` that can be used for either the current StringArray and an Arrow-backed StringArray. | {
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ATM we have one reasonable behavior `Timestamp("2020-09-04") == date(2020, 9, 4)` and two un-reasonable behaviors: ``Timestamp("2020-09-04").tz_localize("US/Pacific") == date(2020, 9, 4)`, `Timestamp.now() == Timestamp.now().date()`. Since the stdlib datetime doesnt consider `datetime(2020, 9, 4) == date(2020, 9, 4)`, this follows the stdlib and considers them never equal.
<s>I'm still getting one test failure locally.</s>
cc @mroeschke | {
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Creating PR to solve [Issue 36040](https://github.com/pandas-dev/pandas/issues/36040).
The old `FixedWindowIndexer.get_window_bounds` function provided unintuitive bounds that went beyond the length of the original array. Additionally, to do a centered rolling operation, it required NaN values to be appended to the end of original array to enable some roundabout way of achieving the centering.
I replaced it with one that seems much simpler and actually creates "fixed" size windows (at least prior to clipping the ends), which the previous function did not.
That being said, I know this PR fails some tests, I'm would appreciate some advice on how best to proceed! | {
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cc @jorisvandenbossche this feels klunky, and the axis==1 part im pretty unsure of. how would you approach this? | {
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} | 12 | 2020-09-05T04:19:41Z | 2020-09-07T19:30:44Z | 2020-09-07T11:16:12Z | CONTRIBUTOR | null | …lled. fixes #35948
- [x] closes #35948
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 10 | 2020-09-05T11:10:53Z | 2020-10-19T19:04:26Z | 2020-10-19T14:01:43Z | CONTRIBUTOR | null | This allows a single dtype to support multiple array classes.
For arrow-backed strings, we'll likely want a separate array class
for ease of implementation, clarity. But we'll have a parametrized
dtype.
```python
class StringDtype:
def __init__(self, storage="python"):
self.storage = storage
def construct_array_type(self): # regular method
if self.storage == "python":
return StringArray
else:
return ArrowStringArray
```
Closes #36126 | {
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} | 2 | 2020-09-05T13:52:12Z | 2020-09-13T15:28:37Z | 2020-09-05T14:44:27Z | CONTRIBUTOR | null | - [x ] closes #35406
- [x ] tests added / passed
- [ x] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Not sure how to do whatsnew entry for this - first contribution! Do let me know how to improve.
Output of scripts/validate_docstring.py:
```
################################################################################
########################## Docstring (pandas.Series) ##########################
################################################################################
One-dimensional ndarray with axis labels (including time series).
Labels need not be unique but must be a hashable type. The object
supports both integer- and label-based indexing and provides a host of
methods for performing operations involving the index. Statistical
methods from ndarray have been overridden to automatically exclude
missing data (currently represented as NaN).
Operations between Series (+, -, /, *, **) align values based on their
associated index values-- they need not be the same length. The result
index will be the sorted union of the two indexes.
Parameters
----------
data : array-like, Iterable, dict, or scalar value
Contains data stored in Series.
.. versionchanged:: 0.23.0
If data is a dict, argument order is maintained for Python 3.6
and later.
index : array-like or Index (1d)
Values must be hashable and have the same length as `data`.
Non-unique index values are allowed. Will default to
RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
and index is None, then the values in the index are used to
reindex the Series after it is created using the keys in the data.
dtype : str, numpy.dtype, or ExtensionDtype, optional
Data type for the output Series. If not specified, this will be
inferred from `data`.
See the :ref:`user guide <basics.dtypes>` for more usages.
name : str, optional
The name to give to the Series.
copy : bool, default False
Copy input data.
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Parameters {'fastpath'} not documented
See Also section not found
No examples section found
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} | 9 | 2020-09-05T14:58:57Z | 2020-09-18T11:34:07Z | 2020-09-08T13:45:09Z | CONTRIBUTOR | null | - [x] closes #31368
- [x] tests added / passed
`indexing/test_partial.py:TestPartialSetting:test_index_name_empty`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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I wouldn't recommend reviewing this yet. In particular, I need to refactor the string ops implementation. That will likely be done in a separate PR to keep things smaller here.
```python
In [1]: import pandas as pd
In [2]: a = pd.Series(pd.array([pd.NA, pd.NA], dtype=pd.StringDtype("python")))
In [3]: b = pd.Series(pd.array([pd.NA, pd.NA], dtype=pd.StringDtype("pyarrow")))
In [4]: a.array
Out[4]:
<StringArray>
[<NA>, <NA>]
Length: 2, dtype: StringDtype[python]
In [5]: b.array
Out[5]:
<ArrowStringArray>
[<NA>, <NA>]
Length: 2, dtype: StringDtype[pyarrow]
```
Closes https://github.com/pandas-dev/pandas/issues/35169 | {
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Setup for #35964. There were two tests that are accidental duplicates; I've just copied them over to the transform modules. Will cleanup in #35964. | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @mroeschke | {
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} | 14 | 2020-09-05T20:59:20Z | 2020-09-18T11:34:47Z | 2020-09-08T15:30:37Z | CONTRIBUTOR | null | - [x] closes #31364
- [x] tests added / passed
- tools/test_to_numeric.py:test_precision
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This relates to a very old issue #8002 where the default precision for CSV files could create wrong answers in the last bit. @jreback was involved in the PR review for that, which created the default to not be the high precision parser.
For `to_numeric()`, I switched it to use `precise_xstrtod` instead of `xstrtod` by default.
Unknown whether there are performance implications for `to_numeric()`, although this comment https://github.com/pandas-dev/pandas/issues/17154#issuecomment-319917647 from @jorisvandenbossche indicates that maybe we should consider switching to the higher precision parser by default in `read_csv()` anyway.
Open question as to whether performance analysis of *this* PR is needed beyond what is shown below, which seems to indicate that using `precise_xstrtod` is *faster* than `xstrtod`
The other possibility is to use a keyword argument in `to_numeric` that would allow the higher precision parser. | {
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} | 3 | 2020-09-05T22:56:02Z | 2020-09-12T21:30:15Z | 2020-09-12T21:28:01Z | MEMBER | null | This doesn't get rid of internals usage entirely, but makes that access 1 layer less deep | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.astype.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html
xref https://github.com/pandas-dev/pandas/pull/35979 | {
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} | 2 | 2020-09-05T23:16:51Z | 2020-09-07T21:14:34Z | 2020-09-07T21:06:30Z | MEMBER | null | - [x] closes #36018
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The number of leves for MultiIndex in input was compressed to one, because the tuples representing the Index rows were not unpacked. Also name was used instead of names -> resulted in missing name for every MultiIndex. | {
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"description": "String extension data ty... | open | false | null | [] | null | 3 | 2020-09-05T23:47:32Z | 2021-08-13T02:30:57Z | null | MEMBER | null | ```
ser = pd.Series([pd.NaT, pd.Timestamp.now()])
>>> ser.astype("string")
0 NaT
1 2020-09-05 16:44:52.259773
dtype: string
>>> pd.Series(ser._values.astype("string"))
0 <NA>
1 2020-09-05 16:44:52.259773
dtype: string
```
It would be nice to have the Series code (i.e. the Block.astype) dispatch to the array code.
Note also:
```
vals = ser._values.astype("string")
>>> vals.astype(ser.dtype)
ValueError: Could not convert object to NumPy datetime
>>> pd.Series(vals, dtype=ser.dtype)
ValueError: Could not convert object to NumPy datetime
>>> pd.to_datetime(vals) # <-- works
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- [ ] xref #32550
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 11 | 2020-09-06T05:49:12Z | 2020-10-31T19:11:34Z | 2020-10-31T19:11:34Z | 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.
**UPD**: bug does not exist anymore in latest master branch, yet still present in 1.1.3 from pip.
Updated version details:
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.0
pip : 20.2.3
setuptools : 40.8.0
Cython : 0.29.21
pytest : 6.0.1
hypothesis : 5.36.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
**Edit**: removed everything related to categorical rhetoric, updated with findings. Corrected several mistypes.
---
#### Code Sample, a copy-pastable example
**UPD**: A clearer sample [provided](https://github.com/pandas-dev/pandas/issues/36158#issuecomment-697977693) by @AlexanderCecile which addresses that bug appears on group names inside lambda, not due to tuples in cell values.
```python
import random
import pandas as pd
df = pd.DataFrame({"col_1": [random.randint(0, 5) for _ in range(5)]})
print(df, end="\n\n")
grp_tuples = [("a", "b"), ("c", "d"), ("e", "f")]
# grp_res = df.groupby(by="col_1")
grp_res = df.groupby(by=lambda x: grp_tuples[x % len(grp_tuples)])
for key, grp in grp_res:
print(f"key: {key}\ngroup:\n{grp}\n")
print(grp_res.indices)
print(grp_res.grouper.indices)
```
##### Original sample
```python
df = pd.DataFrame({'Tuples': ((x, y)
for x in [0, 1]
for y in np.random.randint(3, 5, 5))})
df.groupby('Tuples').grouper.indices
df.groupby(lambda x: df.iloc[x,0]).indices
```
||0|1|2|3|4|5|6|7|8|9|
|-|-|-|-|-|-|-|-|-|-|-|
|'Tuples'|(0,3)|(0,3)|(0,3)|(0,3)|(0,3)|(1,4)|(1,4)|(1,4)|(1,3)|(1,4)|
---
### Problem description
**Problem**: .indices do not work on lambda made group by with tuples
In the sample above two different **.groupby** in crude terms do the same thing and provide groups with same characteristics - grouping DataFrame by column values which are represented by tuples. Technically single column **DF** is a **Series** object, but issue remains the same if some other columns are added. However these groups share some weird behavior:
- Both have same output for **.groups**:
> {(0, 3): [0, 1, 2, 3, 4], (1, 3): [8], (1, 4): [5, 6, 7, 9]}
- Both show same .**head**(), .**size**() some other methods [listed here](https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html). Groups can be selected with tuple key `(0, 3)` with all same output.
And here come the differences:
```diff
gb.transform('size')
+ just shows the index of a DF
- IndexError: Column(s) Tuples already selected
gb1.sum() / gb1.agg(sum)
+ shows column 'Tuples' with recurring tuples, e.g. (0, 3), (1, 3)
+ in case GroupBy is prepared with as_index=False, also shows index
- show index and 'Tuples' column where tuples are added per group
- on each line: (0, 3, 0, 3, 0, 3), (1, 3, 1, 3)
gb.all()
+ index with tuples per line, e.g. (0, 3), (1, 3)
+ if as_index=False:
+ ValueError: Length mismatch: Expected axis has 0 elements, new values have 3 elements
- table with tuples (indexes if as_index=False) and True values
gb.count()
+ Tuples column and no counts
+ if as_index=False:
+ IndexError: list index out of range
- table with tuples (indexes if as_index=False) and actual count of groups
```
Basically all aggregated methods have same result as above. However if **GroupBy** is made on Series `df['Tuples'].groupby(df['Tuples'])` majority of aggregated functions start working as for lambda made **GroupBy**, output is the same, but does not show as table in jupyter, just plain-text, yet I guess that's an unexpected behavior. That's __sapid__, but whatever, these are the differences one can live with.
## Main issue
**Tuple** group names in **lambda** or named function ***GroupBy*** break method **.get_group()** and attribute **.indices**.
As mentioned in [docs](https://pandas.pydata.org/docs/user_guide/groupby.html#piping-function-calls) **GroupBy** works pretty well with two-dimensional keys, although missing handful level selectors that MultiIndex has. Perhaps that's a topic for __feature-request__, not an issue.
Sample in [docs](https://pandas.pydata.org/docs/user_guide/groupby.html#piping-function-calls) produces same tuple groups `dict_keys( [ ('Store_1', 'Product_1'), ... () ]`, this should not be a problem. Also **GroupBy** made with lambda works all fine with numeric / string labels.
In case with tuples, **.indices** and **.get_group** fall with error `NotImplementedError: isna is not defined for MultiIndex`.
</br>
**.get_group()**
<details><summary>.get_group() Traceback</summary><blockquote>
<details><summary>ERROR</summary><blockquote>
```python
df.groupby(lambda x: df.iloc[x,0]).get_group((0,3))
```
```diff
- ---------------------------------------------------------------------------
- NotImplementedError Traceback (most recent call last)
- <ipython-input-3-c37a9ee93781> in <module>
- ----> 1 df.groupby(lambda x: df.iloc[x,0]).get_group((0,3))
```
</details><details><summary>Part 1 => .get_indices</summary><blockquote>
##### c:\python37\lib\site-packages\pandas\core\groupby\groupby.py in get_group(self, name, obj)
806 obj = self._selected_obj
807
--> 808 inds = self._get_index(name)
809 if not len(inds):
810 raise KeyError(name)
##### c:\python37\lib\site-packages\pandas\core\groupby\groupby.py in _get_index(self, name)
628 Safe get index, translate keys for datelike to underlying repr.
629 """
--> 630 return self._get_indices([name])[0]
631
632 @cache_readonly
##### c:\python37\lib\site-packages\pandas\core\groupby\groupby.py in _get_indices(self, names)
593 return []
594
--> 595 if len(self.indices) > 0:
596 index_sample = next(iter(self.indices))
597 else:
##### c:\python37\lib\site-packages\pandas\core\groupby\groupby.py in indices(self)
572 """
573 self._assure_grouper()
--> 574 return self.grouper.indices
575
576 def _get_indices(self, names):
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
</details><details><summary>Part 2 => Categories nan</summary><blockquote>
##### c:\python37\lib\site-packages\pandas\core\groupby\ops.py in indices(self)
222 """ dict {group name -> group indices} """
223 if len(self.groupings) == 1:
--> 224 return self.groupings[0].indices
225 else:
226 codes_list = [ping.codes for ping in self.groupings]
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\groupby\grouper.py in indices(self)
558 return self.grouper.indices
559
--> 560 values = Categorical(self.grouper)
561 return values._reverse_indexer()
562
##### c:\python37\lib\site-packages\pandas\core\arrays\categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
360
361 # we're inferring from values
--> 362 dtype = CategoricalDtype(categories, dtype.ordered)
363
364 elif is_categorical_dtype(values.dtype):
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in __init__(self, categories, ordered)
161
162 def __init__(self, categories=None, ordered: Ordered = False):
--> 163 self._finalize(categories, ordered, fastpath=False)
164
165 @classmethod
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in _finalize(self, categories, ordered, fastpath)
315
316 if categories is not None:
--> 317 categories = self.validate_categories(categories, fastpath=fastpath)
318
319 self._categories = categories
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in validate_categories(categories, fastpath)
487 if not fastpath:
488
--> 489 if categories.hasnans:
490 raise ValueError("Categorical categories cannot be null")
491
</details><details><summary>Part 3 => nan, Multiindex</summary><blockquote>
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\indexes\base.py in hasnans(self)
2046 """
2047 if self._can_hold_na:
-> 2048 return bool(self._isnan.any())
2049 else:
2050 return False
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\indexes\base.py in _isnan(self)
2026 """
2027 if self._can_hold_na:
-> 2028 return isna(self)
2029 else:
2030 # shouldn't reach to this condition by checking hasnans beforehand
##### c:\python37\lib\site-packages\pandas\core\dtypes\missing.py in isna(obj)
122 Name: 1, dtype: bool
123 """
--> 124 return _isna(obj)
125
126
##### c:\python37\lib\site-packages\pandas\core\dtypes\missing.py in _isna(obj, inf_as_na)
151 # hack (for now) because MI registers as ndarray
152 elif isinstance(obj, ABCMultiIndex):
--> 153 raise NotImplementedError("isna is not defined for MultiIndex")
154 elif isinstance(obj, type):
155 return False
### NotImplementedError: isna is not defined for MultiIndex
**>>> End of Traceback <<<**
</details></details>
**.indices**
<details>
<summary>.indices Traceback</summary><blockquote>
<details><summary>ERROR</summary><blockquote>
```python
df.groupby(lambda x: df.iloc[x,0]).indices
```
```diff
- ---------------------------------------------------------------------------
- NotImplementedError Traceback (most recent call last)
- <ipython-input-5-fd5a9f33be4e> in <module>
- ----> 1 df.groupby(lambda x: df.iloc[x,0]).indices
```
</details><details><summary>Part 1 => groupings, Categorical</summary><blockquote>
##### c:\python37\lib\site-packages\pandas\core\groupby\groupby.py in indices(self)
572 """
573 self._assure_grouper()
--> 574 return self.grouper.indices
575
576 def _get_indices(self, names):
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\groupby\ops.py in indices(self)
222 """ dict {group name -> group indices} """
223 if len(self.groupings) == 1:
--> 224 return self.groupings[0].indices
225 else:
226 codes_list = [ping.codes for ping in self.groupings]
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\groupby\grouper.py in indices(self)
558 return self.grouper.indices
559
--> 560 values = Categorical(self.grouper)
561 return values._reverse_indexer()
562
##### c:\python37\lib\site-packages\pandas\core\arrays\categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
360
361 # we're inferring from values
--> 362 dtype = CategoricalDtype(categories, dtype.ordered)
363
364 elif is_categorical_dtype(values.dtype):
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in __init__(self, categories, ordered)
161
162 def __init__(self, categories=None, ordered: Ordered = False):
--> 163 self._finalize(categories, ordered, fastpath=False)
164
165 @classmethod
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in _finalize(self, categories, ordered, fastpath)
315
316 if categories is not None:
--> 317 categories = self.validate_categories(categories, fastpath=fastpath)
318
319 self._categories = categories
</details><details><summary>Part 2 => Categories nan</summary><blockquote>
##### c:\python37\lib\site-packages\pandas\core\dtypes\dtypes.py in validate_categories(categories, fastpath)
487 if not fastpath:
488
--> 489 if categories.hasnans:
490 raise ValueError("Categorical categories cannot be null")
491
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\indexes\base.py in hasnans(self)
2046 """
2047 if self._can_hold_na:
-> 2048 return bool(self._isnan.any())
2049 else:
2050 return False
#### pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
##### c:\python37\lib\site-packages\pandas\core\indexes\base.py in _isnan(self)
2026 """
2027 if self._can_hold_na:
-> 2028 return isna(self)
2029 else:
2030 # shouldn't reach to this condition by checking hasnans beforehand
##### c:\python37\lib\site-packages\pandas\core\dtypes\missing.py in isna(obj)
122 Name: 1, dtype: bool
123 """
--> 124 return _isna(obj)
125
126
##### c:\python37\lib\site-packages\pandas\core\dtypes\missing.py in _isna(obj, inf_as_na)
151 # hack (for now) because MI registers as ndarray
152 elif isinstance(obj, ABCMultiIndex):
--> 153 raise NotImplementedError("isna is not defined for MultiIndex")
154 elif isinstance(obj, type):
155 return False
### NotImplementedError: isna is not defined for MultiIndex
**>>> End of Traceback <<<**
</details></details></br>
~~Seems like in both cases problem starts on **Categorical(self.grouper)** and no categories are found, that's just my doubt.~~
Problem starts during grouper init ([line 413](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/grouper.py#L413)), if lambda or function is used, grouper is assigned lambda or named function respectively and [line 440](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/grouper.py#L440) is skipped as it does not see MultiIndex behind it.
Later on [line 512](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/grouper.py#L512) changes are triggered and [line 518](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/grouper.py#L518) assigns actual value (MultiIndex) instead of a function, however this case skips [line 440](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/grouper.py#L440) casting and bug appears.
Most of the methods listed in grouper.py for lambda made **GroupBy grouper** are working, to name a few: **codes**, **ngroups**, **result_index**. Everything the _BaseGrouper_ has. Only indices and get_group do not work.
Pandas is a great module and I'm happy to use it without those indices especially that I can get the same output performing a `for` loop on .groups __PrettyDict__, yet that's a bug and usage of multiple-level group names is a potential growth area for the module.
I will be happy to continue digging on this problem further on my own and will appreciate any input on the course of this error.
#### Expected Output
```
>>> df.groupby(lambda x: df.iloc[x,0]).indices
{(0, 3): array([0, 1, 2, 3, 4], dtype=int64),
(1, 3): array([8], dtype=int64),
(1, 4): array([5, 6, 7, 9], dtype=int64)}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.0
pip : 20.2.2
setuptools : 40.8.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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Simple extension to Styler to allow cells to be assigned CSS classes from a DataFrame of strings.
Seems to be a reasonably common request.
The existing framework, i.e. the `cell_context` dictionary (which previously did nothing) is leveraged.
@TomAugspurger
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All if statements above the changed block return, so we can remove the else's and flatten out the structure a bit. | {
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} | 1 | 2020-09-06T14:12:04Z | 2020-09-07T20:47:44Z | 2020-09-07T20:47:40Z | CONTRIBUTOR | null | - [x] closes #30859
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Logic of the test is to check that the value hasn't changes and that the dtype remains uint64. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/36165 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36165/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36165/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36165/events | https://github.com/pandas-dev/pandas/issues/36165 | 694,400,142 | MDU6SXNzdWU2OTQ0MDAxNDI= | 36,165 | BUG: Adding offset produces NonExistentTimeError | {
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````
pandas/tests/tseries/offsets/test_offsets_properties.py:88:
[snip]
---------------------------------- Hypothesis ----------------------------------
Falsifying example: test_on_offset_implementations(
dt=datetime.datetime(1900, 1, 1, 0, 0, tzinfo=tzfile('/usr/share/zoneinfo/Iceland')),
offset=<96 * MonthBegins>,
)
You can reproduce this example by temporarily adding @reproduce_failure('5.29.0', b'AAEAMgAAAAAAAAAAAABgAA==') as a decorator on your test case
````
Can reproduce with:
from datetime import datetime
import pytz
from pandas.tseries.offsets import MonthBegin
dt = datetime(1900, 1, 1, 0, 0, tzinfo=pytz.timezone('Iceland'))
offset = MonthBegin(96)
dt + offset
which produces the trace:
````
File "pandas/_libs/tslibs/offsets.pyx", line 422, in pandas._libs.tslibs.offsets.BaseOffset.__add__
File "pandas/_libs/tslibs/offsets.pyx", line 424, in pandas._libs.tslibs.offsets.BaseOffset.__add__
File "pandas/_libs/tslibs/offsets.pyx", line 172, in pandas._libs.tslibs.offsets.apply_wraps.wrapper
File "pandas/_libs/tslibs/timestamps.pyx", line 1317, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
File "pandas/_libs/tslibs/tzconversion.pyx", line 63, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc_single
File "pandas/_libs/tslibs/tzconversion.pyx", line 319, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
pytz.exceptions.NonExistentTimeError: 1908-01-01 00:00:00
````
Not sure if this is a bug or an invalid input. | {
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} | 0 | 2020-09-06T17:05:57Z | 2020-09-06T18:07:56Z | 2020-09-06T18:07:55Z | NONE | null | Backport PR #36115: REGR: append tz-aware DataFrame with tz-naive values | {
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} | 1 | 2020-09-06T17:10:01Z | 2020-09-07T19:04:48Z | 2020-09-07T19:04:43Z | CONTRIBUTOR | null | - [x] pandas/tests/indexing/test_iloc.py
- [x] pandas/tests/indexing/test_indexing.py
- [x] pandas/tests/indexing/test_loc.py
- [x] pandas/tests/internals/test_internals.py
- [x] pandas/tests/io/formats/test_css.py
- [x] pandas/tests/io/formats/test_info.py
- [x] pandas/tests/io/json/test_compression.py
- [x] pandas/tests/io/json/test_pandas.py
- [x] pandas/tests/io/parser/test_c_parser_only.py
- [x] pandas/tests/io/parser/test_parse_dates.py
- [x] pandas/tests/io/parser/test_usecols.py
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added a test to trigger the reported error using code from the issue. An if statement was included to return a more helpful error message - borrowing the solution from the issue. | {
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} | 7 | 2020-09-06T19:04:28Z | 2020-09-09T22:25:32Z | 2020-09-09T22:25:32Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/indexing.html
#### Documentation problem
Index is described as a "set", but behaves as a multiset: len() counts each occurrent of a duplicate element separately.
Also, Index is described as an "ordered set", but that term connotes a set with an order relation on the _values_ of its elements.
#### Suggested fix for documentation
An index seems more like an immutable ordered _list_; maybe, describe it as that? | {
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} | 1 | 2020-09-06T20:26:35Z | 2020-09-07T21:51:02Z | 2020-09-07T20:46:34Z | MEMBER | null | This in turn will let us share more methods with the parent class, at the cost of re-shuffling where we do some of the view-casting. | {
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- [ ] tests added / passed
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Note: this is a docs pr, so not adding tests or a whatsnew entry. | {
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} | 4 | 2020-09-06T21:28:24Z | 2020-09-13T20:33:16Z | 2020-09-13T20:29:42Z | CONTRIBUTOR | null | - [x] closes #33666
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Wrapped the `path` passed into `pandas.io.spss.read_spss` in a `pandas.io.common.stringify_path` call prior to passing it to `pyreadstat.read_sav`. This fixes #33666. I've also included tests validating `pd.read_spss` accepts strings and `pathlib.Path`s. | {
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Need some guidance on tests here. Do we need tests for other file formats here as well? Also unsure about naming conventions.
The primary bug in this case (apart from the indentation problem in the original code) is that the code was ignoring cases where the XML of the cell had multiple child nodes that were not spaces
reverted implementation from #33233 incorporating part of its modifications. | {
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- [x] passes `black pandas`
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Let's get ourselves one less special case to worry about | {
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} | 0 | 2020-09-07T09:49:50Z | 2020-09-08T00:30:20Z | 2020-09-08T00:30:20Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html
#### Documentation problem
It seems that in the examples cells the json_normalize is not preceded by pandas. (i.e. pandas dot)
This does not allow to simply copy and paste the examples.
#### Suggested fix for documentation
Just insert " pandas. " preceding the json_normalize() method in the example cells where it is missing. | {
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} | 26 | 2020-09-07T10:07:24Z | 2020-11-27T07:29:39Z | 2020-11-26T15:54:44Z | CONTRIBUTOR | null | - [ ] closes #36113
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 29 | 2020-09-07T10:32:12Z | 2020-10-29T13:27:08Z | 2020-10-29T13:15:36Z | MEMBER | null | [](https://github.com/simonjayhawkins/pandas-release/actions?query=workflow%3A%22Tag+Release%22)
Tracking issue for the 1.1.3 release.
https://github.com/pandas-dev/pandas/milestone/77
Please do not remove/change milestones from these issues without a note explaining the reasoning (changing milestones doesn't trigger notification)
During issue triage, regressions from 1.0.5 onwards should be milestoned 1.1.3 in the first instance. cc @pandas-dev/pandas-triage
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} | 3 | 2020-09-07T11:28:54Z | 2020-09-07T19:44:44Z | 2020-09-07T19:41:43Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/36155#issuecomment-687846461
does not need backport. release note already in 1.1.2 on backport branch | {
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} | 6 | 2020-09-07T11:52:53Z | 2020-09-13T15:53:38Z | 2020-09-13T15:53:38Z | 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
pdf = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
pdf.apply(["sum", lambda df: df.sum(), lambda df: df.sum()]) # lambdas have postfix 0, 1 ...
A B
sum 12 27
<lambda_0> 12 27
<lambda_1> 12 27
s = pd.Series([4] * 3)
s.apply(["sum", lambda df: df.sum(), lambda df: df.sum()]) # lambdas have not postfix 0, 1 ...
sum 12
<lambda> 12
<lambda> 12
dtype: int64
```
#### Problem description
Could anyone explain please? Is it normal behavior? Why DataFrame's index has postfix for lambdas, but Series's index hasn't. The same behavior is observed for `agg`. There is no such behavior in pandas==1.0.5.
#### Output of ``pd.show_versions()``
<details>
pandas : 1.1.1
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2020-09-07T12:11:10Z | 2020-09-07T13:04:29Z | 2020-09-07T12:30:43Z | NONE | null | - [x] I have checked that this issue has not already been reported.
I've looked through all issues regarding `to_datetime`, but I could've missed something.
- [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
pd.to_datetime(["13.12.2020", "12.13.2020"])
# DatetimeIndex(['2020-12-13', '2020-12-13'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
Two different inputs produce the same result. The reason is that the function uses a different format for different entries. I don't know what the usecase of this is, but usually all the data has the same format. The error also passes silently.
#### Expected Output
Either an error that something is wrong with the hint to use `days_first=True` or detect the format for all values and apply it.
I'm aware that the format can't always be determined, but it is possible to detect if something is wrong.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.1.1
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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} | 2 | 2020-09-07T12:11:14Z | 2020-09-07T19:41:30Z | 2020-09-07T19:39:16Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/35831#issuecomment-688163069
(I've not tested this locally, after backport can retrigger https://github.com/MacPython/pandas-wheels/pull/97 to check) | {
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} | 1 | 2020-09-07T12:16:13Z | 2020-09-07T19:07:35Z | 2020-09-07T19:05:51Z | MEMBER | null | - [ ] closes #35921
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2020-09-07T15:40:32Z | 2020-09-09T13:24:19Z | 2020-09-09T13:24:15Z | CONTRIBUTOR | null | #35925
- pandas/tests/arrays/categorical/test_replace.py
- pandas/tests/arrays/sparse/test_array.py
- pandas/tests/arrays/test_array.py
- pandas/tests/arrays/test_timedeltas.py
- pandas/tests/base/test_conversion.py
- pandas/tests/dtypes/test_missing.py
- pandas/tests/extension/base/methods.py
- pandas/tests/extension/test_sparse.py
- pandas/tests/frame/indexing/test_setitem.py
please give me a feedback if there is something that I have to change.. thank you before | {
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} | 9 | 2020-09-07T16:00:38Z | 2020-09-08T03:37:02Z | 2020-09-08T00:30:20Z | CONTRIBUTOR | null | - [x] closes #36184
- [x] Inserted " pandas. " preceding the json_normalize() method in the example cells where it was missing.
Documentation Location : [https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html) | {
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"description": "A propos... | open | false | null | [] | null | 71 | 2020-09-07T16:55:17Z | 2021-10-29T22:16:54Z | null | CONTRIBUTOR | null | Update: concrete proposal for Copy-on-Write: https://docs.google.com/document/d/1ZCQ9mx3LBMy-nhwRl33_jgcvWo9IWdEfxDNQ2thyTb0/edit?usp=sharing
---
[_original post_]
Hi all,
During the recent sprint we discussed copy / view semantics in indexing operations. I've tried to summarize *a* proposal that came out of it.
The tldr is
1. Indexing operations always return a view *where possible* (always when indexing columns, if possible when indexing the rows).
2. For consistent behavior, we implement either Copy on Write or Error on Write (explained in the proposal)
3. For readability / understandability, we give users the tools to control in code whether mutations to a "child" dataframe (result of an indexing operation) propagate to the parent, while avoiding unnecessary "defensive" copies.
The full proposal is available at https://docs.google.com/document/d/1csGE4qigPR2vzmU2--jwURn3sK5k5eVewinxd8OUPk0/edit. I'd like to collect comments there and then propose properly by adding it to the roadmap.
cc @pandas-dev/pandas-core | {
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} | 0 | 2020-09-07T17:38:01Z | 2020-09-08T15:31:24Z | 2020-09-08T15:29:03Z | MEMBER | null | The blocks are made unnecessary by the recent changes to use BlockManager.apply | {
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} | 1 | 2020-09-07T18:39:53Z | 2020-09-08T09:51:21Z | 2020-09-08T09:51:21Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [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
pd.DataFrame({"s1": []}).groupby("s1").rolling(window=1).sum()
```
#### Problem description
Since a pandas update, the groupby above started failing in the edge case of an empty DataFrame. It used to simply return an empty DataFrame before, which seems like a more robust behaviour to me.
#### Expected Output
```
>>> pd.DataFrame({"s1": []}).groupby("s1").rolling(window=1).sum()
Empty DataFrame
Columns: []
Index: []
```
#### Actual Output
```
>>> pd.DataFrame({"s1": []}).groupby("s1").rolling(window=1).sum()'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "site-packages/pandas/core/window/rolling.py", line 2072, in sum
return super().sum(*args, **kwargs)
File "site-packages/pandas/core/window/rolling.py", line 1424, in sum
window_func, center=self.center, floor=0, name="sum", **kwargs
File "site-packages/pandas/core/window/rolling.py", line 2194, in _apply
**kwargs,
File "site-packages/pandas/core/window/rolling.py", line 528, in _apply
blocks, obj = self._create_blocks(self._selected_obj)
File "site-packages/pandas/core/window/rolling.py", line 2230, in _create_blocks
list(self._groupby.grouper.indices.values())
File "<__array_function__ internals>", line 6, in concatenate
ValueError: need at least one array to concatenate
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.17-200.fc32.x86_64
Version : #1 SMP Fri Aug 21 15:23:46 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.7.3
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.12.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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- [x] closes #33548
- [x] closes #35275
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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I think I found a solution for this issue. We used `obj.index` to get the Index values. In case of resample `obj.index` had the original index. This index was not necessarily a RangeIndex of course -> resulted in a IndexError. In case of a RangeIndex the object `self._grouper` may have been resorted -> Wrong elements were selected. The object `self.obj` stored the original DataFrame, but already sorted in the first go around (is overwritten later). I stored the mapping from original index to a new synthetic RangeIndex (sorted after the grouping column). This allows us to replace the Index of obj in a way, that the take function works.
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} | 9 | 2020-09-07T19:51:30Z | 2021-03-30T20:16:43Z | 2020-11-06T18:03:18Z | MEMBER | null | - [x] closes https://github.com/pandas-dev/pandas/issues/35429
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~(Need to wait until the v1.1.3 whatsnew gets merged before adding a release note.)~
This is taking the approach of treating NA as False within NDFrame.mask and NDFrame.where when dealing with a nullable boolean mask. | {
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} | 2 | 2020-09-07T19:54:48Z | 2020-09-07T21:13:36Z | 2020-09-07T21:11:30Z | MEMBER | null | - [x] closes #16784
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Thanks @jbrockmendel for fixing my related issue | {
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} | 0 | 2020-09-07T21:13:12Z | 2020-09-07T22:10:03Z | 2020-09-07T22:10:03Z | NONE | null | Backport PR #36152: Fix compressed multiindex for output of groupby.rolling | {
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https://api.github.com/repos/pandas-dev/pandas/issues/36204 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36204/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36204/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36204/events | https://github.com/pandas-dev/pandas/issues/36204 | 695,410,606 | MDU6SXNzdWU2OTU0MTA2MDY= | 36,204 | BUG: Incorrect parsing of ISO 8601 durations | {
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} | 3 | 2020-09-07T22:06:02Z | 2020-10-17T00:07:07Z | 2020-10-17T00:07:07Z | 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
pd.Timedelta('P1Y')
pd.Timedelta('P1M')
pd.Timedelta('P1W')
pd.Timedelta('P1D')
pd.Timedelta('P1DT1H')
pd.Timedelta('PT1H')
pd.Timedelta('P1H')
```
#### Problem description
Trying [most iso8601 duration formats](https://en.wikipedia.org/wiki/ISO_8601#Durations) fails.
For above examples:
* Expected pd.Timedelta instances of 1yr, 1mo, 1wk, 1d, 1d 1hr
* ... but got `ValueError: Invalid ISO 8601 Duration format - ...` on Y/M/W, PT1H
* ... 1D was '0'
* ... 1DT1H was '1d' (no hr)
Encountered while trying to figure out neo4j.time.Duration().isoformat() -> pandas ->arrow > rapids cudf timedelta[*] . Neo4j returns values like `P1Y3M15DT2H3M` and `P1Y3M`. | {
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} | 1 | 2020-09-07T22:06:54Z | 2020-09-08T10:23:17Z | 2020-09-08T10:22:20Z | MEMBER | null | https://github.com/pandas-dev/pandas/issues/35831#issuecomment-688518465 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/36206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36206/events | https://github.com/pandas-dev/pandas/pull/36206 | 695,463,419 | MDExOlB1bGxSZXF1ZXN0NDgxNzA1MjUw | 36,206 | REF: implement Categorical._box_func, make _box_func a method | {
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} | 1 | 2020-09-08T00:50:57Z | 2020-09-08T23:10:51Z | 2020-09-08T22:24:55Z | MEMBER | null | Defining `_box_func` on `Categorical` is to facilitate more code sharing with datetimelike. Also there are a bunch of places that do something _similar_ but slightly different that I want to standardize within `Categorical`.
Changing `_box_func` from a property-lambda to a method on the datetimelike arrays gives a slight perf boost:
```
In [1]: import pandas as pd
In [2]: dti = pd.date_range("2016-01-01", periods=3)
In [3]: %timeit dti[1]
8.73 µs ± 50.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
8.08 µs ± 119 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
```
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] Changed `pandas.` to `pd.` in the docstring of `pandas.json_normalize`. Fixed the expected results.
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} | 2 | 2020-09-08T02:01:13Z | 2020-11-12T05:15:26Z | 2020-09-08T09:51:21Z | MEMBER | null | - [x] closes #36197
- [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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} | 6 | 2020-09-08T03:03:56Z | 2020-10-12T01:23:58Z | 2020-10-12T01:23:57Z | MEMBER | null | We use special logic for dt64/td64/period dtypes that makes view/copy behavior different from other dtypes:
```
dti = pd.date_range("2016-01-01", periods=4, tz="US/Pacific")
key = np.array([True, True, False, False])
ser1 = pd.Series(dti._data)
ser2 = pd.Series(range(4))
res1 = ser1[key]
res2 = ser2[key]
>>> res1._values._data.base is None
False
>>> res2._values.base is None
True
```
cc @jorisvandenbossche IIRC you advocated not doing this special casing. | {
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- [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.
---
When trying to convert a DataFrame that contains Path objects to json it crashes:
```python
import pandas as pd
from pathlib import Path
df = pd.DataFrame({'a': [Path('m1')]})
df.to_json()
```
output
```
Traceback (most recent call last):
File "<ipython-input-67-05359facf0e6>", line 1, in <module>
df.to_json()
File "/home/rikard/.local/lib/python3.7/site-packages/pandas/core/generic.py", line 2305, in to_json
indent=indent,
File "/home/rikard/.local/lib/python3.7/site-packages/pandas/io/json/_json.py", line 84, in to_json
indent=indent,
File "/home/rikard/.local/lib/python3.7/site-packages/pandas/io/json/_json.py", line 144, in write
self.indent,
File "/home/rikard/.local/lib/python3.7/site-packages/pandas/io/json/_json.py", line 244, in _write
indent,
File "/home/rikard/.local/lib/python3.7/site-packages/pandas/io/json/_json.py", line 166, in _write
indent=indent,
OverflowError: Maximum recursion level reached
```
It might be that Path objects are not supported by to_json, but I wouldn't expect an infinite recursion.
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-64-generic
Version : #58-Ubuntu SMP Fri Jul 10 19:33:51 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.16.2
pytz : 2019.2
dateutil : 2.7.3
pip : 18.1
setuptools : 41.1.0
Cython : None
pytest : 3.7.4
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | 2020-09-08T09:35:01Z | 2020-09-13T13:15:07Z | 2020-09-13T13:15:07Z | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = {"name": ["abc", "xyz"]}
df = pd.DataFrame(data)
print(df.agg({'name': 'count'}))
```
#### Problem description
In the above code snippet, there is a column called 'name' in the dataframe and when executing it an exception is being thrown. Following the stacktrace , it is observed that in line 475 of core/base.py , df.name is being passed to the `name` argument of
result = Series(result, name=getattr(self, "name", None))
when the dataframe has a column called 'name'.
The same code snippet works fine for any other column name. For example, if we change the column name to `nameee`. It executes fine.
Stack trace
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
470 try:
--> 471 result = DataFrame(result)
472 except ValueError:
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
467 elif isinstance(data, dict):
--> 468 mgr = init_dict(data, index, columns, dtype=dtype)
469 elif isinstance(data, ma.MaskedArray):
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
282 ]
--> 283 return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
284
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype, verify_integrity)
77 if index is None:
---> 78 index = extract_index(arrays)
79 else:
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/internals/construction.py in extract_index(data)
386 if not indexes and not raw_lengths:
--> 387 raise ValueError("If using all scalar values, you must pass an index")
388
ValueError: If using all scalar values, you must pass an index
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/frame.py in aggregate(self, func, axis, *args, **kwargs)
7358 try:
-> 7359 result, how = self._aggregate(func, axis=axis, *args, **kwargs)
7360 except TypeError as err:
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/frame.py in _aggregate(self, arg, axis, *args, **kwargs)
7383 return result, how
-> 7384 return super()._aggregate(arg, *args, **kwargs)
7385
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
474 # we have a dict of scalars
--> 475 result = Series(result, name=getattr(self, "name", None))
476
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
228
--> 229 name = ibase.maybe_extract_name(name, data, type(self))
230
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/indexes/base.py in maybe_extract_name(name, obj, cls)
5658 if not is_hashable(name):
-> 5659 raise TypeError(f"{cls.__name__}.name must be a hashable type")
5660
TypeError: Series.name must be a hashable type
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-6-efe06ed8dce0> in <module>
2 data = {"name": ["abc", "xyz"]}
3 df = pd.DataFrame(data)
----> 4 print(df.agg({'name': 'count'}))
~/.virtualenvs/dimensions-connectors/lib/python3.7/site-packages/pandas/core/frame.py in aggregate(self, func, axis, *args, **kwargs)
7363 f"incompatible data and dtype: {err}"
7364 )
-> 7365 raise exc from err
7366 if result is None:
7367 return self.apply(func, axis=axis, args=args, **kwargs)
TypeError: DataFrame constructor called with incompatible data and dtype: Series.name must be a hashable type
```
#### Expected Output
name 2
dtype: int64
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 17.4.0
Version : Darwin Kernel Version 17.4.0: Sun Dec 17 09:19:54 PST 2017; root:xnu-4570.41.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : 0.29.16
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.11
tables : None
tabulate : 0.7.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
<details>
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} | 0 | 2020-09-08T09:53:16Z | 2020-09-08T10:49:23Z | 2020-09-08T10:49:23Z | NONE | null | Backport PR #36208: BUG: GroupbyRolling with an empty frame | {
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"following_url": "https://api.github.com/users/erfannariman/following{/other_use... | null | 3 | 2020-09-08T10:09:42Z | 2020-09-08T12:32:39Z | 2020-09-08T12:32:39Z | CONTRIBUTOR | null | Right now both `DataFrame.explode` and `Series.explode` will unnest a list to rows. But most of the times the origin of the problem is that we have a concatenated string with a seperator. So the solution is to overwrite the column while splitting it to a list and then calling `DataFrame.explode`:
```python
df = pd.DataFrame({
"a":["string1;string2;string3", "string4;string5"]
})
a
0 string1;string2;string3
1 string4;string5
df.assign(a=df["a"].str.split(";")).explode("a")
a
0 string1
0 string2
0 string3
1 string4
1 string5
```
What would be more convenient is to have a `sep` argument which then checks if the column dtype is "object" or "string" and does the splitting itself:
```
df.explode("a", sep=";")
a
0 string1
0 string2
0 string3
1 string4
1 string5
```
Note: if argument `sep` is given and the column values is list-like, it should raise. | {
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} | 0 | 2020-09-08T10:22:30Z | 2020-09-08T11:05:52Z | 2020-09-08T11:05:52Z | NONE | null | Backport PR #36205: DOC: doc fix | {
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} | 0 | 2020-09-08T11:17:48Z | 2020-09-30T12:22:25Z | 2020-09-30T12:22:25Z | CONTRIBUTOR | null | This is an issue to track a refactor of StringMethods that'll be necessary for ArrowStringArray to use pyarrow compute algorithms.
We'll need to update StringMethods
1. Extract the array from the Series / Index
2. Dispatch the string methods
3. Wrap the result
So in my proposal, we'll have something like
```python
class StringMethods:
def __init__(self, data, ...):
self._array = data.array
...
def upper(self):
return self._wrap_result(self._array._str.upper())
```
cc @xhochy. I have a branch started and will hopefully finish it off this week. | {
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