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} | 1 | "2021-04-23T12:24:45Z" | "2021-04-26T15:12:04Z" | "2021-04-26T14:52:02Z" | CONTRIBUTOR | null | I'm experimenting with subclassing NumericIndex and using the test suite to check if everything passes ok (by adding the new subclass to `indices_dict` in `conftest.py` and making a `makeXIndex` func in `_testing.py.
`COMPATIBLE_INCONSISTENT_PAIRS ` in `test_setops.py` is very tied to specific index classes, so is difficult to use on new index classes. So here's a PR to make this part more friendly to testing subclasses by making it test dtypes instead of index class. | {
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For example, for a scalar right operand, we can check it only once if it's compatible with numexpr usage. Or for ArrayManager, we can check the size constraint once upfront (since each array has the same size). For those cases, this avoids overhead in `expressions.evaluate`.
To this end, I created a new `can_use_numexpr` that starts to gather some checks (there might be more that can be added), call this up front in `DataFrame._dispatch_frame_op` (this could IMO be bit cleaner with https://github.com/pandas-dev/pandas/pull/39772, but the if/else checks inside `_dispatch_frame_op` work as well). And then the result of `can_use_numexpr` is passed through to the array_ops. | {
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} | 1 | "2021-04-23T13:24:50Z" | "2021-04-26T12:35:02Z" | "2021-04-26T12:14:12Z" | CONTRIBUTOR | null | rename `Styler.render` -> `Styler._render_html` and create alias for backwards compat.
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] | open | false | null | [] | null | 0 | "2021-04-23T13:30:20Z" | "2021-04-23T13:30:20Z" | 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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(index=range(10**6))
In [4]: df['categories'] = np.arange(len(df)) % 4
In [5]: df['data'] = np.random.random(len(df))
In [6]: for i in range(10):
...: df[f'i:{i}'] = np.random.random(len(df))
...:
In [7]: df.groupby('categories')[['data']].boxplot()
Out[7]:
0 AxesSubplot(0.1,0.559091;0.363636x0.340909)
1 AxesSubplot(0.536364,0.559091;0.363636x0.340909)
2 AxesSubplot(0.1,0.15;0.363636x0.340909)
3 AxesSubplot(0.536364,0.15;0.363636x0.340909)
dtype: object
```
... results in the following:
#### Problem description
The variables `i:*` should not appear at all in the plot, given that in ``In [7]``, only ``data`` was selected from the ``DataFrameGroupby``. Neither should the grouping variable. Indeed,
```python
In [8]: df.groupby('categories')[['data']].describe()
Out[8]:
data
count mean std min 25% 50% 75% max
categories
0 250000.0 0.499758 0.288484 2.852768e-07 0.249453 0.499626 0.749729 0.999990
1 250000.0 0.499857 0.288900 7.654410e-06 0.249737 0.499469 0.750530 0.999998
2 250000.0 0.499340 0.288823 7.296400e-07 0.248793 0.498451 0.749593 1.000000
3 250000.0 0.500614 0.288230 1.751182e-06 0.251923 0.501253 0.749567 0.999994
```
#### Expected Output
The desired outcome is obtained with
```python
In [9]: df[['data']].groupby(df['categories'])[['data']].boxplot()
```
...which produces
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.2.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.18.1.el7.x86_64
Version : #1 SMP Tue Mar 17 23:49:17 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 40.6.2
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 4 | "2021-04-23T14:30:20Z" | "2021-09-02T01:40:52Z" | null | NONE | null | #### Is your feature request related to a problem?
While Panda's documentation is stellar, it often only answers the "how" of a specific function, not the "when" or "why". This makes it hard to know what a specific function should be used for and what common usecases are.
#### Describe the solution you'd like
Matplotlib and Sklear both have a very nice solution to this problem, where they automatically (I think) include links in the end of documentation pages towards examples that use that specific function, and to relevant places in the user guide.
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} | 1 | "2021-04-23T19:24:01Z" | "2021-04-28T16:08:59Z" | "2021-04-28T10:17:19Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | "2021-04-23T20:57:21Z" | "2021-07-23T22:10:45Z" | "2021-05-26T11:57:43Z" | CONTRIBUTOR | null | As the [docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files) explain, the binary spreadsheet formats (xls, xlsb) store all numbers as floats, so by default pandas tries to convert floats to integers if it doesn’t lose information (1.0 --> 1). "You can pass ``convert_float=False` to disable this behavior, which may give a slight performance improvement." I tested this on four file types for a spreadsheet of ~440,000 cells, and recorded the best times out of 10 repetitions:
File type | convert_floats=True | convert_floats=False | speed up
-|-|-|-
xls |1.081|1.036 | 4.2%
xlsb | 3.413 | 3.357 | 1.6%
ods | 27.798 | 27.770 | 0.1%
xlsx | 5.182 | 5.189 | -0.1%
`convert_floats` was probably written for the benefit of .xls files, but the benefit is minor. The .xlsx files even have a slight penalty because openpyxl already converts floats to int where possible, and so pandas converts them back to float if `convert_floats=False`.
Since .xlsx files are now the most common spreadsheet format (citation: google search), and `convert_floats` only exists for performance, is it time to remove `convert_floats`? The spreadsheet engines would keep the behaviour of `convert_floats=True` and the argument would be deprecated. This change would simplify all four engines, and if anybody really needs their ints as floats, they can always specify a `dtype`. Note: this possible deprecation came up in https://github.com/pandas-dev/pandas/issues/8212#issuecomment-54804297 before `dtype` was finalized in `read_excel`.
I can work on this if the community likes the idea. | {
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Deprecation for c parser | {
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This currently raises
<details>
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2021.1/scratches/scratch_4.py", line 539, in <module>
pd.read_csv('test.csv', header=0, usecols=[0, 10], engine='python')
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/readers.py", line 552, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/readers.py", line 471, in _read
return parser.read(nrows)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/readers.py", line 998, in read
index, columns, col_dict = self._engine.read(nrows)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/python_parser.py", line 285, in read
data, columns = self._exclude_implicit_index(alldata)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/python_parser.py", line 303, in _exclude_implicit_index
names = [names[i] for i in sorted(self._col_indices)]
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers/python_parser.py", line 303, in <listcomp>
names = [names[i] for i in sorted(self._col_indices)]
</details>
IndexError: list index out of range
on master but unfortunately works on 1.2.4, so we can either raise ParserError with 1.2.4 or fix and deprecate then to remove in 2.0, related to #41129
I think fixing and deprecating would be more sensible, but only realised that this works on 1.2.4 after finishing this, so wanted to put up for discussion at least :)
cc @gfyoung
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} | 1 | "2021-04-24T09:35:49Z" | "2021-04-26T12:41:35Z" | "2021-04-26T11:42:21Z" | CONTRIBUTOR | null | The uniques returned from `pd.factorize` should keep their original dtype, where possible.
```python
>>> arr =np.array([1,2], dtype="int8")
>>> pd.factorize(arr)
(array([0, 1], dtype=int64), array([1, 2], dtype=int64)) # master
(array([0, 1], dtype=int64), array([1, 2], dtype=int8)) # this PR
```
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} | 0 | "2021-04-24T11:12:42Z" | "2021-04-27T08:10:36Z" | "2021-04-26T13:29:27Z" | CONTRIBUTOR | null | - [x] xref #37643
- [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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- [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
```bash
$ pip show pandas
Name: pandas
Version: 1.2.4
```
```python
import pandas as pd
import numpy as np
lzt = ["a","b","c"]
arr = np.array(lzt)
df = pd.DataFrame(arr).astype('string')
srs = df[0]
u = srs.unique()
type(u)
<class 'pandas.core.arrays.string_.StringArray'>
u.dtype == 'string'
True
u.tolist()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'StringArray' object has no attribute 'tolist'
```
#### Problem description
So I tried changing my code to use `list(srs.unique())`, but then all of my numeric dtypes become unserializable for JSON.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
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 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41135 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41135/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41135/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41135/events | https://github.com/pandas-dev/pandas/issues/41135 | 866,726,983 | MDU6SXNzdWU4NjY3MjY5ODM= | 41,135 | BUG: numpy dtype checker not understand string dtype `TypeError: data type not understood` | {
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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.
---
#### Code Sample, a copy-pastable example
```bash
$ pip show pandas
Name: pandas
Version: 1.2.4
```
```python
import pandas as pd
import numpy as np
lzt = ["a","b","c"]
arr = np.array(lzt)
df = pd.DataFrame(arr).astype('string')
srs = df[0]
u = srs.unique()
type(u)
<class 'pandas.core.arrays.string_.StringArray'>
u.dtype == 'string'
True
np.issubdtype(u.dtype, np.signedinteger)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/layne/.pyenv/versions/3.7.6/lib/python3.7/site-packages/numpy/core/numerictypes.py", line 393, in issubdtype
arg1 = dtype(arg1).type
TypeError: data type not understood
```
#### Problem description
This might be better off as a numpy issue or a non-issue, but i wanted to check here first. I rely on np parent classes for how i handle different pandas columns of different dtypes. Could 'string' roll up to 'object' type?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
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 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-04-24T14:00:28Z" | "2021-05-21T13:50:50Z" | "2021-05-21T13:50:50Z" | MEMBER | null | This is value-dependent behavior:
```
print(DataFrame({'a': [1, 3]}).groupby([1, 1]).mean())
```
results in the integer 2, whereas
```
print(DataFrame({'a': [1, 3]}).mean())
```
results in 2.0. | {
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# Your code here
```
#### Problem description
I recently tried to create a shallow copy of a pandas.Series that values where python lists. But when I tried to edit the copied series, the original series was affected as well.
```python
import pandas as pd
s = pd.Series({'foo': [1, 2, 3]})
s_copy = s.copy(deep=False)
s_copy['foo'] = [1, 2, 3, 4]
print(s)
```
This yields: foo [1, 2, 3, 4]
#### Expected Output
As this is meant to be a shallow copy, that expected behavior is that the original Series shall not be affected: foo [1, 2, 3]
#### Output of ``pd.show_versions()``
<details>
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 56.0.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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} | 3 | "2021-04-24T15:04:21Z" | "2021-11-09T22:06:59Z" | "2021-05-21T13:50:50Z" | MEMBER | null | - [x] closes #41137
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Built on top of #40790 | {
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} | 4 | "2021-04-24T15:33:28Z" | "2021-04-26T21:58:37Z" | "2021-04-26T12:53:55Z" | MEMBER | null | We have a few tests which are really slow and not marked as such. I went through the list below and marked everything greater than 4 seconds as slow. Should give us between 4 and 5 minutes performance boost
<details>
# 29.68s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_iterator_loop
# 12.98s call pandas/tests/test_sorting.py::TestSorting::test_int64_overflow_moar
# 11.43s call pandas/tests/indexing/multiindex/test_chaining_and_caching.py::test_indexer_caching
# 10.13s call pandas/tests/groupby/transform/test_transform.py::test_cython_transform_frame[shift-args3-<lambda>]
# 9.87s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_from_iterator
# 9.79s call pandas/tests/groupby/transform/test_transform.py::test_cython_transform_frame[shift-args2-<lambda>]
# 9.41s teardown pandas/tests/window/moments/test_moments_rolling_quantile.py::test_center_reindex_frame[1.0]
# 8.82s call pandas/tests/io/sas/test_xport.py::TestXport::test1_basic
# 8.43s call pandas/tests/io/parser/common/test_chunksize.py::test_warn_if_chunks_have_mismatched_type[python]
# 8.38s call pandas/tests/io/parser/common/test_chunksize.py::test_chunks_have_consistent_numerical_type[python]
# 8.13s call pandas/tests/io/parser/test_c_parser_only.py::test_grow_boundary_at_cap[c_low]
# 8.12s call pandas/tests/io/parser/test_c_parser_only.py::test_grow_boundary_at_cap[c_high]
# 6.97s call pandas/tests/indexes/datetimes/test_date_range.py::TestDateRanges::test_date_range_int64_overflow_stride_endpoint_different_signs
# 5.90s call pandas/tests/frame/test_constructors.py::TestDataFrameConstructors::test_constructor_mrecarray
# 5.84s teardown pandas/tests/window/moments/test_moments_rolling_skew_kurt.py::test_center_reindex_frame[skew]
# 5.61s call pandas/tests/io/parser/test_c_parser_only.py::test_precise_conversion[c_high]
# 5.29s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_path_pathlib
# 5.13s call pandas/tests/groupby/transform/test_transform.py::test_cython_transform_frame[cumsum-args1-<lambda>]
# 4.93s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_from_file
# 4.86s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_from_buffer
# 4.84s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_path_localpath
# 4.59s call pandas/tests/indexes/multi/test_integrity.py::test_hash_collisions
# 4.26s call pandas/tests/indexing/interval/test_interval.py::TestIntervalIndex::test_loc_getitem_large_series
# 3.99s call pandas/tests/io/parser/test_c_parser_only.py::test_precise_conversion[c_low]
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} | 5 | "2021-04-24T16:07:31Z" | "2021-05-12T01:46:03Z" | "2021-04-30T20:49:51Z" | CONTRIBUTOR | null | Series clipping method could not handle series with NA values.
Issue was fixed by first comparing values and only after then converted
to numpy array. Now the test code provided with the issue ticket provides an expected result.
- [x] closes #40581
- [x] tests added / passed
- I did run test_fast.sh and 3 test failed but I think they are not related to my changes
- test_can_set_locale_valid_set
- test_header_multi_index_blank_line[c_high]
- test_header_multi_index_blank_line[c_high]
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-04-24T17:36:11Z" | "2021-05-21T16:20:32Z" | "2021-05-21T16:20:32Z" | CONTRIBUTOR | null | The api is inconsistent in some parts. Currently Styler only allows either non-sparse index and cols or sparse index and cols, but not separately. This should be a relatively easy change to implement into the `_translation` and/or `rendering` code. | {
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} | 0 | "2021-04-24T17:52:38Z" | "2021-05-06T23:32:14Z" | "2021-05-06T23:32:02Z" | CONTRIBUTOR | null | Currently Styler only works with unique indexes, it may be possible to allow non-unique indexes, and prohibit only certain features that need a unique index | {
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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.
---
**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
import sympy
scalar_float = 3.5
scalar_sympy = sympy.Rational(7, 2)
series_float = pandas.Series([3.5])
series_sympy = pandas.Series([sympy.Rational(7, 2)])
scalar_float * series_float # Yields a single-element float series
series_float * scalar_float # Yields a single-element float series
scalar_sympy * series_sympy # YIELDS A SCALAR FLOAT
series_sympy * scalar_sympy # Yields a single-element sympy Rational series
```
#### Problem description
When doing arithmetic between a scalar `sympy Rational` value and a single-element `pandas Series`, if the scalar value is on the left side of the arithmetic equation the result is not a single-element `Series` as you would expect, but is instead a scalar value. Also, the `sympy Rational` gets evaluated into a floating point number.
See the `ipython` output below:
```
In [1]: import pandas
In [2]: import sympy
In [3]: scalar_float = 3.5
In [4]: scalar_sympy = sympy.Rational(7, 2)
In [5]: series_float = pandas.Series([3.5])
In [6]: series_sympy = pandas.Series([sympy.Rational(7, 2)])
In [7]: scalar_float * series_float
Out[7]:
0 12.25 # <---------Correct, as expected
dtype: float64
In [8]: series_float * scalar_float
Out[8]:
0 12.25 # <---------Correct, as expected
dtype: float64
In [9]: scalar_sympy * series_sympy
Out[9]: 12.2500000000000 # <-------------INCORRECT
In [10]: series_sympy * scalar_sympy
Out[10]:
0 49/4 # <---------Correct, as expected
dtype: object
```
#### Expected Output
I would expect the output of `scalar_sympy * series_sympy` to be equal to `series_sympy * scalar_sympy`
#### Output of ``pd.show_versions()``
<details>
In [8]: pandas.show_versions()
/Users/maxmcclorey/Documents/workspace/captivateiq/venv/lib/python3.8/site-packages/setuptools/distutils_patch.py:25: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
warnings.warn(
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.0
pip : 20.3.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ATM we correctly wrap them for DataFrameGroupBy but not for SeriesGroupBy. Doing this correctly for SeriesGroupBy has the benefit of avoiding the need to special-case it in DataFrameGroupBy
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} | 12 | "2021-04-25T06:52:40Z" | "2021-07-11T16:12:41Z" | "2021-07-11T16:12:25Z" | 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.
---
<img width="825" alt="截屏2021-04-25 下午2 46 04" src="https://user-images.githubusercontent.com/34571894/115983689-476ee880-a5d5-11eb-8f01-9ccc7d04659f.png">
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:06:51 PST 2021; root:xnu-7195.81.3~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 6 | "2021-04-25T07:26:22Z" | "2021-05-23T06:18:22Z" | "2021-05-23T06:18:22Z" | CONTRIBUTOR | null | __Problem:__
One of the only operations I ever find myself going out of my way to create a sql temp-table upload my frame and do the operation in sql is for filtered cross joins. For large datasets they can both take a long time and use large amounts of memory. Here is an example of such an operation
__Current Solution:__
```
dr = pd.date_range('1900-01-01', periods=10000)
df = pd.DataFrame({
'start_date': pd.Series(dr),
'end_date': pd.Series(dr) + pd.Timedelta(days=30),
'value': range(len(dr)),
'id': np.random.choice(list('abcdefg'), len(dr))
})
dates = pd.Series(pd.date_range(df.start_date.min(), df.end_date.max())).to_frame('ts')
# takes ~8 sec (not particularly long in this example) and peak memory at ~10 GB
df.merge(dates, how='cross').loc[lambda dfx: (dfx.ts >= dfx.start_date) & (dfx.ts < dfx.end_date)]
```
This is how it would be done in SQL, which I don't have memory stats for, but is much faster
```
select * from table1
cross join table2
where ts >= start_date and ts <= end_date
```
This is a common problem people try to solve using pandas, as there are several instances on stack overflow of people asking how to do this (here are just a few I found with a quick search, there are many more):
https://stackoverflow.com/questions/43593554/merging-two-dataframes-based-on-a-date-between-two-other-dates-without-a-common/43594038
https://stackoverflow.com/questions/30627968/merge-pandas-dataframes-where-one-value-is-between-two-others
https://stackoverflow.com/questions/31328014/merging-dataframes-based-on-date-range
There are many different solutions ranging in complexity and performance. Often the answer people provide is to just do it in sql.
__Proposed Solution:__ (not concrete/final proposal, just a starting point for discussion)
I think that one way this could be solved in a performant way with an easy to use interface is to provide a lazily evaluated cross join. Which is resolved to a pandas dataframe after a filtering operation (e.g. `.loc`, `.query`, etc.). Below I provide an example implementation, that I don't think is the complete or optimal solution, but rather just a quick example to get the point across.
```
import itertools as it
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
class LazyMergeLoc:
def __get__(self, instance, owner):
self.chunk_pairs = instance.chunk_pairs
return self
def __getitem__(self, item):
results = []
for df1, df2 in self.chunk_pairs:
result = df1.merge(df2, how='cross').loc[item]
results.append(result)
return pd.concat(results)
class LazyMerge:
loc = LazyMergeLoc()
def __init__(self, chunk_pairs):
self.chunk_pairs = chunk_pairs
def lazy_cross_join(df1, df2, left_chunk_size, right_chunk_size):
left_chunks = list(chunks(df, left_chunk_size))
right_chunks = list(chunks(dates, right_chunk_size))
chunk_pairs = it.product(left_chuknks, right_chunks)
return LazyMerge(chunk_pairs)
pd.lazy_cross_join(df, dates, left_chunk_size=1000, right_chunk_size=1000).loc[lambda dfx: (dfx.ts >= dfx.start_date) & (dfx.ts < dfx.end_date)]
```
This solution provides the same result in the same time (and much faster too in many cases), and peak memory of only a couple hundred MB. ~50x less memory.
Again, I don't think this is necessarily the right implementation or interface. I'm sure there are even more performant ways to resolve the lazily defined join, not through chunks, perhaps some optimized cython code. But I do think this type of lazy merge would provide a large benefit to the library.
__Breaking Implications:__
None
__Alternatives:__
Many solutions provided in the linked (and other) SO posts. All of which are either (a) not performant, (b) complicated, (c) not generalizable, or (d) require leaving the pandas ecosystem.
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} | 3 | "2021-04-25T08:07:40Z" | "2021-04-26T12:03:03Z" | "2021-04-26T12:03:03Z" | CONTRIBUTOR | null | ```
RangeIndex(start=0, stop=2).isin(RangeIndex(start=0, stop=1)) # <- TypeError
Index([0, 1, 2]).isin(RangeIndex(start=0, stop=1)) # <- TypeError
RangeIndex(start=0, stop=2).isin(Index([0, 1])) # <- OK
Index([0, 1, 2]).isin(Index([0, 1])) # <- OK
```
Have only tested on master. Not yet on 1.2.4
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https://api.github.com/repos/pandas-dev/pandas/issues/41152 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41152/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41152/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41152/events | https://github.com/pandas-dev/pandas/pull/41152 | 867,053,475 | MDExOlB1bGxSZXF1ZXN0NjIyNzk2MTA4 | 41,152 | TYP: Overload series/ffill and bfill | {
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} | 0 | "2021-04-25T16:30:23Z" | "2021-04-26T17:28:44Z" | "2021-04-26T17:28:44Z" | CONTRIBUTOR | null | - Closes part of MarcoGorelli/pyladieslondon-sprints#29
- Adds `typing.overload` to: `ffill` and ``bfill` from https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py
## Setup and Output for `ffill`
```python
# t_ffill.py
import pandas as pd
inplace: bool
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=False))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=True))
reveal_type(pd.DataFrame([1, None, None]).ffill())
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=inplace))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=False))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=True))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=inplace))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=False, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=True, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=inplace, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=False, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=True, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=inplace, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=False, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=True, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=inplace, limit=1))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=False, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=True, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=inplace, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=False, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=True, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(inplace=inplace, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=False, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=True, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).ffill(axis=0, inplace=inplace, limit=1, downcast="infer"))
```
which results in the following `mypy` output:
```bash
$ mypy t_ffill.py
t_ffill.py:6: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:7: note: Revealed type is 'None'
t_ffill.py:8: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:9: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:11: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:12: note: Revealed type is 'None'
t_ffill.py:13: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:14: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:16: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:17: note: Revealed type is 'None'
t_ffill.py:18: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:19: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:21: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:22: note: Revealed type is 'None'
t_ffill.py:23: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:24: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:26: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:27: note: Revealed type is 'None'
t_ffill.py:28: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:29: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:31: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:32: note: Revealed type is 'None'
t_ffill.py:33: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:34: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:36: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:37: note: Revealed type is 'None'
t_ffill.py:38: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:39: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:41: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:42: note: Revealed type is 'None'
t_ffill.py:43: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:44: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
```
## Setup and Ouput for `bfill`
```python
# t_bfill.py
import pandas as pd
inplace: bool
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=False))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=True))
reveal_type(pd.DataFrame([1, None, None]).bfill())
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=inplace))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=False))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=True))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=inplace))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=False, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=True, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=inplace, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=False, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=True, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=inplace, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=False, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=True, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=inplace, limit=1))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=False, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=True, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=inplace, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=False, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=True, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(inplace=inplace, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=False, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=True, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, limit=1, downcast="infer"))
reveal_type(pd.DataFrame([1, None, None]).bfill(axis=0, inplace=inplace, limit=1, downcast="infer"))
```
which results in the following `mypy` output:
```bash
$ mypy t_bfill.py
t_ffill.py:6: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:7: note: Revealed type is 'None'
t_ffill.py:8: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:9: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:11: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:12: note: Revealed type is 'None'
t_ffill.py:13: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:14: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:16: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:17: note: Revealed type is 'None'
t_ffill.py:18: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:19: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:21: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:22: note: Revealed type is 'None'
t_ffill.py:23: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:24: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:26: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:27: note: Revealed type is 'None'
t_ffill.py:28: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:29: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:31: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:32: note: Revealed type is 'None'
t_ffill.py:33: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:34: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:36: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:37: note: Revealed type is 'None'
t_ffill.py:38: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:39: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
t_ffill.py:41: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:42: note: Revealed type is 'None'
t_ffill.py:43: note: Revealed type is 'pandas.core.frame.DataFrame*'
t_ffill.py:44: note: Revealed type is 'Union[pandas.core.frame.DataFrame*, None]'
``` | {
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} | 33 | "2021-04-25T20:48:37Z" | "2021-08-07T18:42:44Z" | "2021-08-05T22:01:59Z" | CONTRIBUTOR | null | - [x] closes #16404
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This PR presents a unified index type called `NumericIndex` for numerical indexing, supporting int64/32/16/8, uint64/32/16/8 and float64/32/16 numpy dtypes. This means that indexes with other numeric numpy dtypes than int64, uint64 and float64 will become possible. This index type is therefore functionally a super type of `Int64Index`, `UInt64Index` and `Float64Index` and is intended to eventually replace those.
Comments very welcome, of course, as this is probably quite a fundamental change to Pandas. @pandas-dev/pandas-core.
## Status
Missing is especially:
* docs and better doc string
* marking `NumericIndex` as experimental in 1.3?
## Deprecation cycle
ThIs PR is intended to be fully backwards compatible.
This means that public operations that currently return e.g. a `Int64Index`, will continue working unchanged. My view is that `NumericIndex` should replace `Int64Index`, `UInt64Index` and `Float64Index` in Pandas 2.0 and those index types should therefore be removed by 2.0. They should therefore get a deprecation warning in timely manner, before 2.0.
Internally there are some use of especially `Int64Index`, for example in groupby and join ops. That could be replaced with `NumricIndex` straight away, giving a performance benefit in cases where casting to higher dtypes can be avoided.
## Outside of scope of this PR
I do not intend to add support for nullable numeric dtypes in this PR. This is partly for technical reasons (there isn't any indexing engines for those types currently) but also to limit the scope of this PR. I'm not opposed to add support for those dtypes later, but I think adding them here would be too much for this PR. Also, it might be more sensible to have extension types in a different index class than numpy types, so probably better to look into that seperately.
Actual deprecation of `Int64Index`, `UInt64Index` and `Float64Index` should also be in a later PR IMO. If Pandas 2.0 is far in the future, it may be better holding off deprecating them until we see a v2.0 in the horizon instead of having a very long deprecation cycle for a relatively often used part of Pandas.
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} | 6 | "2021-04-25T21:09:02Z" | "2021-04-30T12:11:19Z" | "2021-04-30T00:33:12Z" | CONTRIBUTOR | null | - [x] closes #22035
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 2 | "2021-04-25T21:39:17Z" | "2021-04-26T12:06:02Z" | "2021-04-26T12:03:04Z" | MEMBER | null | - [x] closes #41151
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
This was easier than I've expected
cc @attack68 | {
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} | 1 | "2021-04-26T02:47:41Z" | "2021-09-04T14:10:10Z" | "2021-09-04T14:09:45Z" | MEMBER | null | #### Is your feature request related to a problem?
Running ASV's takes a really long time, especially with old computers, and the ASVs in PRs are not run in continuous mode, which makes it hard to find regressions before a PR is merged.
https://github.com/pandas-dev/pandas/blob/accf345fdd4fb92056a5e62d4105fc8f4491ff2c/.github/workflows/ci.yml#L78
(master, what we run currently, asv dev)
#### Describe the solution you'd like
A github actions workflow that runs ```asv continuous -f 1.1 upstream/master HEAD``` on PRs after seeing a comment such as ```@github-actions asv```. In order to prevent false positives, I think the bot should first merge in master to a PR before running benchmarks.
https://github.com/pandas-dev/pandas/blob/accf345fdd4fb92056a5e62d4105fc8f4491ff2c/.github/workflows/comment_bot.yml#L10-L12
(example for triggering)
Afterwards, the bot should comment the results. (I'm not sure how to do this)
I don't think it should be a check, since it takes a while to run and might be flaky. Making it a check would run it on every commit in a PR which would make CI queue times longer for everyone.
#### Additional context
#36860 https://pandas.pydata.org/docs/development/roadmap.html#performance-monitoring | {
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} | 1 | "2021-04-26T06:34:02Z" | "2021-07-05T11:05:36Z" | "2021-05-07T12:58:54Z" | CONTRIBUTOR | null | For showing and hiding columns/rows `Styler` currently has two methods:
- `.hide_columns(subset)`: which hides a selection of columns and the associated data values.
- `.hide_index()`: which hides the index keys completely and displays all the data values.
This PR adds a `.hide_values(subset, axis, show)` method, which is a superset of `.hide_columns`. It allows the same functionality to operate row-wise, and adds the kwarg `show` which allows an inverse method, exclusively showing instead of hiding.
### Follow-on:
#### Option 1
Keep `.hide_values(subset, axis, show)` as is and **deprecate** `.hide_columns`.
#### Option 2
Make `._hide_values(subset, axis, show)` a private module and include/add the public methods:
- `hide_columns` = `_hide_values(subset=subset, axis="columns", show=False)`.
- `hide_rows` = `_hide_values(subset=subset, axis="index", show=False)`.
- `show_columns` = `_hide_values(subset=subset, axis="columns", show=True)`.
- `show_rows` = `_hide_values(subset=subset, axis="index", show=True)`.
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} | 7 | "2021-04-26T07:16:41Z" | "2021-04-28T19:16:59Z" | "2021-04-28T15:18:04Z" | CONTRIBUTOR | null | - [x] closes #40921
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 4 | "2021-04-26T07:17:12Z" | "2021-05-25T20:09:30Z" | "2021-05-25T20:09:27Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
⬆️ UPGRADE: Autoupdate pre-commit config (#41160)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.5-py2.py3-none-any.whl (98 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Using cached virtualenv-20.4.6-py2.py3-none-any.whl (7.2 MB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.1 filelock-3.0.12 identify-2.2.5 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.6
```
### stderr:
```Shell
WARNING: You are using pip version 21.1.1; however, version 21.1.2 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.5/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... updating 20.8b1 -> 21.5b1.
Updating https://github.com/codespell-project/codespell ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... updating v3.4.0 -> v4.0.1.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
updating f7061b1 -> 1.5.5.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.12.0 -> v2.18.1.
Updating https://github.com/pre-commit/pygrep-hooks ... already up to date.
Updating https://github.com/asottile/yesqa ... updating v1.2.2 -> v1.2.3.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Failed
- hook id: black
- files were modified by this hook
reformatted pandas/_config/config.py
reformatted pandas/_libs/arrays.pyi
reformatted pandas/_libs/algos.pyi
reformatted pandas/_libs/groupby.pyi
reformatted pandas/_libs/index.pyi
reformatted pandas/_libs/internals.pyi
reformatted pandas/_libs/hashtable.pyi
reformatted pandas/_libs/join.pyi
reformatted pandas/_libs/ops.pyi
reformatted pandas/_libs/parsers.pyi
reformatted pandas/_libs/reshape.pyi
reformatted pandas/_libs/testing.pyi
reformatted pandas/_libs/tslib.pyi
reformatted pandas/_libs/tslibs/ccalendar.pyi
reformatted pandas/_libs/tslibs/conversion.pyi
reformatted pandas/_libs/lib.pyi
reformatted pandas/_libs/tslibs/dtypes.pyi
reformatted pandas/_libs/tslibs/fields.pyi
reformatted pandas/_libs/tslibs/parsing.pyi
reformatted pandas/_libs/tslibs/nattype.pyi
reformatted pandas/_libs/tslibs/strptime.pyi
reformatted pandas/_libs/tslibs/timedeltas.pyi
reformatted pandas/_libs/tslibs/period.pyi
reformatted pandas/_libs/tslibs/timezones.pyi
reformatted pandas/_libs/tslibs/tzconversion.pyi
reformatted pandas/_libs/tslibs/vectorized.pyi
reformatted pandas/_libs/tslibs/timestamps.pyi
reformatted pandas/_libs/window/indexers.pyi
reformatted pandas/_libs/window/aggregations.pyi
reformatted pandas/_libs/writers.pyi
reformatted pandas/_testing/__init__.py
reformatted pandas/conftest.py
reformatted pandas/core/algorithms.py
reformatted pandas/core/apply.py
reformatted pandas/core/arrays/categorical.py
reformatted pandas/core/arrays/masked.py
reformatted pandas/core/arrays/sparse/scipy_sparse.py
reformatted pandas/core/computation/expressions.py
reformatted pandas/core/computation/expr.py
reformatted pandas/core/computation/pytables.py
reformatted pandas/core/dtypes/common.py
reformatted pandas/core/dtypes/dtypes.py
reformatted pandas/core/dtypes/cast.py
reformatted pandas/core/groupby/ops.py
reformatted pandas/core/generic.py
reformatted pandas/core/indexes/category.py
reformatted pandas/core/indexes/range.py
reformatted pandas/core/indexes/multi.py
reformatted pandas/core/internals/array_manager.py
reformatted pandas/core/internals/blocks.py
reformatted pandas/core/internals/managers.py
reformatted pandas/core/nanops.py
reformatted pandas/core/ops/__init__.py
reformatted pandas/core/reshape/tile.py
reformatted pandas/core/reshape/merge.py
reformatted pandas/io/formats/format.py
reformatted pandas/io/parquet.py
reformatted pandas/io/parsers/readers.py
reformatted pandas/io/sas/sas_xport.py
reformatted pandas/io/sql.py
reformatted pandas/io/pytables.py
reformatted pandas/io/stata.py
reformatted pandas/tests/arithmetic/test_period.py
reformatted pandas/tests/base/test_constructors.py
reformatted pandas/tests/dtypes/test_common.py
reformatted pandas/tests/extension/base/reduce.py
reformatted pandas/tests/frame/methods/test_clip.py
reformatted pandas/tests/generic/test_generic.py
reformatted pandas/tests/generic/test_label_or_level_utils.py
reformatted pandas/tests/indexing/common.py
reformatted pandas/tests/indexing/test_coercion.py
reformatted pandas/tests/indexing/test_indexing.py
reformatted pandas/tests/internals/test_internals.py
reformatted pandas/tests/io/generate_legacy_storage_files.py
reformatted pandas/tests/io/pytables/common.py
reformatted pandas/tests/io/pytables/conftest.py
reformatted pandas/tests/plotting/frame/test_frame.py
reformatted pandas/tests/plotting/test_misc.py
reformatted pandas/tests/resample/test_base.py
reformatted pandas/tests/resample/test_deprecated.py
reformatted pandas/tests/reshape/merge/test_merge_asof.py
reformatted pandas/tests/series/methods/test_clip.py
reformatted pandas/tests/test_multilevel.py
All done! ✨ 🍰 ✨
83 files reformatted, 1290 files left unchanged.
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Failed
- hook id: pyupgrade
- exit code: 1
- files were modified by this hook
Rewriting doc/sphinxext/announce.py
Rewriting pandas/io/pytables.py
Rewriting pandas/tests/io/parser/test_c_parser_only.py
Rewriting pandas/tests/generic/test_series.py
Rewriting pandas/util/_decorators.py
Rewriting pandas/core/reshape/merge.py
Rewriting pandas/_config/config.py
Rewriting pandas/tests/io/parser/test_multi_thread.py
Rewriting pandas/core/reshape/tile.py
Rewriting pandas/io/formats/latex.py
Rewriting pandas/io/html.py
Rewriting pandas/tests/io/parser/test_skiprows.py
Rewriting pandas/tests/generic/test_frame.py
Rewriting pandas/tests/reshape/test_util.py
Rewriting pandas/io/formats/excel.py
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed 95 files: </summary>
- .pre-commit-config.yaml
- doc/sphinxext/announce.py
- pandas/_config/config.py
- pandas/_libs/algos.pyi
- pandas/_libs/arrays.pyi
- pandas/_libs/groupby.pyi
- pandas/_libs/hashtable.pyi
- pandas/_libs/index.pyi
- pandas/_libs/internals.pyi
- pandas/_libs/join.pyi
- pandas/_libs/lib.pyi
- pandas/_libs/ops.pyi
- pandas/_libs/parsers.pyi
- pandas/_libs/reshape.pyi
- pandas/_libs/testing.pyi
- pandas/_libs/tslib.pyi
- pandas/_libs/tslibs/ccalendar.pyi
- pandas/_libs/tslibs/conversion.pyi
- pandas/_libs/tslibs/dtypes.pyi
- pandas/_libs/tslibs/fields.pyi
- pandas/_libs/tslibs/nattype.pyi
- pandas/_libs/tslibs/parsing.pyi
- pandas/_libs/tslibs/period.pyi
- pandas/_libs/tslibs/strptime.pyi
- pandas/_libs/tslibs/timedeltas.pyi
- pandas/_libs/tslibs/timestamps.pyi
- pandas/_libs/tslibs/timezones.pyi
- pandas/_libs/tslibs/tzconversion.pyi
- pandas/_libs/tslibs/vectorized.pyi
- pandas/_libs/window/aggregations.pyi
- pandas/_libs/window/indexers.pyi
- pandas/_libs/writers.pyi
- pandas/_testing/__init__.py
- pandas/conftest.py
- pandas/core/algorithms.py
- pandas/core/apply.py
- pandas/core/arrays/categorical.py
- pandas/core/arrays/masked.py
- pandas/core/arrays/sparse/scipy_sparse.py
- pandas/core/computation/expr.py
- pandas/core/computation/expressions.py
- pandas/core/computation/pytables.py
- pandas/core/dtypes/cast.py
- pandas/core/dtypes/common.py
- pandas/core/dtypes/dtypes.py
- pandas/core/generic.py
- pandas/core/groupby/ops.py
- pandas/core/indexes/category.py
- pandas/core/indexes/multi.py
- pandas/core/indexes/range.py
- pandas/core/internals/array_manager.py
- pandas/core/internals/blocks.py
- pandas/core/internals/managers.py
- pandas/core/nanops.py
- pandas/core/ops/__init__.py
- pandas/core/reshape/merge.py
- pandas/core/reshape/tile.py
- pandas/io/formats/excel.py
- pandas/io/formats/format.py
- pandas/io/formats/latex.py
- pandas/io/html.py
- pandas/io/parquet.py
- pandas/io/parsers/readers.py
- pandas/io/pytables.py
- pandas/io/sas/sas_xport.py
- pandas/io/sql.py
- pandas/io/stata.py
- pandas/tests/arithmetic/test_period.py
- pandas/tests/base/test_constructors.py
- pandas/tests/dtypes/test_common.py
- pandas/tests/extension/base/reduce.py
- pandas/tests/frame/methods/test_clip.py
- pandas/tests/generic/test_frame.py
- pandas/tests/generic/test_generic.py
- pandas/tests/generic/test_label_or_level_utils.py
- pandas/tests/generic/test_series.py
- pandas/tests/indexing/common.py
- pandas/tests/indexing/test_coercion.py
- pandas/tests/indexing/test_indexing.py
- pandas/tests/internals/test_internals.py
- pandas/tests/io/generate_legacy_storage_files.py
- pandas/tests/io/parser/test_c_parser_only.py
- pandas/tests/io/parser/test_multi_thread.py
- pandas/tests/io/parser/test_skiprows.py
- pandas/tests/io/pytables/common.py
- pandas/tests/io/pytables/conftest.py
- pandas/tests/plotting/frame/test_frame.py
- pandas/tests/plotting/test_misc.py
- pandas/tests/resample/test_base.py
- pandas/tests/resample/test_deprecated.py
- pandas/tests/reshape/merge/test_merge_asof.py
- pandas/tests/reshape/test_util.py
- pandas/tests/series/methods/test_clip.py
- pandas/tests/test_multilevel.py
- pandas/util/_decorators.py
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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} | 1 | "2021-04-26T08:10:52Z" | "2021-04-28T10:22:30Z" | "2021-04-28T10:22:27Z" | MEMBER | null | The current `_bool_arith_check` helper function in `expressions.py` does 2 things: 1) check for operations we do not allow in pandas for boolean data (division, power) and 2) check for ops that are not supported by numexpr but have a pure python fallback.
That first aspect is not related to numexpr, though: we always check this (and raise an error if appropriate), whether numexpr is used or not.
So this PR does:
- Move the check for disallowed bool ops out of the numexpr code to `array_ops.py`. This is a cleaner separation of concerns, and ensures that the check is done consistently (eg if not calling floordiv through numexpr (https://github.com/pandas-dev/pandas/pull/40727), or if in the future not calling expressions.py if we know we can't use numexpr, such as in https://github.com/pandas-dev/pandas/pull/41122)
- In `expressions.py`, move the remaining check for unsupported ops (by numexpr) inside an except block: this way, we don't check up-front (for all ops, for all types), and only do the types check if there was actually a NotImplementedError. That avoids paying the overhead costs for all other ops. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41162 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41162/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41162/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41162/events | https://github.com/pandas-dev/pandas/pull/41162 | 867,411,848 | MDExOlB1bGxSZXF1ZXN0NjIzMDg2OTcw | 41,162 | BUG: pandas concat does not match index names when concatenating two dataframes with a multiindex | {
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] | closed | false | null | [] | null | 11 | "2021-04-26T08:18:46Z" | "2021-08-17T00:48:50Z" | "2021-08-17T00:48:50Z" | CONTRIBUTOR | null | - [x] closes #40849
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 3 | "2021-04-26T08:25:05Z" | "2021-04-28T12:56:13Z" | "2021-04-28T12:35:50Z" | MEMBER | null | Re-take of https://github.com/pandas-dev/pandas/pull/40727, see that PR for explanation.
Tests will still be failing (so don't merge yet ;)), but depend on #41161 to be fixed | {
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} | 2 | "2021-04-26T10:01:37Z" | "2021-04-26T11:53:42Z" | "2021-04-26T11:48:01Z" | MEMBER | null | Some more JSON skips that I missed in https://github.com/pandas-dev/pandas/pull/41081 can now be removed
xref https://github.com/pandas-dev/pandas/issues/39146 | {
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```python
>>> arr = np.array([True, False, True])
>>> arr / True
array([1., 0., 1.])
>>> pd.Series(arr) / True
...
NotImplementedError: operator '/' not implemented for bool dtypes
```
This is done for the division and power operations (`not_allowed={"/", "//", "**"}`):
https://github.com/pandas-dev/pandas/blob/934cad6ab61b867c6ae54941c5cd87340d44b80a/pandas/core/computation/expressions.py#L215-L218
For the nullable BooleanArray, for now we simply relied on the operations as defined by the underlying numpy bool array:
```python
>>> pd.array(arr) / True
<FloatingArray>
[1.0, 0.0, 1.0]
Length: 3, dtype: Float64
```
That's for the `BooleanArray`, but the check is currently done on the "array_op" level (but because it is done within expressions.py, we don't run that check for EAs, xref https://github.com/pandas-dev/pandas/pull/41161).
So questions:
- Do we actually want to continue disallowing this operation, while numpy allows it? (also, eg `pd.Series(arr) / 1` does work, it's only disallowed if both operands are boolean)
- If we keep disallowing it, we should probably also disallow it on the `BooleanArray` level, and not only check it on the DataFrame/Series ops level in `array_ops.py` ?
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} | 1 | "2021-04-26T18:26:31Z" | "2021-05-06T01:41:26Z" | "2021-05-06T01:41:26Z" | 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
pd.read_excel('trailing_blanks.xlsx').shape
```
Test files available [here](https://github.com/ahawryluk/pandas/tree/trailing_blanks).
I loaded this sample spreadsheet in all five filetypes to see what shape is returned:
filetype | pandas 1.2.x | master
-|-|-
xls | (3, 3) | (3, 3)
xlsx | (11, 6) | (3, 6)
xlsm | (11, 6) | (3, 6)
xlsb | (11, 6) | (11, 6)
ods | (3, 3) | (3, 3)
#### Expected Output
`(3, 3)`
#### Problem description
Spreadsheet files can contain cells with no values, such as the formatted blank cells shown below. In the worst cases, the cell used to contain a value or formatting but currently contains neither and is thus invisible to the user of the spreadsheet application. The trailing rows of NaNs in xls[x|m] files were recently resolved in #39547 by @rhshadrach but the additional columns still remain. In a case of the size pictured here, it's a minor annoyance, but I've come across spreadsheets in the wild where one more cells were unintentionally created on row `2**20` or column `2**14`, which can create a severe performance issues. To fix the performance issue, the empty rows/columns must be trimmed before the data is passed to TextParser.
I suspect that these bug reports are related:
#40569
#40976
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 1e912821c03f08784cd12c046999c2033d94dd85
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56-Ubuntu SMP Mon Apr 12 17:18:36 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.3.0.dev0+1412.g1e912821c0
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.2
hypothesis : 6.1.1
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : 0.7.1
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : 1.0.8
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
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} | 0 | "2021-04-26T20:08:23Z" | "2021-04-30T20:36:56Z" | "2021-04-30T20:36:56Z" | MEMBER | null | Caused by https://github.com/pandas-dev/pandas/pull/41082, the following now fails:
```
In [1]: dtidx = pd.date_range("2012", periods=3, tz="UTC")
In [2]: pd.core.internals.make_block(dtidx, placement=[0])
...
ValueError: Wrong number of dimensions. values.ndim != ndim [1 != 2]
```
Even though the Block is now stored as 2D values, in our internal "public" API, we should still accept 1D values for datetimetz.
From the dask tests, https://github.com/dask/dask/issues/7607
cc @jbrockmendel | {
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} | 4 | "2021-04-26T20:23:12Z" | "2021-05-03T11:40:47Z" | "2021-04-29T23:34:46Z" | MEMBER | null | - [x] closes #33765
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"A": [12,23,34,45]}, index = [list("aabb"), [0,1,2,3]])
print(df)
print("- - -")
print(df.loc[df.A < 30, :].loc[["b"], :]) # empty as expected
print("- - -")
print(df.loc[df.A < 10, :].loc[["b"], :]) # raises ValueError
```
#### Complete Output of Code Sample
<details>
```python-traceback
A
a 0 12
1 23
b 2 34
3 45
- - -
Empty DataFrame
Columns: [A]
Index: []
- - -
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 889, in __getitem__
return self._getitem_tuple(key)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1060, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 791, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 865, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1113, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1053, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1254, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key)
File "C:\test\venv124\lib\site-packages\pandas\core\indexes\multi.py", line 2559, in _convert_listlike_indexer
_, indexer = self.reindex(keyarr, level=level)
File "C:\test\venv124\lib\site-packages\pandas\core\indexes\multi.py", line 2470, in reindex
target, indexer, _ = self._join_level(
File "C:\test\venv124\lib\site-packages\pandas\core\indexes\base.py", line 3924, in _join_level
ngroups = 1 + new_lev_codes.max()
File "C:\test\venv124\lib\site-packages\numpy\core\_methods.py", line 39, in _amax
return umr_maximum(a, axis, None, out, keepdims, initial, where)
ValueError: zero-size array to reduction operation maximum which has no identity
```
</details>
#### Problem description
In both cases the ``df.loc[df.A...]`` returns a dataframe that doesn't contain any rows with an index value of "b".
Accordingly in the first case the result of ``.loc[["b"], :]`` is an empty dataframe, but in the second case a ValueError is raised. The difference between the cases is that in the first case ``df.loc[df.A...]`` returns a dataframe with some rows (though none with index value "b"), while in the second case ``df.loc[df.A...]`` returns a dataframe with zero rows.
I think that shouldn't make a difference.
In the original code ``.loc[df.A...]`` and ``.loc[["b"], :]`` are not directly combined in one expression, but the first one creates a selection of rows of the dataframe, this selection is processed further, and during this another expression uses the second ``.loc``.
The traceback looks very similar to the one in #40235. Maybe both bugs have a common root cause.
#### Expected Output
``df.loc[df.A < 10, :].loc[["b"], :]`` returns an empty dataframe like ``df.loc[df.A < 30, :].loc[["b"], :]`` does.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
...
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
...
</details>
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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.
---
**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
dates_df = pd.DataFrame(pd.Series(name='col_name', dtype='object'), index=pd.DatetimeIndex([], name='time', freq=None))
dates_df.index = dates_df.index.tz_localize('UTC')
weights_time = pd.date_range(start='20200101', end='20200110')
weights_time = weights_time.tz_localize('UTC')
weights_df = pd.DataFrame(index=weights_time)
weights_df['col_name'] = 'model1'
weights_df['attr'] = 1.5
joined_df = pd.merge_asof(
dates_df,
weights_df,
by="col_name",
left_index=True,
right_index=True,
direction="backward",
)
```
#### Problem description
The two input dataframes (dates_df and weights_df) have a DatetimeIndex, but the resulting joined_df does not.
#### Expected Output
I would expect the resulting index to be a DatetimeIndex and have type 'datetime64[ns, UTC]'
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.67-ts1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.16.6
pytz : 2020.4
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.29.21
pytest : 5.3.1
hypothesis : 6.0.3
sphinx : 3.4.3
blosc : 1.5.1
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.5.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.2.1
fastparquet : 0.4.1
gcsfs : 0.6.1
lxml.etree : 4.5.2
matplotlib : 3.0.3
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pytest : 5.3.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.3
xarray : 0.16.2
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
numba : 0.53.0
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 5 | "2021-04-27T00:05:40Z" | "2021-08-30T07:52:26Z" | "2021-08-30T07:52:26Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [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
s = pd.Series({0: 0+2j, 1: 3+4j, 2: 5+6j})
s.to_json()
```
#### Problem description
Calling `to_json()` on a `DataFrame` or `Series` with `complex` data gives unexpected results. Running the above Code Sample gives the following output:
```
'{"0":{"imag":2.0},"1":{"imag":4.0},"2":{"imag":6.0}}'
```
This is a problem both because (1) the output is not terribly useful (the real part is just dropped), and (2) according to [the docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#fallback-behavior) this should raise an error (see the following snippet copied from the linked docs page):
```python
>>> DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json() # raises
RuntimeError: Unhandled numpy dtype 15
```
#### Expected Output
According to the docs, this should at least raise a `RuntimeError`. However, it would be nice for this to actually be supported.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 0158382ac8459a197d088790fd45f433c83c25c8
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1441.g0158382ac8
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 7 | "2021-04-27T04:08:55Z" | "2021-05-26T17:03:09Z" | "2021-05-26T11:57:43Z" | CONTRIBUTOR | null | - [x] closes #41127
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I added tm.assert_produces_warning for every existing test that uses the `convert_float` argument, and a git grep only found one reference to `convert_float` in doc/. Is anything else required for a deprecation? IIUC, the actual feature would get removed in a pandas 2.0, correct? | {
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} | 0 | "2021-04-27T14:34:14Z" | "2021-04-30T19:52:06Z" | "2021-04-30T18:51:13Z" | CONTRIBUTOR | null | clean-up of typing issues. | {
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} | 2 | "2021-04-27T14:57:36Z" | "2021-05-07T05:00:58Z" | "2021-05-07T05:00:26Z" | CONTRIBUTOR | null | #### https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nunique.html
#### Documentation problem
The above documentation provides an example using `pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})` upon which `df.nunique()` returns
```
A 3
B 1
```
This example might be a bit misleading as there are `3` and `1` present in dataframe as column elements and cause confusion.
#### Suggested fix for documentation
I would like to suggest using something like `pd.DataFrame({'A': [5, 6, 7], 'B': [5, 1, 1]})` upon which `df.nunique()` returns
```
A 3
B 2
```
as this examples serves the purpose of `nunique` better as its clear that it counts the number of unique elements in column and does not return anything resembling any column element.
Furthermore, I would like to extend the example dataframe to `df.nunique(axis=1)` and match corresponding values with new dataframe as :
```
df.nunique(axis=1)
0 1
1 2
2 2
dtype: int64
```
Does this change need to be documented in `whatsnew`? | {
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- [] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [] whatsnew entry
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} | 1 | "2021-04-27T15:31:20Z" | "2021-05-07T12:31:37Z" | "2021-05-07T05:00:27Z" | CONTRIBUTOR | null | - [x] closes #41180
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 13 | "2021-04-27T16:46:30Z" | "2021-09-03T00:27:15Z" | "2021-08-20T02:39:01Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
def get_data():
# request data from the server
return data
datas = get_data()
#datas consist of 2195 rows and 22 column
df = pd.DataFrame(datas)
total = df.groupby('userid').max()['point'].sum()
print(f"sum all point: {total}")
Traceback (most recent call last):
File "playground-test.py", line 15, in <module>
total = df.groupby('userid').max()['point'].sum()
File "/home/playground/.local/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 1560, in max
numeric_only=numeric_only, min_count=min_count, alias="max", npfunc=np.max
File "/home/playground/.local/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 1001, in _agg_general
how=alias, alt=npfunc, numeric_only=numeric_only, min_count=min_count,
File "/home/playground/.local/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 1022, in _cython_agg_general
how, alt=alt, numeric_only=numeric_only, min_count=min_count
File "/home/playground/.local/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 1135, in _cython_agg_blocks
assert len(locs) == result.shape[1]
AssertionError
```
```python
import pandas as pd
def get_data():
# request data from the server
return data
datas = get_data()
#let's copy dataframe and only include 2 column
df_copy = df[['userid','point']].copy()
total = df_copy.groupby('userid').max()['point'].sum()
print(f"sum all point: {total}")
64511636
```
#### Problem description
`groupby` `max` function on dataframe that consist of 22 column does make error with AssertionError. However, if I tried to made smaller dataframe with 2 column. It does work.
I am on Pandas 1.1.5 and I dont know why, I can't upgrade to 1.2.4
```linux
$pip3 install pandas --upgrade
Requirement already satisfied: pandas in /home/playground/.local/lib/python3.6/site-packages (1.1.5)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/site-packages (from pandas) (2020.1)
Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib64/python3.6/site-packages (from pandas) (1.19.5)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/site-packages (from pandas) (2.8.1)
Requirement already satisfied: six>=1.5 in /home/playground/.local/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)
```
```linux
$$ pip3 install pandas==1.2.4
ERROR: Could not find a version that satisfies the requirement pandas==1.2.4
ERROR: No matching distribution found for pandas==1.2.4
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.24.1.el7.x86_64
Version : #1 SMP Thu Apr 8 19:51:47 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.utf-8≈
LANG : en_US.utf-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.4.7
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 22 | "2021-04-27T19:10:01Z" | "2021-08-22T14:00:45Z" | "2021-08-22T14:00:45Z" | CONTRIBUTOR | null | - [ ] Closes part of MarcoGorelli/pyladieslondon-sprints#29
- [ ] Adds typing.overload to: `concat` from https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/concat.py
## `mypy` Setup and Sutput
```python
# t.py
import pandas as pd
from pandas._typing import Axis
from pandas.core.generic import NDFrame
axis: Axis
ndf1: NDFrame
ndf2: NDFrame
s1 = pd.Series
s2 = pd.Series
df1 = pd.DataFrame
df2 = pd.DataFrame
reveal_type(pd.concat([s1, s2]))
reveal_type(pd.concat([s1, s2], axis=0))
reveal_type(pd.concat([s1, s2], axis=1))
reveal_type(pd.concat([s1, s2], axis="index"))
reveal_type(pd.concat([s1, s2], axis="columns"))
reveal_type(pd.concat([df1, df2]))
reveal_type(pd.concat([df1, df2], axis=0))
reveal_type(pd.concat([df1, df2], axis=1))
reveal_type(pd.concat([df1, df2], axis="index"))
reveal_type(pd.concat([df1, df2], axis="columns"))
reveal_type(pd.concat([ndf1, ndf2]))
reveal_type(pd.concat([ndf1, ndf2], axis=0))
reveal_type(pd.concat([ndf1, ndf2], axis=1))
reveal_type(pd.concat([ndf1, ndf2], axis="index"))
reveal_type(pd.concat([ndf1, ndf2], axis="columns"))
```
which results in the following mypy output:
```bash
$ mypy t.py
t.py:16: note: Revealed type is 'pandas.core.series.Series'
t.py:17: note: Revealed type is 'pandas.core.series.Series'
t.py:18: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:19: note: Revealed type is 'pandas.core.series.Series'
t.py:20: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:22: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:23: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:24: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:25: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:26: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:28: note: Revealed type is 'Union[pandas.core.frame.DataFrame, pandas.core.series.Series]'
t.py:29: note: Revealed type is 'Union[pandas.core.frame.DataFrame, pandas.core.series.Series]'
t.py:30: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:31: note: Revealed type is 'Union[pandas.core.frame.DataFrame, pandas.core.series.Series]'
t.py:32: note: Revealed type is 'pandas.core.frame.DataFrame'
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} | 5 | "2021-04-27T19:28:32Z" | "2021-05-02T23:50:22Z" | "2021-05-02T23:50:11Z" | CONTRIBUTOR | null | - [x] xref #37643
- [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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] | open | false | null | [] | null | 0 | "2021-04-27T22:35:23Z" | "2021-08-20T02:39:14Z" | null | NONE | null | #### Histogram through pandas.DataFrame.hist
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'length': np.random.normal(0, 1, 1000),
'width': np.random.normal(0, 1, 1000),
'a': np.random.randint(0, 2, 1000)})
df.hist(column=['length', 'width'], by='a', figsize=(4, 2))
```
#### Problem description
by-column `a` plotted along specified columns `length` and `width` ([link](https://i.stack.imgur.com/khZfd.png)). The full problem is described on this stackoverflow [post](https://stackoverflow.com/questions/67289674/remove-by-column-in-pandas-dataframe-hist).
#### Expected Output
Problem vanishes when by-column is non-numeric ([link](https://i.stack.imgur.com/047eQ.png)).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-142-generic
Version : #146~16.04.1-Ubuntu SMP Tue Apr 13 09:27:15 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.11
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 0 | "2021-04-28T01:10:51Z" | "2021-04-28T15:51:05Z" | "2021-04-28T15:19:43Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-04-28T06:49:48Z" | "2021-04-28T14:42:42Z" | "2021-04-28T14:42:42Z" | 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
# Your code here
pd.MultiIndex.from_tuples(("2018", "11"))
```
#### Problem description
I want to make Multiple index to append my dataframe.
So, I used those codes and get something different with my think
#### Expected Output
I think
MultiIndex([('2018', '11'),
)
but, I got
MultiIndex([('2', '0'),
('1', '1')],
)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.11.6.el7.x86_64
Version : #1 SMP Thu Jan 4 01:06:37 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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} | 1 | "2021-04-28T08:59:23Z" | "2021-04-30T19:52:23Z" | "2021-04-30T18:52:55Z" | CONTRIBUTOR | null | This PR ensures that all index instantiation calls in `tests.indexes.numeric.py` use the `_holder` class variable instead of the actual index class. This will make it easier to extend the existing tests to tests for the new `NumIndex` in #41153.
Also renames `_holder` to `_index_cls` for all of `tests.indexes`. I think this is a more descriptive name.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
I've labeled the error something silly but that can be fixed later.
This pull request replaces this older pr #41008
I've put all my tests inside one file for easier review.
### Issues
I'm unsure how to stop this test from failing `test_pivot_table_doctest_case` Could you help with this?
This test (`test_invalid_colormap`) also fails but when I rerun it individually, it passes ...
I also had an issue with flake8 and black not working together ... in my most recent commit so I just committed it with `--no-verify`. | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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- [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
# Outer merge on two "Int64" columns casts the joining column to "int64"
df1 = pd.DataFrame(dict(nr=[1,2])).astype("Int64")
df2 = pd.DataFrame(dict(nr=[2,3])).astype("Int64")
pd.merge(df1, df2, on="nr", how="outer").dtypes
# nr int64
# dtype: object
# Inner merge is OK
pd.merge(df1, df2, on="nr", how="inner").dtypes
# nr Int64
# dtype: object
# Left merge is OK
pd.merge(df1, df2, on="nr", how="left").dtypes
# nr Int64
# dtype: object
# Right merge also casts to "int64"
pd.merge(df1, df2, on="nr", how="right").dtypes
# nr int64
# dtype: object
```
#### Problem description
"Int64" columns lose their data type and are converted to "int64" when used as joining column in an outer or right merge.
This does not seem to happen for inner and left merges.
#### Expected Output
"Int64" columns should retain their dtype during merge operations.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : German_Switzerland.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 51.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.0b1
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Currently, according to Pandas documentation, dataframe.isna() returns a same-size dataframe object (or Series) containing boolean values for each element indicating whether that element is or is not a pd.NA. However, in pandas current implementation, there is no way to clearly check if a single value in a dataFrame (or Series) is NA. For example: If I want to iterate over a Series, and perform some operation to each element, but ignore any pd.NA elements, there is no intuitive way to check for that. Without running into errors such as "boolean value of NA is ambiguous".
#### Describe the solution you'd like
a pandas function which simply accepts any entity and returns whether or not it is a pd.NA object, or direct implementation for the == or is operator for pd.NA
#### Describe alternatives you've considered
I am aware that workarounds for this exist, such as using applyMap() or Map() with na_action='ignore', but that doesn't mean that this functionality I am proposing shouldn't also exist. The current dataframe.isna() function could be used to solve this issue as well, but I would have to find all indices for True in the returned dataframe, which is not an elegant solution, and is also computationally expensive.
```python
data = some_pandas_series
for i in range(len(data)):
print(data[i].isna())
##or some other function which would achieve the same results in this way
```
this should iterate through the Series and print True or False to the screen indicating whether or not each element is pd.NA | {
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} | 3 | "2021-04-28T16:45:59Z" | "2021-05-05T20:14:09Z" | "2021-05-05T20:14:09Z" | MEMBER | null | distutils is deprecated in Python 3.10, it will be removed in 3.12.
https://www.python.org/dev/peps/pep-0632/ has a section about migration.
xref #41113 | {
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} | 0 | "2021-04-28T19:10:16Z" | "2021-04-30T15:22:00Z" | "2021-04-30T14:53:48Z" | MEMBER | null | get the namespaces/privateness "right"
avoid runtime imports
fix "type: ignore"s | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41205/events | https://github.com/pandas-dev/pandas/pull/41205 | 870,263,875 | MDExOlB1bGxSZXF1ZXN0NjI1NDg3NTcw | 41,205 | DOC: Add Lux to Pandas Ecosystem page | {
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} | 2 | "2021-04-28T19:12:38Z" | "2021-05-01T18:11:00Z" | "2021-05-01T18:10:48Z" | CONTRIBUTOR | null | #### Location of the documentation
List of systems under [pandas ecosystem](https://pandas.pydata.org/docs/ecosystem.html#pandas-ecosystem)
#### Documentation problem
Add [Lux](https://github.com/lux-org/lux) to the ["pandas ecosystem" documentation](https://pandas.pydata.org/docs/ecosystem.html#pandas-ecosystem)
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https://api.github.com/repos/pandas-dev/pandas/issues/41206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41206/events | https://github.com/pandas-dev/pandas/issues/41206 | 870,313,596 | MDU6SXNzdWU4NzAzMTM1OTY= | 41,206 | QST: ERROR in Jupyter using pandas module: ImportError: cannot import name 'np_version_under1p17' from 'pandas.compat.numpy' (unknown location) | {
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-------
English:
Guys I was trying to learn a program with Jupyter, but when I installed pandas at
'pip install pandas' please worked, but I used (import pandas as pd) it on jupyter and I give the following error message:
```python
# ImportError Traceback (most recent call last)
<ipython-input-35-33fbe9d324d8> in <module>
----> 1 import pandas as pd
2
3 df = pd.read_excel(r'C:\Users\USER\Desktop\Vendas - Dez.xlsx')
4 df.corr()
~\anaconda3\lib\site-packages\pandas\__init__.py in <module>
20
21 # numpy compat
---> 22 from pandas.compat.numpy import (
23 np_version_under1p17 as _np_version_under1p17,
24 np_version_under1p18 as _np_version_under1p18,
ImportError: cannot import name 'np_version_under1p17' from 'pandas.compat.numpy' (unknown location)"
```
=============================================
Portuguese:
Cara eu estava tentando aprender a programar com o Jupyter, mas quando instalei o pandas pelo
'pip install pandas' pareceu dar certo, mas fui usar (import pandas as pd) no jupyter e da a seguinte mensagem:
```python
"ImportError Traceback (most recent call last)
<ipython-input-35-33fbe9d324d8> in <module>
----> 1 import pandas as pd
2
3 df = pd.read_excel(r'C:\Users\USER\Desktop\Vendas - Dez.xlsx')
4 df.corr()
~\anaconda3\lib\site-packages\pandas\__init__.py in <module>
20
21 # numpy compat
---> 22 from pandas.compat.numpy import (
23 np_version_under1p17 as _np_version_under1p17,
24 np_version_under1p18 as _np_version_under1p18,
ImportError: cannot import name 'np_version_under1p17' from 'pandas.compat.numpy' (unknown location)"
```
----------
HELP PLEASE GUYS!!!!! | {
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} | 12 | "2021-04-28T21:12:45Z" | "2021-06-18T02:25:50Z" | "2021-05-05T20:14:09Z" | MEMBER | null | - [x] close #41199
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} | 1 | "2021-04-28T22:11:47Z" | "2021-04-29T23:33:11Z" | "2021-04-29T23:33:11Z" | 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.
---
1. Create a DF in Pandas 1.1.0+
```python
import pandas as pd
dfContents = [{"foo": 1.1, "bar": True, "baz": "test1"}, {"foo":5.5, "bar": False, "baz": "test2"}]
df = pd.DataFrame(dfContents)
print(df.head())
df.to_pickle("bugReportSample.pkl", protocol= 4)
```
2. Load in a different computer/system/subsystem with a different Pandas version like 1.0.5
```python
import pandas as pd
df = pd.read_pickle("bugReportSample.pkl")
print(df.head())
```
#### Problem description
A core objects structure should be compatible across minor versions; which [via the semver standard](https://semver.org/#summary)
> Given a version number MAJOR.MINOR.PATCH, increment the:
>
> MAJOR version when you make incompatible API changes,
> ***MINOR version when you add functionality in a backwards compatible manner, and*** \[emphasis added\]
> PATCH version when you make backwards compatible bug fixes.
> Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.
#### Expected Output
A serialized dataframe should be compatible across versions.
- Serialization to basic objects should be backwards compatible, in such a way that all reasonable serializations should "just work" (eg, joblib, pickle, hdf5 ...)
- If it cannot be in all cases, native write functions like `DataFrame.to_pickle()` should handle the backwards-compatibility work, at least via flag.
- If it cannot be at all, the _major_ version should be incremented as per semver, since this breaks backwards compatibility.
- If the major version cannot be incremented, Pandas shouldn't pretend to follow semantic versioning and should use a version number that is plainly and obviously not semantic versioning, like Ubuntu's whimsical named versions.
#### Output of ``pd.show_versions()``
<details>
Writer system:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #488-Microsoft Mon Sep 01 13:43:00 PST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
>>>
```
Reader system:
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 47.1.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fastparquet : 0.4.0
gcsfs : None
lxml.etree : 4.6.1
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 3.0.0
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.50.1
```
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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I would like to resample a sorted time series with a sorted list of non-uniform, non-overlapping and not necessarily contiguous time intervals.
Taking benefit that...
* the time serie i sorted
* and and the non-uniform not necessarily contiguous time intervals are not overlapping,
...reading through the time serie needs to be done only once.
[Posting my question on SO](https://stackoverflow.com/questions/67250482/efficient-groupby-when-rows-of-groups-are-contiguous), with use of `groupby` just to show intended result, and asking if this could be done 'more efficiently' in pandas, I have been proposed to use Numba.
#### Describe the solution you'd like
I would propose to:
- re-use [`IntervalIndex`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.IntervalIndex.html) to define non-uniform, sorted and non overlapping intervals. This feature can conveniently be checked then with `is_non_overlapping_monotonic`
- have a new parameter `label` in `IntervalIndex` to define how should be named each interval (left or right bound, similar to `resample` API)
- have [`resample`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html) accepting `IntervalIndex`
- have the parameters `closed` and `label` forwarded from `IntervalIndex` to `resample`
Additionally, `resample` could also accept a single sequence of timestamps to define sorted non-uniform **contiguous** time intervals, similar to what [`cut`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html) can accept for its `bins` parameter.
#### API breaking implications
Use case example.
```python
import pandas as pd
# Time series.
length = 14
ts = pd.date_range(start='2021/01/01 08:00', periods=length, freq='30T')
tseries = pd.DataFrame({'val':range(length)}, index=ts)
# Custom disjoint intervals.
hours_start = ['7:46', '9:25', '9:35', '10:15', '10:55', '13:10']
hours_end = ['8:43', '9:32', '10:09', '10:42', '12:55', '13:31']
days = ['2021/01/01']*len(hours_start)
start = pd.DatetimeIndex(map(pd.Timestamp, map(' '.join, zip(days, hours_start))))
end = pd.DatetimeIndex(map(pd.Timestamp, map(' '.join, zip(days, hours_end))))
custom_intervals = pd.IntervalIndex.from_arrays(start, end, closed='left')
# New API here, with 'closed' and 'label' parameters forwarded from 'IntervalIndex' to 'resample'.
tseries['cumsum'] = tseries['val'].resample(custom_intervals).agg({'val': 'cumsum'})['val']
```
#### Describe alternatives you've considered
* using Numba of course
* using `rolling`. Am I right in thinking that `resample` is a convenience method calling `rolling` under the hood?
#### Additional context
I am assuming that `resample` already takes benefit that the time series is sorted according its DatetimeIndex. Am I right?
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] | closed | false | null | [] | null | 13 | "2021-04-29T08:21:35Z" | "2021-08-20T02:42:41Z" | "2021-08-20T02:42:41Z" | NONE | null | _Pandas frequently errors about importing NumPy. When I try to import pandas, it tries to import NumPy and creates an error.
I don't know why but when I delete venv folder and create a new venv and install pandas again, it automatically resolves but after 3-4 days, it throws an error about importing NumPy. There isn't a problem with the NumPy version because I don't manually install NumPy or any package with NumPy dependency before installing pandas. Anyway, I don't use another package using Numpy.
If you know what is the problem, please reply._
I opened this issue 1 week ago but it has closed because I didn't have that error at the time and don't have a full traceback.
I had the same error again and now I have the full traceback.
```
Traceback (most recent call last):
File "/pathtomyproject/main.py", line 22, in
import pandas as pd
File "/pathtomyproject/venv/lib/python3.9/site-packages/pandas/init.py", line 16, in
raise ImportError(
ImportError: Unable to import required dependencies:
numpy:
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
The Python version is: Python3.9 from "/pathtomyproject/venv/bin/python3"
The NumPy version is: "1.20.2"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: No module named 'numpy.core._multiarray_umath'
```
I didn't install any other packages or updated anything in that venv during last week. And as I mentioned above, I didn't use any library which is NumPy dependent. | {
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] | closed | false | null | [] | null | 2 | "2021-04-29T08:52:55Z" | "2021-04-29T12:52:55Z" | "2021-04-29T12:52:55Z" | NONE | null | [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html]
#### Documentation problem
[When setting 'key argument' in sort_index function (DataFrame object method), I cannot acces to index value because it's consider as an object. I haven't found the way of extracting Index' object value. I do not know if I'm wrong or a new Index method would be required for accesing that value]
#### Suggested fix for documentation
[Here I attached some code to let me explain:
df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['(0-1)', '(1-2)', '(2-3)', '(3-4)'])
df.sort_index(key=lambda x: int(x[1]))
Useful information: https://stackoverflow.com/questions/64160980/key-function-when-using-sort-index]
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] | open | false | null | [] | null | 1 | "2021-04-29T09:05:56Z" | "2021-04-29T13:54:46Z" | 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. (v1.2.4)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
There is a bug when calling Series.replace(List, ...), the Series gets converted to dtype 'object', which is incorrect.
Let's start with a simple example that works as expected.
```
df = pd.DataFrame({'a': ['abc', 'def']}, dtype="string")
s0 = df["a"].replace('abc', 'xyz')
print(s0)
```
The output is as expected, and Series has maintained the type __string__:
```
0 xyz
1 def
Name: a, dtype: string
```
But when calling Series.replace(List, ...), Series gets converted to dtype __object__, which is incorrect.
```
s1 = df["a"].replace(['abc', 'any other string'], "xyz" )
print(s1)
```
Output:
```
0 xyz
1 def
Name: a, dtype: object
```
#### Problem description
Datatype of Series should not change when using a different argument to Series.replace().
#### Expected Output
```
0 xyz
1 def
Name: a, dtype: string
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 21.0.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 3 | "2021-04-29T09:56:17Z" | "2021-05-01T11:46:25Z" | "2021-05-01T11:46:25Z" | 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
# Your code here
df1 = pd.DataFrame(np.arange(16).reshape(4,4),
index = [[1,1,2,2],['s1','s2','s1','s2']],
columns = [['apple','apple','banana','banana'], ['price','qty','price','qty']],
)
# first
df1["apple"]["price"] = [12, 16, 15, 18]
# second
df1["apple"]["price"] = ["a", "b", "c", "d"]
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
first code(change with numeric data)'s output is exactly same with what I think.
but when I used second one(change with string data), there's nothing change.
I know It can solve with code df1.loc[:, ("apple", "price")] = ["a", "b", "c", "d"], but I just wonder why second one is impossible.
#### Expected Output
apple banana
price qty price qty
1 s1 a 1 2 3
s2 16 b 6 7
2 s1 c 9 10 11
s2 18 d 14 15
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.11.6.el7.x86_64
Version : #1 SMP Thu Jan 4 01:06:37 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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} | 0 | "2021-04-29T11:41:24Z" | "2021-05-03T07:04:51Z" | "2021-05-02T23:53:32Z" | MEMBER | null | follow-up to #41025
```
[ 50.00%] ··· strings.Contains.time_contains ok
[ 50.00%] ··· ============== ========== ==========
-- regex
-------------- ---------------------
dtype True False
============== ========== ==========
str 22.0±0ms 15.0±0ms
string 17.2±0ms 10.9±0ms
arrow_string 6.84±0ms 2.37±0ms
============== ========== ==========
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41218/events | https://github.com/pandas-dev/pandas/pull/41218 | 871,163,192 | MDExOlB1bGxSZXF1ZXN0NjI2MjI3NDc4 | 41,218 | Optimising Series.nunique for Nan values #40865 | {
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} | [] | closed | false | null | [] | null | 0 | "2021-04-29T15:40:51Z" | "2021-04-29T16:01:12Z" | "2021-04-29T16:00:58Z" | CONTRIBUTOR | null | - [ ] closes #40865
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"due_on": "2021-06-30T07:00:00Z",
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"node_id": "MDk6TWlsZXN0b25lNjA5NTgxOA==",
"number": 80,
"open_issues": 1,
"state": "closed",
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/80"
} | 0 | "2021-04-29T18:10:46Z" | "2021-04-30T09:20:49Z" | "2021-04-29T22:10:13Z" | MEMBER | null | ```
[ 27.78%] ··· strings.Methods.time_lstrip ok
[ 27.78%] ··· ============== ==========
dtype
-------------- ----------
str 22.7±0ms
string 16.4±0ms
arrow_string 1.71±0ms
============== ==========
[ 38.89%] ··· strings.Methods.time_rstrip ok
[ 38.89%] ··· ============== ==========
dtype
-------------- ----------
str 19.7±0ms
string 13.9±0ms
arrow_string 1.71±0ms
============== ==========
[ 43.06%] ··· strings.Methods.time_strip ok
[ 43.06%] ··· ============== ==========
dtype
-------------- ----------
str 18.9±0ms
string 14.6±0ms
arrow_string 1.64±0ms
============== ==========
``` | {
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