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https://api.github.com/repos/pandas-dev/pandas/issues/29214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29214/events | https://github.com/pandas-dev/pandas/issues/29214 | 512,236,530 | MDU6SXNzdWU1MTIyMzY1MzA= | 29,214 | CI/TST: Better information about the tests in azure-pipelines | {
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] | open | false | null | [] | null | 4 | 2019-10-25T00:03:34Z | 2021-07-22T05:15:22Z | null | MEMBER | null | Copying from https://github.com/astropy/astropy/pull/8445#issuecomment-546140223
If you install the newest version of pytest-azurepipelines (1.0.0rc1) https://pypi.org/project/pytest-azurepipelines/1.0.0rc1/ I've switched the output from Junit to Nunit XML as its better supported in Azure Pipelines.
You can then run this Azure plugin https://marketplace.visualstudio.com/items?itemName=AnthonyShaw.vss-pytest and you get an extra tab in the UI with all the information from the Pytest run, including skip reasons, fail reasons, fixtures, versions, parameters.
I guess we want this. | {
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} | 6 | 2019-10-25T00:31:54Z | 2019-10-25T16:18:35Z | 2019-10-25T16:18:35Z | MEMBER | null | Not sure if anyone prefers to publish the new logo when we release pandas 1.0 and the new website is life. I don't have a strong preference, but I'd say let's change the logo now if nobody cares, so one thing less to think about. I'll change the twitter logo and tweet about it once this is merged. | {
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"default... | closed | false | null | [] | null | 8 | 2019-10-25T06:00:54Z | 2019-11-08T00:52:27Z | 2019-11-08T00:52:27Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29217 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29217/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29217/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29217/events | https://github.com/pandas-dev/pandas/issues/29217 | 512,321,571 | MDU6SXNzdWU1MTIzMjE1NzE= | 29,217 | In read_excel parse_date=False is not working | {
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"description": "read_excel, to_excel"... | closed | false | null | [] | null | 8 | 2019-10-25T06:05:27Z | 2019-11-23T23:02:53Z | 2019-11-23T23:02:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.read_file('filename.xlsx', header= None, skiprows=1, parse_dates=False)
```
#### Problem description
In read_excel parse_date default value is False , but still it is parsing the date.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.14.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.0.8
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 17 | 2019-10-25T07:17:34Z | 2020-01-09T12:59:57Z | 2020-01-07T07:05:19Z | CONTRIBUTOR | null | The output of the AssertionError raised by assert_frame_equal is changed:
- old: `AssertionError: Attributes of DataFrame.iloc[:, 1] are different`
- new: `AssertionError: Attributes of DataFrame.loc[:, 'b'] are different`
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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As `TestData` was replaced with fixtures in all series tests, we can `pandas/tests/series/_common.py` altogether (it's only content is `TestData`). | {
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} | 3 | 2019-10-25T09:27:14Z | 2019-10-29T16:18:29Z | 2019-10-29T16:18:24Z | CONTRIBUTOR | null | Part of #22550
* Replaced test_data fixture in `pandas/tests/series/indexing/` with more granular fixtures
* Removed pandas/tests/series/indexing/conftest.py as the test_data fixture is not needed anymore
* Removed pandas/tests/series/common.py as TestData isn't used anymore
- [x] closes #22550
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/29221 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29221/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29221/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29221/events | https://github.com/pandas-dev/pandas/issues/29221 | 512,431,386 | MDU6SXNzdWU1MTI0MzEzODY= | 29,221 | df.sample random_state behavior across processes | {
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} | [] | closed | false | null | [] | null | 4 | 2019-10-25T10:19:26Z | 2019-10-25T10:30:56Z | 2019-10-25T10:29:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import multiprocessing as mp
import pandas as pd
def sample(df):
df_sampled = df.sample(df.shape[0], replace=True)
print(df_sampled.index)
if __name__ == '__main__':
with mp.Pool(4) as p:
df1 = pd.DataFrame({
'r1': [0, 1, 2, 3, 4, 5],
'r2': [0, 1, 3, 4, 1, 9]
})
df2 = pd.DataFrame({
'r1': [0, 39, 5, 3, 4, 5],
'r2': [0, 1, 1, 9, 4, 4]
})
plist = [df1, df2]
p.map( sample, plist )
```
#### Problem description
When you run the above code, the samples generated are identical. After a significant amount of time debugging this, I finally came to the conclusion that it's probably related to the fact that numpy copies the RandomState from the parent process to the child processes, so the random seed used by the two child processes are the same (see: https://github.com/numpy/numpy/issues/9650).
On a similar, but more complicated example, the state wasn't necessarily the same across all processes, but would be shared between subsets of processes (e.g., there might be three states shared by six child processes). So I'm definitely not sure exactly what's going on under the hood, but it's definitely confusing.
I *did* manage to figure out that it can be solved by explicitly passing a new random state too `sample()`.
I can't find any reference to this anywhere, so I don't know if it's expected behavior or a bug, and so I don't actually know what the problem is. I would suggest that if it's not a bug then there is a documentation problem because this seems unintuitive. If I call `df.sample` twice in a row in a single thread (without setting the random_state param), I get different samples, so it seems logical that if I call `df.sample` twice in different threads it would behave similarly and produce two different samples. Thus, I'd suggest that if it's expected behavior it should be explicitly documented somewhere accessible.
I'll also note that this seems to be an issue even if you create the data frame in the child process, so I'm guessing Pandas is using some kind of global RandomState.
This is, arguably, different than the numpy issue inasmuch as Pandas hides the internal implementation around the random seeding, making it much more difficult to debug the actual issue. So while I understand the reasoning behind leaving the behavior as is in the numpy case, I'm not sure their justifications are as strong here.
I'll go ahead and apologize if this is either noted somewhere and I missed it or something that should be understood by me. I spent a decent amount of time trying to solve this issue and couldn't find this behavior documented anywhere and I'm not well-versed in Pandas, so if the problem is on my end, I'm sorry.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.3
setuptools: 41.2.0
Cython: None
numpy: 1.16.4
scipy: 1.3.1
pyarrow: 0.12.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details> | {
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} | 1 | 2019-10-25T12:01:46Z | 2019-10-25T12:41:39Z | 2019-10-25T12:41:36Z | CONTRIBUTOR | null | Part of #22471
This is removing/replacing TestData usage in the following files:
* `test_reshape.py`
* `test_sorting.py`
* `test_subclass.py`
* `test_timeseries.py`
* `test_to_csv.py`
--------
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-10-25T13:43:46Z | 2019-12-04T13:10:24Z | 2019-12-04T13:10:24Z | MEMBER | null | From a quick look, it seems we are not testing the latest version of pyarrow on our CI. | {
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} | 1 | 2019-10-25T16:10:22Z | 2019-10-29T16:16:52Z | 2019-10-29T16:16:45Z | CONTRIBUTOR | null | Part of #22471
This is removing/replacing TestData usage in the following files:
* `test_query_eval.py`
* `test_indexing.py`
Also removes `TestData` from `pandas/tests/frame/common.py`
--------
- [x] closes #22471
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 0 | 2019-10-25T17:13:32Z | 2019-11-14T18:06:25Z | 2019-11-14T18:06:08Z | MEMBER | null | I've stumbled across this code a few times in groupby and it took me a while to grasp. I'm pretty sure this always evaluates to False, unless we are dealing with a MultiIndex.
For code clarity, I think it would be better if we just did an isinstance check as I don't think the intent of `_has_complex_internals` is very clear | {
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} | 5 | 2019-10-25T19:21:18Z | 2019-11-08T14:05:54Z | 2019-11-08T14:05:49Z | MEMBER | null | xref the discussion in https://github.com/pandas-dev/pandas/issues/20612, and a companion to my PR in Arrow: https://github.com/apache/arrow/pull/5512
Summary: to support ExtensionArrays in the conversion of an arrow table into a pandas DataFrame, we need some way to convert pyarrow Arrays into pandas ExtensionArrays, given a certain pandas dtype (in pyarrow, we can for example know the resulting dtype from the stored metadata).
For that, I propose to add the `ExtensionDtype.__from_arrow__` method, with the following signature:
```
class ExtensionDtype:
def __from_arrow__(self, array: pyarrow.Array) -> pandas.ExtensionArray:
...
```
Note: I only added documentation about it (which should still be expanded) for now, and not a method in the base class (eg a NotImplementedError), because in pyarrow we use `hasattr` to see if this is supported (see the linked arrow PR). | {
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} | 2 | 2019-10-25T20:41:52Z | 2019-10-25T21:25:58Z | 2019-10-25T21:25:54Z | MEMBER | null | This might fix the intermittent build failures by updating the package lists before trying to install anything via aptitude | {
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```python
import numpy as np
import pandas as pd
arr = np.random.randint(2, size=10)
arr
array([0, 1, 0, 1, 0, 0, 0, 0, 0, 1])
sparr = pd.SparseArray(arr, fill_value=0)
sparr
[0, 1, 0, 1, 0, 0, 0, 0, 0, 1]
Fill: 0
IntIndex
Indices: array([1, 3, 9], dtype=int32)
np_arr = np.asarray(sparr)
np_arr
array([1, 1, 1])
```
#### Problem description
Converting back to a dense array from a Pandas SparseArary no longer works as intended. It should keep all values in the array as is, but instead the new array removes all elements that are the fill_value. Thus the new array ends up with less elements than before.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
```python
np_arr
array([0, 1, 0, 1, 0, 0, 0, 0, 0, 1])
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 5.2.1
pip: 18.1
setuptools: 41.4.0
Cython: 0.29.13
numpy: 1.17.3
scipy: 1.3.1
pyarrow: None
xarray: None
IPython: 7.8.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.3.10
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 5 | 2019-10-26T05:14:57Z | 2019-12-13T14:21:39Z | 2019-12-13T14:21:39Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df = pd.read_csv("historical_dataset.csv")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 709, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 449, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 818, in __init__
self._make_engine(self.engine)
File "/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 1049, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 1695, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 402, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 718, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: File b'historical_dataset.csv' does not exist
```
#### Problem description
Noticed that when source does not exist, there is a leading 'b' on the source on the error message. For Python3, is it necessary to do the encode operation on line 667 of https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/parsers.pyx? Works fine for Python2.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-31-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.14.5
pytz : 2018.5
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : 1.7.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.3
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.3
matplotlib : 2.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/29234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29234/events | https://github.com/pandas-dev/pandas/pull/29234 | 512,794,116 | MDExOlB1bGxSZXF1ZXN0MzMyNzM4Mjg2 | 29,234 | PERF: PeriodEngine.get_loc accept narrow data type | {
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] | closed | false | null | [] | null | 20 | 2019-10-26T05:34:34Z | 2020-01-17T00:23:29Z | 2020-01-16T21:36:00Z | CONTRIBUTOR | null | - [x] closes #29038
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xref #28628.
I want to refactor `PeriodIndex.get_loc`. Because `PeriodIndex.get_loc` compute twice `Int64index.get_loc` and `PeriodEngine.get_loc`. Problem is only `PeriodEngine.get_loc` accepts `Period.ordinal`.
I try to change `PeriodIndex.get_loc` in two ways.
First one, I try to remove `PeriodEngine.get_loc` in `PeriodIndex.get_loc`. so just compute `.get_loc` once. I change several ways But always stuck in test cases. So I give it up this way.
Second one, I try to change `PeriodEngine.get_loc` to accept `Period` not only `Period.ordinal`. This way is success in that work successfully and pass all test cases. However Problem is most cases users don't use `Period` using `PeriodIndex.get_loc` and `PeriodEngine.get_loc` can't accept "intolerance" argument. Not using `Int64index.get_loc` can't be possible.
But I send PR. Little improvement is also improvement.
---------------------------------------------------------------------------------------------------------------------
improvement : memory usage with Period.get_loc
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https://api.github.com/repos/pandas-dev/pandas/issues/29235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29235/events | https://github.com/pandas-dev/pandas/issues/29235 | 512,820,850 | MDU6SXNzdWU1MTI4MjA4NTA= | 29,235 | Categories reordered to alphabetical when plotting histograms | {
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"description": "Ca... | closed | false | null | [] | null | 0 | 2019-10-26T10:44:24Z | 2020-05-05T00:50:16Z | 2020-05-05T00:50:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({
'width': [0.7, 0.2, 0.15, 0.2, 1.1],
'length': [1.5, 0.5, 1.2, 0.9, 3]
}, index= ['pig', 'rabbit', 'duck', 'chicken', 'horse'])
# Whether or not `column` is passed the order is still reordered to alphabetical.
df.hist(column=['width','length'])
plt.show()
```
#### Problem description
When plotting histograms from a DataFrame (see code-snippet above) the order of subplots is not maintained. Passing `column` does not appear to have any effect on the ordering either.
#### Expected Output
Plots/subplots order equals column order in DataFrame.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.2.1
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.2.2
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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] | closed | false | null | [] | null | 0 | 2019-10-26T12:47:00Z | 2019-10-29T03:16:23Z | 2019-10-29T03:16:23Z | CONTRIBUTOR | null | [pandas/core/Indexes/timedeltas.py](https://github.com/pandas-dev/pandas/blob/c7462edc33e89452ae55cb52f61b23cb66e48a5b/pandas/core/indexes/timedeltas.py)
#28602
#27977
[pandas/core/indexes/base.py](https://github.com/pandas-dev/pandas/blob/0a44feafd58f3c5576e5f4bf3d0136dcacc352f2/pandas/core/indexes/base.py)
```
python ./scripts/validate_docstrings.py pandas.TimedeltaIndex.to_series
```
```
7 Errors found:
Summary should fit in a single line
Parameter "index" description should start with a capital letter
Parameter "index" description should finish with "."
Parameter "name" description should start with a capital letter
Parameter "name" description should finish with "."
The first line of the Returns section should contain only the type, unless multiple values are being returned
Return value has no description
```
[pandas/core/arrays/datetime.py](https://github.com/pandas-dev/pandas/blob/18a9e4c8ab253e83ba43767d890576186be13332/pandas/core/arrays/datetimelike.py)
```
./scripts/validate_docstrings.py pandas.TimedeltaIndex.mean
```
```
5 Errors found:
Parameters {**kwargs, *args} not documented
Unknown parameters {skipna}
Parameter "skipna" description should finish with "."
Return value has no description
Missing description for See Also "numpy.ndarray.mean" reference
```
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} | 7 | 2019-10-26T13:49:24Z | 2019-11-14T18:06:17Z | 2019-11-14T18:06:09Z | CONTRIBUTOR | null | closes #29227
Hi, it my first issues within hacktoberfest.
I replaced _has_complex_internals with isinstance/
Is it possible to import ABCMultiIndex?
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} | [] | closed | false | null | [] | null | 1 | 2019-10-26T17:27:41Z | 2019-10-26T17:37:57Z | 2019-10-26T17:37:57Z | NONE | null | If a columns is treated by pandas.DataFrame.fillna and it doesnt have any missing values then pandas automatically converts the datatypes of columns 'int','float' or 'bool' if it can
You can reproduce the behavior here:
```
improt pandas as pd
x = pd.DataFrame({'a':[1,2,np.nan,3],'d':['x','y','z',np.nan],'e':[1,1,2,2]})
x['e'] = x['e'].astype('O')
obj_cols = x.select_dtypes(include='O').columns
print(x.loc[:,obj_cols].dtypes)
#d object
#e object
# dtype: object
x.loc[:,obj_cols]=x.select_dtypes(include='O').fillna('missing')
print(x.loc[:,obj_cols].dtypes)
#d object
#e int64
#dtype: object]
```
is this a bug or a feature?
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} | 2 | 2019-10-26T21:06:21Z | 2019-10-28T19:36:39Z | 2019-10-28T18:40:13Z | CONTRIBUTOR | null | Per discussion in #29165, this PR significantly reduces the run time of the `rolling.Apply.time_rolling` benchmark from 8 minutes minimum.
```
$ asv dev -b rolling.Apply.time_rolling
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[ 0.00%] ·· Benchmarking existing-py_home_chris_anaconda3_bin_python
[ 50.00%] ··· Running (rolling.Apply.time_rolling--).
[100.00%] ··· rolling.Apply.time_rolling ok
[100.00%] ··· ============= ======== ======= =========================== ============= ============
-- raw
---------------------------------------------------------- --------------------------
constructor window dtype function True False
============= ======== ======= =========================== ============= ============
DataFrame 3 int <built-in function sum> 1.87±0.1ms 44.8±2ms
DataFrame 3 int <function sum> 3.53±0.2ms 126±3ms
DataFrame 3 int <function Apply.<lambda>> 4.20±0.2ms 126±7ms
DataFrame 3 float <built-in function sum> 1.86±0.07ms 45.1±1ms
DataFrame 3 float <function sum> 3.42±0.03ms 129±5ms
DataFrame 3 float <function Apply.<lambda>> 4.34±0.5ms 133±9ms
DataFrame 300 int <built-in function sum> 31.8±3ms 44.1±2ms
DataFrame 300 int <function sum> 2.90±0.2ms 92.6±10ms
DataFrame 300 int <function Apply.<lambda>> 3.24±0.2ms 93.9±4ms
DataFrame 300 float <built-in function sum> 33.3±20ms 85.4±40ms
DataFrame 300 float <function sum> 3.06±0.3ms 91.0±3ms
DataFrame 300 float <function Apply.<lambda>> 3.19±0.2ms 91.5±4ms
Series 3 int <built-in function sum> 1.42±0.03ms 43.6±1ms
Series 3 int <function sum> 3.08±0.06ms 127±2ms
Series 3 int <function Apply.<lambda>> 3.67±0.2ms 127±20ms
Series 3 float <built-in function sum> 1.41±0.03ms 43.5±0.5ms
Series 3 float <function sum> 3.08±0.1ms 123±3ms
Series 3 float <function Apply.<lambda>> 3.65±0.1ms 129±6ms
Series 300 int <built-in function sum> 31.2±2ms 43.8±3ms
Series 300 int <function sum> 2.35±0.1ms 90.4±4ms
Series 300 int <function Apply.<lambda>> 2.74±0.2ms 93.2±6ms
Series 300 float <built-in function sum> 31.7±0.8ms 45.6±1ms
Series 300 float <function sum> 2.36±0.1ms 108±20ms
Series 300 float <function Apply.<lambda>> 2.82±0.09ms 99.4±2ms
============= ======== ======= =========================== ============= ============
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29240/events | https://github.com/pandas-dev/pandas/issues/29240 | 512,907,508 | MDU6SXNzdWU1MTI5MDc1MDg= | 29,240 | Trying to use to_sql on dataframe with numpy.ndarray columns causes ValueError | {
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"description": "to_sql, ... | open | false | null | [] | null | 3 | 2019-10-27T01:44:31Z | 2020-05-16T04:06:16Z | null | NONE | null | I am trying out using pandas+sqlalchemy (specifically sqlite) as a means to store my data. In my use case, I will have a number of rows where most will be obvious types such as string, int, float, etc - but I will want to store a few numpy ndarrays. In the example below, each 'channel' entry is a 1x3000 array of floats. For higher dimensional arrays (at most 2D) I plan to store the shape of the data alongside the flattened array, so this is all I need.
#### Code Sample, a copy-pastable example if possible
This was run in Jupyter Lab.
```python
import sqlalchemy
import numpy as np
import pandas as pd
engine = sqlalchemy.create_engine('sqlite:///:memory:')
allPts = []
for i in range(0,30):
name = "R"+str(i).zfill(3)
start = np.random.rand()
end = np.random.rand()
data = np.random.random((4,3000))
thisDict = {'name':name,'start':start,'end':end,'channel1':data[:,0],'channel2':data[:,1],'channel3':data[:,2],'channel4':data[:,3]}
allPts.append(pd.Series(thisDict))
asDF = pd.DataFrame(allPts)
asDF.to_sql('dftest',engine,if_exists='replace')
```
This produces the traceback
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-e1fb02c5da64> in <module>
----> 1 asDF.to_sql('dftest',engine,if_exists='replace')
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\pandas\core\generic.py in to_sql(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
2710 chunksize=chunksize,
2711 dtype=dtype,
-> 2712 method=method,
2713 )
2714
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\pandas\io\sql.py in to_sql(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
516 chunksize=chunksize,
517 dtype=dtype,
--> 518 method=method,
519 )
520
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\pandas\io\sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)
1318 )
1319 table.create()
-> 1320 table.insert(chunksize, method=method)
1321 if not name.isdigit() and not name.islower():
1322 # check for potentially case sensitivity issues (GH7815)
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\pandas\io\sql.py in insert(self, chunksize, method)
754
755 chunk_iter = zip(*[arr[start_i:end_i] for arr in data_list])
--> 756 exec_insert(conn, keys, chunk_iter)
757
758 def _query_iterator(
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\pandas\io\sql.py in _execute_insert(self, conn, keys, data_iter)
668 """
669 data = [dict(zip(keys, row)) for row in data_iter]
--> 670 conn.execute(self.table.insert(), data)
671
672 def _execute_insert_multi(self, conn, keys, data_iter):
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\base.py in execute(self, object_, *multiparams, **params)
986 raise exc.ObjectNotExecutableError(object_)
987 else:
--> 988 return meth(self, multiparams, params)
989
990 def _execute_function(self, func, multiparams, params):
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\sql\elements.py in _execute_on_connection(self, connection, multiparams, params)
285 def _execute_on_connection(self, connection, multiparams, params):
286 if self.supports_execution:
--> 287 return connection._execute_clauseelement(self, multiparams, params)
288 else:
289 raise exc.ObjectNotExecutableError(self)
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\base.py in _execute_clauseelement(self, elem, multiparams, params)
1105 distilled_params,
1106 compiled_sql,
-> 1107 distilled_params,
1108 )
1109 if self._has_events or self.engine._has_events:
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1251 except BaseException as e:
1252 self._handle_dbapi_exception(
-> 1253 e, statement, parameters, cursor, context
1254 )
1255
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\base.py in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1473 util.raise_from_cause(sqlalchemy_exception, exc_info)
1474 else:
-> 1475 util.reraise(*exc_info)
1476
1477 finally:
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\util\compat.py in reraise(tp, value, tb, cause)
151 if value.__traceback__ is not tb:
152 raise value.with_traceback(tb)
--> 153 raise value
154
155 def u(s):
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\base.py in _execute_context(self, dialect, constructor, statement, parameters, *args)
1227 if not evt_handled:
1228 self.dialect.do_executemany(
-> 1229 cursor, statement, parameters, context
1230 )
1231 elif not parameters and context.no_parameters:
c:\users\(me)\documents\pyenv\dataproc\lib\site-packages\sqlalchemy\engine\default.py in do_executemany(self, cursor, statement, parameters, context)
575
576 def do_executemany(self, cursor, statement, parameters, context=None):
--> 577 cursor.executemany(statement, parameters)
578
579 def do_execute(self, cursor, statement, parameters, context=None):
ValueError: could not convert BLOB to buffer
```
#### Problem description
I notice that the bottom of the traceback specifies the error started in sqlalchemy - though for all my googling I can't find any record of this problem (or error message) anywhere either with sqlalchemy or pandas. I'm *assuming* this means I probably am doing something wrong, but I'm not seeing it when looking at docs etc. Storing a numpy array as a binary blob makes sense to me, so I feel like the type inference makes sense.
#### Expected Output
Writing to the database/no error. :)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.6.0
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29241/events | https://github.com/pandas-dev/pandas/issues/29241 | 512,980,469 | MDU6SXNzdWU1MTI5ODA0Njk= | 29,241 | merge vs. join - unify parameter names | {
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"description": "Concat, ... | open | false | null | [] | null | 2 | 2019-10-27T15:23:52Z | 2020-05-07T03:53:05Z | null | CONTRIBUTOR | null | #### Problem description
`pandas.DataFrame.merge` function is conceptually simillar like `pandas.DataFrame.join` function.
(first one one merges on specified columns, second merges on index).
These 2 functions use various parameters to do the same thing:
* `join` function has 2 params: `lsuffix + rsuffix`
* `merge` function has only 1 param: `suffixes`
We should unify names and usage of these parameter(s).
Let's use first way or second way, and deprecate the other way.
_(Personally, I prefer to have only 1 param `suffixes` as it is shorter and there is 1 param for 1 purpose)._
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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"description": "Incorrect or improved er... | open | false | null | [] | null | 5 | 2019-10-27T17:13:41Z | 2021-07-22T05:17:05Z | null | MEMBER | null | ```python
>>> import pandas as pd
>>>
>>> pd.__version__
'0.26.0.dev0+684.g953757a3e'
>>>
>>> from pandas._config import config
>>>
>>> config.register_option("a.b.c", 1)
>>>
>>> config.get_option("a.b.c")
1
>>>
>>> config.register_option("a.b.c", 2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\_config\config.py", line 445, in register_option
raise OptionError(msg.format(key=key))
pandas._config.config.OptionError: "Option 'a.b.c' has already been registered"
>>>
>>> config.register_option("a.b", {"c": 2})
>>>
>>> config.get_option("a.b.c")
2
>>>
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} | 3 | 2019-10-27T17:15:37Z | 2020-02-05T08:47:25Z | 2020-01-26T01:35:52Z | CONTRIBUTOR | null |
xref #28097
This is follow up pr. eliminate branch statement. "grouper" variable is assigned when handle NAs. | {
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} | 5 | 2019-10-27T17:58:52Z | 2021-11-20T23:21:58Z | 2019-10-30T22:47:16Z | MEMBER | null | This just gets the helper functions. Getting the rest is more difficult. | {
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] | closed | false | null | [] | null | 1 | 2019-10-27T18:01:55Z | 2019-11-03T13:00:00Z | 2019-11-03T12:59:56Z | CONTRIBUTOR | null | For `vmin` `vmax` use the same implementation as `Styler.bar`
For dtype `Int64` issue, deprecated `.values` and use `.to_numpy` instead
Here explicitly assign the dtype to float since we are doing normalize
- [x] closes #12145 and #28869
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-10-27T18:05:54Z | 2019-10-31T17:13:44Z | 2019-10-31T17:10:31Z | MEMBER | null | The remaining two non-trivial cases are ("aggregate", "first") and ("aggregate", "median"). The "first" case I think is straightforward. The "median" case I'm reticent to change because it isn't clear to me why it is a dict at all (cc @WillAyd if you have any ideas) | {
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] | closed | false | null | [] | null | 4 | 2019-10-27T20:03:06Z | 2021-07-22T05:29:10Z | 2021-07-22T05:29:10Z | CONTRIBUTOR | null | Hi @simonjayhawkins @jreback ,
Merge here refers to `full outer join`.
Can we have a feature of merging a list of dataframes. Looping over the list of files one after the other without considering their size, can be inefficient.
The algorithm capitalizes on the fact that `full outer join` is `associative` as well as `commutative`, so the order of merging would not change the final result.
Let's say we have 100 files `(f1, f2, f3, .... f100)` all of them indexed by `id`, all of which needs to be merged together to form one consolidate file. Insetead of merging them one after the other in a loop, the quick_merge follows these steps.
1. Add all the files `f1, f2, f3, ... f100` to a heap(the datastructure).
2. Balance the heap, such that the top contains the one with `minimum rowsize`.
3. Pop the top 2 elements from the heap, merge(full outer join) them and push back again to the heap.
4. Repeat the process until we have just one file in the heap, which will be the final merged file.
5. Return the final merged file from heap.
Theoretically a full outer merge costs `O(n*m)`, where `n` and `m` are number of rows in the files (without indexing).
If we index, the same complexity reduces to `O(n+m)`. So, if we have two small files in each step to merge, we save time.
### Usage
```python
from quick_merge import quick_merge as qm
df_1 = pd.DataFrame(
np.random.randint(0, 100, size=(100, 6)), columns=list("ABCDEF")
)
df_2 = pd.DataFrame(
np.random.randint(0, 100, size=(100, 9)), columns=list("ABCDEFGHI")
)
df_3 = pd.DataFrame(np.random.randint(0, 100, size=(100, 3)), columns=list("ABC"))
dfs = [df_1, df_2, df_3]
res = qm(dataframes=dfs, merge_key='A')
```
https://github.com/farziengineer/quick_merge/blob/master/README.md | {
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] | closed | false | null | [] | null | 8 | 2019-10-27T22:48:47Z | 2019-11-03T15:41:13Z | 2019-11-03T15:41:13Z | NONE | null | Added to_html() and _repr_html_() to Series class.
- [ ] closes #5563
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-10-27T23:02:56Z | 2020-02-09T17:05:17Z | 2019-10-28T18:40:57Z | MEMBER | null | Appears to be dead code, allows for simplification of _get_hashtable_algo. | {
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} | 9 | 2019-10-28T07:49:59Z | 2019-12-18T08:11:21Z | 2019-12-18T08:11:21Z | MEMBER | null | I recently ran into this as well (but forgot to open an issue), and raised now by @alexiswl in https://github.com/pandas-dev/pandas/issues/18262#issuecomment-546746982
`Series.item()` was a consequence of (historically) inheriting from np.ndarray, and was deprecated (like a set of other ndarray-inhertited methods/attributes) a while ago.
While `.item()` could also be used to select the "i-th" element (`.item(i)`), and this use case is certainly redundant (not arguing here to get that aspect back), there is one use case where `item()` can actually be useful: if you do not pass *i*, the method returns the element of the Series *only* if it has one element, otherwise it errors.
Such a situation can typically occur if you use boolean indexing (or `query`) to select a single element. Eg in cases like `s[s == 'val']` or `df.loc[df['col1'] == 'val', 'col2']` where you know the condition *should* yield a single element.
You then typically want the scalar element as result, but those two code snippets give you a Series of one element. In those cases, you could use `item()` to retrieve it: `s[s == 'val'].item()`.
I saw some people using `.item()` exactly for this use case, so wondering if it is worth to keep it for this (only the version without passing *i*).
The logical alternative is doing a `.iloc[0]`, but `.item()` has the advantage of guaranteeing there was actually only one result item.
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] | closed | false | null | [] | null | 2 | 2019-10-28T09:54:30Z | 2019-10-28T17:31:55Z | 2019-10-28T17:31:55Z | NONE | null | #### Bug reproducing example
```python
import pandas as pd
a = {'col_name1': {('first', 'first'): 0, ('first', 'second'): 1}, 'col_name2': {('first', 'first'): 1, ('first', 'second'): 2}}
df = pd.DataFrame(a)
print(df.to_csv(index_label=False, sep='|'))
```
#### Printed value
```
__name1__|__name2__
first|first|0|1
first|second|1|2
```
#### Expected Output
```
||__name1__|__name2__
first|first|0|1
first|second|1|2
```
### Problem description
#### There was found incorrect work in function of `df.to_csv(index_label=False)` with df multiindex: Unexpected move out column names over index columns.
**Note**: I've check this problem by look for phrase "to_csv index_label=False" and found some similar problems, however this problem wasn't described in all of its. I fork pandas master and install it for test issue.
**Note**: *I'm newbie in python (half of year development) and this my first commit for public repository on github. I read some articles, such as "Craft Minimal Bug Reports " and "How to create a Minimal, Reproducible Example"
I have two questions:*
*1. How i can test my solution?
2. Need I create pull request now or I must to wait comments below?*
P.s. *I create commit in my fork: https://github.com/pandas-dev/pandas/compare/master...PhenixFlame:fix_branch*
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : 953757a3e37ffb80570a20a8eca52dae35fc27bb
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-32-generic
pandas : 0.26.0.dev0+684.g953757a3e
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.13
```
### Where is the problem?
I' am suppose that the error is locate in pandas/pandas/io/formats/csvs.CSVFormatter._save_header:277-278 strings:
<details>
```python
if self.index:
...................................................
else:
encoded_labels = []
```
my suggestion changes:
```python
if self.index:
...................................................
else:
index_label = [''] * len(obj.index.names)
encoded_labels = list(index_label)
```
overview of code for analyse:
```python
def _save_header(self):
index_label = self.index_label # index_label=False
................................................................................................
if self.index:
# should write something for index label
if index_label is not False:
if index_label is None:
if isinstance(obj.index, ABCMultiIndex):
index_label = []
for i, name in enumerate(obj.index.names):
if name is None:
name = ""
index_label.append(name)
else:
index_label = obj.index.name
if index_label is None:
index_label = [""]
else:
index_label = [index_label]
elif not isinstance(
index_label, (list, tuple, np.ndarray, ABCIndexClass)
):
# given a string for a DF with Index
index_label = [index_label]
encoded_labels = list(index_label)
else:
encoded_labels = []
if not has_mi_columns or has_aliases:
encoded_labels += list(write_cols)
writer.writerow(encoded_labels)
else:
# write out the mi
columns = obj.columns
# write out the names for each level, then ALL of the values for
# each level
for i in range(columns.nlevels):
# we need at least 1 index column to write our col names
col_line = []
if self.index:
# name is the first column
col_line.append(columns.names[i])
if isinstance(index_label, list) and len(index_label) > 1:
col_line.extend([""] * (len(index_label) - 1))
col_line.extend(columns._get_level_values(i))
writer.writerow(col_line)
```
</details>
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 2 | 2019-10-28T13:39:12Z | 2020-05-12T10:31:18Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df1 = pd.DataFrame(data={'col1': [1.1, 1.2]},
index=pd.MultiIndex.from_product([['A'], [1.0, 2.0]],
names=['id1', 'id2']))
df2 = pd.DataFrame(data={'col2': [2.1, 2.2]},
index=pd.MultiIndex.from_product([['A'], [np.NaN, 2.0]],
names=['id1', 'id2']))
print(df1.join(df2))
```
#### Problem description
The index containing the NaN value of `df2` is falsely joined with the index of `df1`. Therefore, the result contains both values of `df2` instead of only one value and a NaN value.
#### Expected Output
```
col1 col2
id1 id2
A 1.0 1.1 NaN
2.0 1.2 2.2
```
The expected result was returned by older pandas versions (successfully tested with 0.18.1 ; 0.20.3 ; 0.21.1 ; 0.22.0).
#### Actual Output
```
col1 col2
id1 id2
A 1.0 1.1 2.1
2.0 1.2 2.2
```
This result is returned by newer pandas versions, beginning with version 0.23.0 (tested with 0.23.0 ; 0.23.4 ; 0.24.2 ; 0.25.2).
I suppose this is a bug, or am I missing something?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29253/events | https://github.com/pandas-dev/pandas/issues/29253 | 513,322,157 | MDU6SXNzdWU1MTMzMjIxNTc= | 29,253 | ml.plots.stresses() transforms input series Evaporation | {
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} | [] | closed | false | null | [] | null | 2 | 2019-10-28T13:59:34Z | 2019-10-28T15:30:22Z | 2019-10-28T14:24:44Z | NONE | null | The original series is transformed with the parameter "recharge_f"
While I expect te function ml.plots.stresses() to plot the original series. | {
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] | open | false | null | [] | null | 8 | 2019-10-28T15:57:51Z | 2020-02-27T01:31:48Z | null | CONTRIBUTOR | null | Fix docstrings where summary should fit in a single line.
From: #27977, most of these errors should just be formatting issues where there are no single line summaries. After setting up and generating the SS06 errors with `python scripts/validate_docstrings.py --errors=SS06`, the workflow I would suggest is:
0. **Read through comments on this issue to check and see if you are doing any duplicate work.**
1. Pick around 10 docstrings to work on and comment those particular docstrings here
1. search for the specific function docstrings in the documentation (https://pandas.pydata.org/pandas-docs/stable/index.html)
1. search for the corresponding lines in the *.py that need to be edited
1. edit summary to fit on one line
1. double check changes with `python scripts/validate_docstrings.py <your-function-or-method>`
Here are all the current errors after running `python ./scripts/validate_docstrings.py --errors=SS06 `, will place a check once your PRs referencing this issue have been merged:
- [ ] `pandas.tseries.offsets.CustomBusinessDay: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.SemiMonthEnd: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.SemiMonthBegin: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.LastWeekOfMonth: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.FY5253Quarter: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.BDay.base: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.BMonthEnd.base: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.BMonthBegin.base: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CBMonthEnd.base: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CBMonthEnd.apply_index: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CBMonthBegin.base: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CBMonthBegin.apply_index: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CDay: Summary should fit in a single line`
- [ ] `pandas.tseries.offsets.CDay.base: Summary should fit in a single line`
- [ ] `pandas.tseries.frequencies.to_offset: Summary should fit in a single line`
- [ ] `pandas.Series.to_period: Summary should fit in a single line`
- [ ] `pandas.Series.transform: Summary should fit in a single line`
- [ ] `pandas.Series.describe: Summary should fit in a single line`
- [ ] `pandas.Series.kurt: Summary should fit in a single line`
- [ ] `pandas.Series.skew: Summary should fit in a single line`
- [ ] `pandas.Series.kurtosis: Summary should fit in a single line`
- [ ] `pandas.Series.is_monotonic: Summary should fit in a single line`
- [ ] `pandas.Series.is_monotonic_increasing: Summary should fit in a single line`
- [ ] `pandas.Series.is_monotonic_decreasing: Summary should fit in a single line`
- [ ] `pandas.Series.align: Summary should fit in a single line`
- [ ] `pandas.Series.first: Summary should fit in a single line`
- [ ] `pandas.Series.last: Summary should fit in a single line`
- [ ] `pandas.Series.reindex: Summary should fit in a single line`
- [ ] `pandas.Series.filter: Summary should fit in a single line`
- [ ] `pandas.Series.argsort: Summary should fit in a single line`
- [ ] `pandas.Series.unstack: Summary should fit in a single line`
- [ ] `pandas.Series.explode: Summary should fit in a single line`
- [ ] `pandas.Series.update: Summary should fit in a single line`
- [ ] `pandas.Series.slice_shift: Summary should fit in a single line`
- [ ] `pandas.Series.dt.date: Summary should fit in a single line`
- [ ] `pandas.Series.dt.timetz: Summary should fit in a single line`
- [ ] `pandas.Series.dt.tz_localize: Summary should fit in a single line`
- [ ] `pandas.Series.str.center: Summary should fit in a single line`
- [ ] `pandas.Series.str.decode: Summary should fit in a single line`
- [ ] `pandas.Series.str.encode: Summary should fit in a single line`
- [ ] `pandas.Series.str.extractall: Summary should fit in a single line`
- [ ] `pandas.Series.str.find: Summary should fit in a single line`
- [ ] `pandas.Series.str.index: Summary should fit in a single line`
- [ ] `pandas.Series.str.len: Summary should fit in a single line`
- [ ] `pandas.Series.str.ljust: Summary should fit in a single line`
- [ ] `pandas.Series.str.normalize: Summary should fit in a single line`
- [ ] `pandas.Series.str.replace: Summary should fit in a single line`
- [ ] `pandas.Series.str.rfind: Summary should fit in a single line`
- [ ] `pandas.Series.str.rindex: Summary should fit in a single line`
- [ ] `pandas.Series.str.rjust: Summary should fit in a single line`
- [ ] `pandas.Series.str.translate: Summary should fit in a single line`
- [ ] `pandas.Series.str.wrap: Summary should fit in a single line`
- [ ] `pandas.Series.str.get_dummies: Summary should fit in a single line`
- [ ] `pandas.Series.str: Summary should fit in a single line`
- [ ] `pandas.Index.str: Summary should fit in a single line`
- [ ] `pandas.Series.plot: Summary should fit in a single line`
- [ ] `pandas.Timestamp.resolution: Summary should fit in a single line`
- [ ] `pandas.Timestamp.fromordinal: Summary should fit in a single line`
- [ ] `pandas.Timestamp.isoweekday: Summary should fit in a single line`
- [ ] `pandas.Timestamp.normalize: Summary should fit in a single line`
- [ ] `pandas.Timestamp.now: Summary should fit in a single line`
- [ ] `pandas.Timestamp.to_julian_date: Summary should fit in a single line`
- [ ] `pandas.Timestamp.today: Summary should fit in a single line`
- [ ] `pandas.Timestamp.tz_localize: Summary should fit in a single line`
- [ ] `pandas.Timestamp.weekday: Summary should fit in a single line`
- [ ] `pandas.Timedelta.isoformat: Summary should fit in a single line`
- [ ] `pandas.Period.strftime: Summary should fit in a single line`
- [ ] `pandas.Interval.closed: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.left: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.right: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.closed: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.length: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.is_non_overlapping_monotonic: Summary should fit in a single line`
- [ ] `pandas.arrays.IntervalArray.set_closed: Summary should fit in a single line`
- [ ] `pandas.read_msgpack: Summary should fit in a single line`
- [ ] `pandas.ExcelWriter: Summary should fit in a single line`
- [ ] `pandas.HDFStore.append: Summary should fit in a single line`
- [ ] `pandas.io.stata.StataReader.value_labels: Summary should fit in a single line`
- [ ] `pandas.io.stata.StataReader.variable_labels: Summary should fit in a single line`
- [ ] `pandas.core.resample.Resampler.transform: Summary should fit in a single line`
- [ ] `pandas.core.resample.Resampler.pipe: Summary should fit in a single line`
- [ ] `pandas.plotting.andrews_curves: Summary should fit in a single line`
- [ ] `pandas.plotting.plot_params: Summary should fit in a single line`
- [ ] `pandas.errors.EmptyDataError: Summary should fit in a single line`
- [ ] `pandas.errors.UnsortedIndexError: Summary should fit in a single line`
- [ ] `pandas.errors.UnsupportedFunctionCall: Summary should fit in a single line`
- [ ] `pandas.api.types.union_categoricals: Summary should fit in a single line`
- [ ] `pandas.api.types.infer_dtype: Summary should fit in a single line`
- [ ] `pandas.api.types.is_datetimetz: Summary should fit in a single line`
- [ ] `pandas.melt: Summary should fit in a single line`
- [ ] `pandas.pivot_table: Summary should fit in a single line`
- [ ] `pandas.crosstab: Summary should fit in a single line`
- [ ] `pandas.qcut: Summary should fit in a single line`
- [ ] `pandas.merge_ordered: Summary should fit in a single line`
- [ ] `pandas.merge_asof: Summary should fit in a single line`
- [ ] `pandas.concat: Summary should fit in a single line`
- [ ] `pandas.unique: Summary should fit in a single line`
- [ ] `pandas.bdate_range: Summary should fit in a single line`
- [ ] `pandas.period_range: Summary should fit in a single line`
- [ ] `pandas.timedelta_range: Summary should fit in a single line`
- [ ] `pandas.infer_freq: Summary should fit in a single line`
- [ ] `pandas.Index: Summary should fit in a single line`
- [ ] `pandas.Index.is_monotonic_increasing: Summary should fit in a single line`
- [ ] `pandas.Index.is_monotonic_decreasing: Summary should fit in a single line`
- [ ] `pandas.Index.copy: Summary should fit in a single line`
- [ ] `pandas.Index.identical: Summary should fit in a single line`
- [ ] `pandas.Index.reindex: Summary should fit in a single line`
- [x] `pandas.Index.where: Summary should fit in a single line`
- [ ] `pandas.Index.unique: Summary should fit in a single line`
- [ ] `pandas.Index.astype: Summary should fit in a single line`
- [ ] `pandas.Index.to_series: Summary should fit in a single line`
- [ ] `pandas.Index.join: Summary should fit in a single line`
- [ ] `pandas.Index.difference: Summary should fit in a single line`
- [ ] `pandas.Index.asof_locs: Summary should fit in a single line`
- [ ] `pandas.Index.get_indexer: Summary should fit in a single line`
- [ ] `pandas.Index.get_indexer_non_unique: Summary should fit in a single line`
- [ ] `pandas.Index.get_value: Summary should fit in a single line`
- [ ] `pandas.Index.slice_indexer: Summary should fit in a single line`
- [ ] `pandas.Int64Index: Summary should fit in a single line`
- [ ] `pandas.UInt64Index: Summary should fit in a single line`
- [ ] `pandas.Float64Index: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.left: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.right: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.closed: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.length: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.is_non_overlapping_monotonic: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.get_indexer: Summary should fit in a single line`
- [ ] `pandas.IntervalIndex.set_closed: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.set_levels: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.set_codes: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.to_hierarchical: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.sortlevel: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.remove_unused_levels: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.get_loc: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.get_loc_level: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.get_indexer: Summary should fit in a single line`
- [ ] `pandas.MultiIndex.get_level_values: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.date: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.timetz: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.inferred_freq: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.indexer_at_time: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.indexer_between_time: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.tz_localize: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.to_perioddelta: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.to_pydatetime: Summary should fit in a single line`
- [ ] `pandas.DatetimeIndex.to_series: Summary should fit in a single line`
- [ ] `pandas.TimedeltaIndex: Summary should fit in a single line`
- [ ] `pandas.TimedeltaIndex.components: Summary should fit in a single line`
- [ ] `pandas.TimedeltaIndex.inferred_freq: Summary should fit in a single line`
- [ ] `pandas.TimedeltaIndex.to_pytimedelta: Summary should fit in a single line`
- [ ] `pandas.TimedeltaIndex.to_series: Summary should fit in a single line`
- [ ] `pandas.PeriodIndex: Summary should fit in a single line`
- [ ] `pandas.Grouper: Summary should fit in a single line`
- [ ] `pandas.core.groupby.GroupBy.pipe: Summary should fit in a single line`
- [ ] `pandas.core.groupby.GroupBy.nth: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.describe: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.filter: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.idxmax: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.idxmin: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.nunique: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.skew: Summary should fit in a single line`
- [ ] `pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing: Summary should fit in a single line`
- [ ] `pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing: Summary should fit in a single line`
- [ ] `pandas.core.groupby.DataFrameGroupBy.corrwith: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.from_custom_template: Summary should fit in a single line`
- [ ] `pandas.io.formats.style.Styler.env: Summary should fit in a single line`
- [ ] `pandas.io.formats.style.Styler.template: Summary should fit in a single line`
- [ ] `pandas.io.formats.style.Styler.loader: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.apply: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.applymap: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.where: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.set_properties: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.clear: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.background_gradient: Summary should fit in a single line`
- [x] `pandas.io.formats.style.Styler.use: Summary should fit in a single line`
- [ ] `pandas.DataFrame: Summary should fit in a single line`
- [ ] `pandas.DataFrame.transform: Summary should fit in a single line`
- [ ] `pandas.DataFrame.corrwith: Summary should fit in a single line`
- [ ] `pandas.DataFrame.describe: Summary should fit in a single line`
- [ ] `pandas.DataFrame.kurt: Summary should fit in a single line`
- [ ] `pandas.DataFrame.kurtosis: Summary should fit in a single line`
- [ ] `pandas.DataFrame.skew: Summary should fit in a single line`
- [ ] `pandas.DataFrame.align: Summary should fit in a single line`
- [ ] `pandas.DataFrame.drop_duplicates: Summary should fit in a single line`
- [ ] `pandas.DataFrame.duplicated: Summary should fit in a single line`
- [x] `pandas.DataFrame.filter: Summary should fit in a single line`
- [x] `pandas.DataFrame.first: Summary should fit in a single line`
- [x] `pandas.DataFrame.idxmax: Summary should fit in a single line`
- [x] `pandas.DataFrame.idxmin: Summary should fit in a single line`
- [x] `pandas.DataFrame.last: Summary should fit in a single line`
- [x] `pandas.DataFrame.reindex: Summary should fit in a single line`
- [x] `pandas.DataFrame.pivot_table: Summary should fit in a single line`
- [x] `pandas.DataFrame.reorder_levels: Summary should fit in a single line`
- [x] `pandas.DataFrame.unstack: Summary should fit in a single line`
- [x] `pandas.DataFrame.melt: Summary should fit in a single line`
- [x] `pandas.DataFrame.slice_shift: Summary should fit in a single line`
- [x] `pandas.DataFrame.to_period: Summary should fit in a single line`
- [x] `pandas.DataFrame.plot: Summary should fit in a single line`
- [x] `pandas.DataFrame.sparse.density: Summary should fit in a single line`
- [x] `pandas.DataFrame.style: Summary should fit in a single line`
- [x] `pandas.Series.as_blocks: Summary should fit in a single line`
- [x] `pandas.Series.ix: Summary should fit in a single line`
- [x] `pandas.Series.ptp: Summary should fit in a single line`
- [x] `pandas.DataFrame.as_blocks: Summary should fit in a single line`
- [x] `pandas.DataFrame.ix: Summary should fit in a single line` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29255/events | https://github.com/pandas-dev/pandas/pull/29255 | 513,439,450 | MDExOlB1bGxSZXF1ZXN0MzMzMjI3MTY1 | 29,255 | Fix SS06 formatting errors | {
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} | 2 | 2019-10-28T17:10:48Z | 2019-10-30T16:17:55Z | 2019-10-29T03:14:34Z | CONTRIBUTOR | null | SS06 (Summary should fit in a single line) errors fixed (#29254) for the following:
```
pandas.DataFrame.slice_shift
pandas.DataFrame.to_period
pandas.DataFrame.plot
pandas.DataFrame.sparse.density
pandas.DataFrame.style
pandas.Series.as_blocks
pandas.Series.ix
pandas.Series.ptp
pandas.DataFrame.as_blocks
pandas.DataFrame.ix
```
Validated with `python scripts/validate_docstrings.py`
Also referencing issue: #27977 | {
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} | 1 | 2019-10-28T19:44:16Z | 2020-01-16T00:33:41Z | 2019-10-30T07:34:30Z | MEMBER | null | follow up to #29174 | {
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} | 5 | 2019-10-28T20:11:15Z | 2020-01-06T16:47:22Z | 2019-10-29T03:16:23Z | CONTRIBUTOR | null | - [ ] closes #29236
only remaining docstring errors are for `TimedeltaIndex.mean`
Parameters {*args, **kwargs} not documented
Unknown parameters {skipna}
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} | 3 | 2019-10-28T21:07:03Z | 2019-10-29T09:00:40Z | 2019-10-29T09:00:40Z | MEMBER | null | With this change (https://github.com/pandas-dev/pandas-sphinx-theme/commit/84134e0804325538918324246f449e694466929b) in the theme, that should work.
However, locally, I am running into the problem that sphinx/html doesn't seem to like the svg logo's .. For local testing I added a converted png which works, but just replacing `.png` with `.svg` makes that the html does not include the image (at least in firefox).
cc @datapythonista
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} | 4 | 2019-10-28T22:43:15Z | 2020-01-01T20:46:53Z | 2020-01-01T20:46:46Z | CONTRIBUTOR | null | test and 3 line fix for a small bug mentioned in 2 issues (1 closed). test might be expanded, I only checked for the error disappearing
- [x] closes #25760 (and #28956 )
- [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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} | 5 | 2019-10-29T02:03:25Z | 2019-11-02T21:07:09Z | 2019-11-02T19:18:16Z | MEMBER | null | cc @WillAyd @jreback, should be orthogonal to #29228. | {
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} | 2 | 2019-10-29T04:48:49Z | 2019-10-29T15:44:08Z | 2019-10-29T13:09:06Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/28467
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} | 3 | 2019-10-29T09:27:58Z | 2021-05-09T00:48:47Z | 2019-10-29T16:57:57Z | CONTRIBUTOR | null | Errors resolved (from list in #29254):
pandas.DataFrame.filter
pandas.DataFrame.first
pandas.DataFrame.idxmax
pandas.DataFrame.idxmin
pandas.DataFrame.last
pandas.DataFrame.reindex
pandas.DataFrame.pivot_table
pandas.DataFrame.reorder_levels
pandas.DataFrame.unstack
pandas.DataFrame.melt
Validated with `python scripts/validate_docstrings.py`
Referencing issue: #27977
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https://api.github.com/repos/pandas-dev/pandas/issues/29264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29264/events | https://github.com/pandas-dev/pandas/issues/29264 | 513,804,310 | MDU6SXNzdWU1MTM4MDQzMTA= | 29,264 | Inconsistent results between `rolling.corr` and `corr` | {
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] | closed | false | null | [] | null | 2 | 2019-10-29T09:52:23Z | 2020-05-22T16:09:24Z | 2020-05-22T16:09:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
x = pd.Series([0.125, 0.625, 0.5, 0.8125, 0.1875])
y = pd.Series([0.2, 0.2, 0.2, 0.2, 0.2])
print('rolling.corr:', x.rolling(5).corr(y).iloc[-1])
print('corr:', x.corr(y))
```
Output:
```
rolling.corr: -inf
corr: nan
```
#### Problem description
For constant vector (e.g., [0.2, 0.2, 0.2, 0.2, 0.2]), `corr` should be `nan` but not `inf`.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29265/events | https://github.com/pandas-dev/pandas/issues/29265 | 513,859,891 | MDU6SXNzdWU1MTM4NTk4OTE= | 29,265 | Numerical errors in var(), std() | {
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] | closed | false | null | [] | null | 1 | 2019-10-29T11:42:10Z | 2019-10-29T15:23:42Z | 2019-10-29T15:23:31Z | NONE | null | ### In pandas:
```python
df = pd.Series([1,2])
df.var()
```
### Output
```python
0.25
```
### In numpy:
```python
np.var([1,2])
```
### Output
```python
0.5
```
#### Problem description
Why does Pandas Series.var() method return results by default without dividing by N? They don't agree with Numpy. And in mathematics, the results should also divided by N. And I am confused about the effect of the parameter ddof=0 option.
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] | closed | false | null | [] | null | 2 | 2019-10-29T12:36:53Z | 2019-10-30T10:38:06Z | 2019-10-30T10:38:06Z | CONTRIBUTOR | null | - [x] closes #29162
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/29267 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29267/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29267/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29267/events | https://github.com/pandas-dev/pandas/issues/29267 | 513,958,608 | MDU6SXNzdWU1MTM5NTg2MDg= | 29,267 | API: public way to get the "best values" of a Series? | {
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"description": "Requires... | open | false | null | [] | null | 7 | 2019-10-29T14:26:50Z | 2021-07-22T05:39:13Z | null | MEMBER | null | For a use case in pyarrow, I need to get the underlying values of a Series: an ExtensionArray if it is back with one, or otherwise the numpy array.
Is there public API to get this? We have `Series.array`, but this always returns an ExtensionArray, and we have `Series.values`, but this return a numpy array for eg periods (for historical reasons).
For Index, we have the private `Index._values` described in the docstring as the "best array representation". And I think `Series._values` is somewhat similar.
But the question is: do we want a public way to get to this? I am personally not sure we should, as there are still dubious cases (like datetime64/timedelta64, for this one, `Index._values` and `Series._values` is actually different ...).
But if we don't add it, do we have a recommended way for external projects to do this?
(basically it is something like the `extract_array(..., extract_numpy=True)` ?)
cc @TomAugspurger @jbrockmendel @jreback
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"following_url": "https://api.github.com/users/erfannariman/following{/other_use... | null | 5 | 2019-10-29T15:01:42Z | 2021-07-22T05:39:57Z | null | CONTRIBUTOR | null | Since `pandas 0.25.0` we have [named aggregations](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html#groupby-aggregation-with-relabeling).
Which works fine if you do aggregations on single columns. But what if you want to apply aggregations over multiple columns:
**example**:
```python
# example dataframe
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
a b c d group
0 0.751462 0.572576 0.192957 0.921723 0
1 0.070777 0.801548 0.601678 0.344633 0
2 0.112964 0.361984 0.416241 0.785764 1
3 0.380045 0.486494 0.000594 0.608759 1
# aggregations on single columns
df.groupby('group').agg(
a_sum=('a', 'sum'),
a_mean=('a', 'mean'),
b_mean=('b', 'mean'),
c_sum=('c', 'sum'),
d_range=('d', lambda x: x.max() - x.min())
)
a_sum a_mean b_mean c_sum d_range
group
0 0.947337 0.473668 0.871939 0.838150 0.320543
1 0.604149 0.302074 0.656902 0.542985 0.057681
```
But what if we want to calculate the `a.max() - b.max()` while aggregating. That does not seem to work. For example, something like this would make sense:
```python
df.groupby('group').agg(
diff_a_b=(['a', 'b'], lambda x: x['a'].max() - x['b'].max())
)
```
---
So is it possible to do named aggregations on multiple columns? If not, is this in the pipeline for future releases?
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} | 3 | 2019-10-29T16:07:49Z | 2019-10-31T16:03:28Z | 2019-10-31T15:56:19Z | MEMBER | null | - [x] closes #28448
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"de... | closed | false | null | [] | null | 2 | 2019-10-29T19:49:16Z | 2019-12-05T16:35:46Z | 2019-12-05T16:35:46Z | MEMBER | null | ref #29165 the stat_ops benchmarks add a significant amount of time to the ASV runs. The "kendall" correlation method in particular takes over 10 seconds per run in some cases, and there are at least 4 occurrences of this. So for that method alone 40s across 4 runs per benchmark suite would be close to 3 minutes just for those benchmarks
There are a few others mixed in there as well that can be tuned. I think a .2 ms runtime would be ideal:
https://pandas.pydata.org/speed/pandas/#summarylist?sort=1&dir=desc
Of course making that method faster to be in line with the rest of the correlation methods is an option as well and this is mentioned by @dsaxton in #28329. Depending on how much effort that is might be OK to move the benchmark first and align back if ever improved | {
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} | 2 | 2019-10-29T20:41:32Z | 2019-11-03T23:39:06Z | 2019-11-01T14:50:59Z | CONTRIBUTOR | null | This is a follow-up issue of #23018 with the goal of enforcing a consistent approach of importing things from pandas.util.testing inside the **test suite**.
Currently, there are multiple import styles used:
1. `import pandas.util.testing as tm`
2. `from pandas.util.testing import ...`
3. `from pandas.util import testing as tm`
As it is already the dominant import style used and also less prone to circular imports 1. is the prefered option. After migrating all current occurrences of 2. and 3. we'll add a linting rule that enforces this convention.
These are the current files that use 2. (generated with `grep -R -l --include="*.py" "from pandas.util.testing import " pandas/tests`:
- [x] pandas/tests/arrays/sparse/test_array.py #29307
- [x] pandas/tests/computation/test_eval.py #29307
- [x] pandas/tests/frame/test_api.py #29300
- [x] pandas/tests/frame/test_apply.py #29300
- [x] pandas/tests/frame/test_axis_select_reindex.py #29300
- [x] pandas/tests/frame/test_block_internals.py #29300
- [x] pandas/tests/frame/test_combine_concat.py #29300
- [x] pandas/tests/frame/test_dtypes.py #29300
- [x] pandas/tests/frame/test_indexing.py #29301
- [x] pandas/tests/frame/test_missing.py #29301
- [x] pandas/tests/frame/test_mutate_columns.py #29301
- [x] pandas/tests/frame/test_nonunique_indexes.py #29301
- [x] pandas/tests/frame/test_operators.py #29301
- [x] pandas/tests/frame/test_quantile.py #29301
- [x] pandas/tests/frame/test_query_eval.py #29302
- [x] pandas/tests/frame/test_rank.py #29302
- [x] pandas/tests/frame/test_replace.py #29302
- [x] pandas/tests/frame/test_reshape.py #29302
- [x] pandas/tests/frame/test_sort_values_level_as_str.py #29303
- [x] pandas/tests/frame/test_sorting.py #29303
- [x] pandas/tests/frame/test_timeseries.py #29303
- [x] pandas/tests/frame/test_to_csv.py #29303
- [x] pandas/tests/generic/test_frame.py #29307
- [x] pandas/tests/generic/test_generic.py #29307
- [x] pandas/tests/generic/test_series.py #29307
- [x] pandas/tests/groupby/test_categorical.py #29287
- [x] pandas/tests/groupby/test_counting.py #29287
- [x] pandas/tests/groupby/test_groupby.py #29287
- [x] pandas/tests/groupby/test_grouping.py #29287
- [x] pandas/tests/groupby/test_index_as_string.py #29287
- [x] pandas/tests/groupby/test_nth.py #29287
- [x] pandas/tests/groupby/test_timegrouper.py #29287
- [x] pandas/tests/groupby/test_transform.py #29287
- [x] pandas/tests/indexes/datetimes/test_datetime.py #29289
- [x] pandas/tests/indexes/datetimes/test_tools.py #29289
- [x] pandas/tests/indexes/multi/test_astype.py #29289
- [x] pandas/tests/indexes/multi/test_indexing.py #29289
- [x] pandas/tests/indexes/test_base.py #29289
- [x] pandas/tests/indexes/test_category.py #29289
- [x] pandas/tests/indexes/timedeltas/test_partial_slicing.py #29289
- [x] pandas/tests/indexes/timedeltas/test_timedelta.py #29289
- [x] pandas/tests/indexes/timedeltas/test_tools.py #29289
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- [x] pandas/tests/indexing/test_iloc.py #29291
- [x] pandas/tests/indexing/test_indexing.py #29291
- [x] pandas/tests/indexing/test_ix.py #29291
- [x] pandas/tests/indexing/test_loc.py #29291
- [x] pandas/tests/internals/test_internals.py
- [x] pandas/tests/io/excel/test_openpyxl.py #29299
- [x] pandas/tests/io/excel/test_style.py #29299
- [x] pandas/tests/io/excel/test_writers.py #29299
- [x] pandas/tests/io/excel/test_xlrd.py #29299
- [x] pandas/tests/io/excel/test_xlsxwriter.py #29299
- [x] pandas/tests/io/excel/test_xlwt.py #29299
- [x] pandas/tests/io/json/test_compression.py #29299
- [x] pandas/tests/io/json/test_pandas.py #29299
- [x] pandas/tests/io/json/test_readlines.py #29299
- [x] pandas/tests/io/parser/test_textreader.py #29299
- [x] pandas/tests/io/pytables/test_compat.py #29299
- [x] pandas/tests/io/pytables/test_complex.py #29299
- [x] pandas/tests/io/pytables/test_store.py #29299
- [x] pandas/tests/io/pytables/test_timezones.py #29299
- [x] pandas/tests/io/test_clipboard.py #29299
- [x] pandas/tests/io/test_feather.py #29299
- [x] pandas/tests/io/test_gcs.py #29299
- [x] pandas/tests/io/test_html.py #29299
- [x] pandas/tests/io/test_packers.py #29299
- [x] pandas/tests/plotting/common.py #29307
- [x] pandas/tests/plotting/test_datetimelike.py #29307
- [x] pandas/tests/resample/test_base.py #29286
- [x] pandas/tests/resample/test_datetime_index.py #29286
- [x] pandas/tests/resample/test_period_index.py #29286
- [x] pandas/tests/resample/test_resample_api.py #29286
- [x] pandas/tests/resample/test_resampler_grouper.py #29286
- [x] pandas/tests/resample/test_time_grouper.py #29286
- [x] pandas/tests/resample/test_timedelta.py #29286
- [x] pandas/tests/reshape/merge/test_join.py #29285
- [x] pandas/tests/reshape/merge/test_merge.py #29285
- [x] pandas/tests/reshape/merge/test_merge_asof.py #29285
- [x] pandas/tests/reshape/merge/test_merge_index_as_string.py #29285
- [x] pandas/tests/reshape/merge/test_merge_ordered.py #29285
- [x] pandas/tests/reshape/test_concat.py #29285
- [x] pandas/tests/reshape/test_reshape.py #29285
- [x] pandas/tests/series/indexing/test_alter_index.py #29277
- [x] pandas/tests/series/indexing/test_boolean.py #29277
- [x] pandas/tests/series/indexing/test_datetime.py #29277
- [x] pandas/tests/series/indexing/test_iloc.py #29277
- [x] pandas/tests/series/indexing/test_indexing.py #29277
- [x] pandas/tests/series/indexing/test_loc.py #29277
- [x] pandas/tests/series/indexing/test_numeric.py #29277
- [x] pandas/tests/series/test_analytics.py #29273
- [x] pandas/tests/series/test_api.py #29273
- [x] pandas/tests/series/test_apply.py #29273
- [x] pandas/tests/series/test_combine_concat.py #29273
- [x] pandas/tests/series/test_constructors.py #29273
- [x] pandas/tests/series/test_datetime_values.py #29273
- [x] pandas/tests/series/test_internals.py #29273
- [x] pandas/tests/series/test_io.py #29273
- [x] pandas/tests/series/test_missing.py #29273
- [x] pandas/tests/series/test_operators.py #29273
- [x] pandas/tests/series/test_rank.py #29273
- [x] pandas/tests/series/test_sorting.py #29273
- [x] pandas/tests/series/test_timeseries.py #29273
- [x] pandas/tests/test_algos.py #29307
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- [x] pandas/tests/util/test_assert_almost_equal.py #29278
- [x] pandas/tests/util/test_assert_categorical_equal.py #29278
- [x] pandas/tests/util/test_assert_extension_array_equal.py #29278
- [x] pandas/tests/util/test_assert_frame_equal.py #29278
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- [x] pandas/tests/util/test_assert_numpy_array_equal.py #29278
- [x] pandas/tests/util/test_assert_series_equal.py #29278
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} | 3 | 2019-10-29T21:22:07Z | 2019-10-29T23:12:11Z | 2019-10-29T23:12:03Z | CONTRIBUTOR | null | Part of #29272
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Minor typo under the [Dedicated-string-data-type](https://dev.pandas.io/docs/whatsnew/v1.0.0.html#dedicated-string-data-type) warning, it should be `StringDtype is currently considered experimental.` instead of `StringDtype` ~and~ `is currently considered experimental.` | {
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} | 4 | 2019-10-29T22:12:31Z | 2019-10-31T14:43:40Z | 2019-10-31T14:24:03Z | MEMBER | null | Medium-term goal is to separate out tslibs-only parts of these tests to make sure we have sufficient coverage in isolation. | {
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} | [] | closed | false | null | [] | null | 2 | 2019-10-29T23:13:22Z | 2019-10-30T04:39:34Z | 2019-10-30T02:59:51Z | MEMBER | null | After going through the docstrings in the file ```pandas/errors/__init__.py``` I came to a conclusion(Please, correct me if I'm wrong about anything) that there's 3 "Classes"(I have no better word for it).
There's is:
Warning - When the user is attempting to do something "bad" but possible (Having 2 different data types in one column for example).
Exception - ???
Error - The user is attempting to do something impossible.
I'd really like to know what's the main difference between an Exception and an Error, And where and when you'd raise them?
Edit: Not sure if this is the correct place to ask this, LMK where is the right place to address questions. | {
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} | 1 | 2019-10-30T01:41:01Z | 2019-10-30T11:55:16Z | 2019-10-30T11:55:01Z | CONTRIBUTOR | null | Part of #29272
Fyi: the changes were created via a script posted in the issue
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-30T01:52:40Z | 2019-10-30T11:56:26Z | 2019-10-30T11:56:23Z | CONTRIBUTOR | null | Part of #29272
Fyi: All changes are generated from a script posted in the issue.
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] | closed | false | null | [] | null | 3 | 2019-10-30T02:04:16Z | 2020-03-12T11:36:59Z | 2020-03-12T11:36:58Z | NONE | null | I have a dataset in csv and a dictionary created by a classifier. It is necessary to put the key values in place of the keys . I use this code `df.replace(mapping,inplace = True) `and it works fine on 100 lines, but for 2000 and more, I get the error
> "TypeError:“ Cannot compare types 'ndarray (dtype = object)' and 'str' ”.
What am I doing wrong ??
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} | 1 | 2019-10-30T08:43:01Z | 2019-10-30T12:43:49Z | 2019-10-30T11:56:52Z | CONTRIBUTOR | null | - [X] closes #29274
- [ ] tests added / passed
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- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-10-30T09:23:51Z | 2021-10-02T00:03:48Z | 2021-10-02T00:03:48Z | CONTRIBUTOR | null | #### Example
```python
# Creation of two data frames
df=pd.DataFrame(data={'a':[1,2,3], 'b':[4,5,6]})
df2=pd.DataFrame(data={'c':[7,8,9]}, index=[2,1,0])
# Assign a column of the second into the first
df['c']=df2['c']
# As expected
df
#Out[50]:
# a b c
#0 1 4 9
#1 2 5 8
#2 3 6 7
# The .at operator works fine
df.at[0,'c']=10
df
#Out[52]:
# a b c
#0 1 4 10
#1 2 5 8
#2 3 6 7
# We store a copy of the data frame
df3=df.copy()
# df does not change. OK
df
#Out[54]:
# a b c
#0 1 4 10
#1 2 5 8
#2 3 6 7
# Use again the operator .at on df
df.at[1,'c']=11
df
#Out[56]:
# a b c
#0 1 4 10
#1 2 5 8 <-----------KO: should have changed
#2 3 6 7
df.at[1,'c']
# Out[57]: 11 <------------ OK. At operator is not consistent...
# Selection of the series
df['c']
#Out[59]:
#0 10
#1 11 <------------ OK.
#2 7
#Name: c, dtype: int64
# Selection of the data frame with a single column
df[['c']]
#Out[60]:
# c
#0 10
#1 8 <------------ KO
#2 7
```
#### Problem description
I realized that the following suite of operations does not work:
1) assignation of a column to a data frame from an other
2) copy of the data frame using the `.copy()` method
3) use of the `.at` operator on the non copied data frame to change a value in the appended column
It leads to an incoherence between the different selectors of the data frame. I would expect a consistent behaviour between different selection methods or at least a warning to prevent the user to do such actions.
#### Expected Output
I expected the value at position `[1,'c']` in the example to be equal to `11` no matter the selection method used: data frame selection, serie selection, .at operator, etc.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-6-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.5.0
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-10-30T09:39:08Z | 2019-10-31T10:05:03Z | 2019-10-31T08:03:54Z | MEMBER | null | Errors fixed (from the list in #29254 ):
pandas.io.formats.style.Styler.from_custom_template
pandas.io.formats.style.Styler.apply
pandas.io.formats.style.Styler.applymap
pandas.io.formats.style.Styler.set_properties
pandas.io.formats.style.Styler.clear
pandas.io.formats.style.Styler.background_gradient
pandas.io.formats.style.Styler.use
pandas.io.formats.style.Styler.where
Validated the fixes with ```python scripts/validate_docstrings.py```
Refrencing issue: #29254 | {
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] | closed | false | null | [] | null | 1 | 2019-10-30T13:47:11Z | 2021-07-22T05:45:06Z | 2021-07-22T05:45:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
# out is a dataframe with columns A,B,C,D (all integers) and has 390,846 rows
# this is the result of a out.head()
# 46.0 276 76514.0 5.293944e+07
# 46.0 276 23564.0 3.330653e+07
# 46.0 276 70229.0 3.065462e+07
# 46.0 276 43472.0 1.868458e+07
# 46.0 276 72654.0 1.825354e+07
for _ in range(3, 12):
t = out \
.set_index("A") \
.groupby(["B", "C"]) \
.D \
.nlargest(_) \
.reset_index()
print(t.shape[1])
```
#### Problem description
if you run the above with my dataset (`out.shape = (390846, 4)`) you will see that from `_=10` , the method`nlargest` only returns 2 columns and drops the 2 index columns. This doesn't happen between for `_` in the range [1,9] .
#### Expected Output
Seen that I'm just printing the number of columns, the expected output is 4 throughout the loops.
#### Output of ``pd.show_versions()``
<details>
NSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.16.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-10-30T13:58:01Z | 2019-10-31T07:47:36Z | 2019-10-31T07:47:27Z | CONTRIBUTOR | null | Part of #29272
Fyi: the changes were created via a script posted in the issue
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-30T14:02:39Z | 2019-10-31T07:49:06Z | 2019-10-31T07:48:57Z | CONTRIBUTOR | null | Part of #29272
Fyi: the changes were created via a script posted in the issue
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} | 1 | 2019-10-30T14:11:57Z | 2019-10-31T07:53:08Z | 2019-10-31T07:52:51Z | CONTRIBUTOR | null | Part of #29272
Fyi: All changes are generated from a script posted in the issue.
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] | closed | false | null | [] | null | 13 | 2019-10-30T14:16:32Z | 2020-04-24T03:09:42Z | 2020-02-12T00:52:46Z | CONTRIBUTOR | null | ## Background:
Multi-row and/or multi-column subplots can utilize shared axes.
An external share happens at axis creation when a sharex or sharey
parameter is specified.
An internal share, or twinning, occurs when an overlayed axis is created
by the `Axes.twinx()` or `Axes.twiny()` calls.
The two types of sharing can be distinguished after the fact in the
following manner. If two axes sharing an axis also have the same
position, they are internally sharing an axis, they are twinned. If their positions are different, they are externally sharing the common axis.
For externally shared axes Pandas automatically removes tick labels for
all but the last row and/or first column in
`./pandas/plotting/_matplotlib/tools.py`'s function `_handle_shared_axes()`.
## The problem:
`_handle_shared_axes()` should be interested in externally shared axes,
whether or not they are also twinned. It should, but **does not**, ignore
axes which are only twinned.
Which means that twinned-only axes wrongly lose their tick labels.
## The cure:
This commit introduces `_ignore_colocated_axes()` which ignores any
axes in a list which have the same position as the given axis. It uses
this function to envelope calls from `_handle_shared_axes()` to
`Axes.get_shared_{x,y}_axes().get_siblings(ax)`.
## The demonstration test case:
Note especially the axis labels (and associated tick labels).
```python
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Create data
df = pd.DataFrame({'a': np.random.randn(1000),
'b': np.random.randn(1000)})
# Create figure
fig = plt.figure()
plots = fig.subplots(2, 3)
# Create *externally* shared axes
plots[0][0] = plt.subplot(231, sharex=plots[1][0])
plots[0][2] = plt.subplot(233, sharex=plots[1][2])
# Create *internally* shared axes
twin_ax1 = plots[0][1].twinx()
twin_ax2 = plots[0][2].twinx()
# Plot data to primary axes
df['a'].plot(ax=plots[0][0], title="External share only").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][0])
df['a'].plot(ax=plots[0][1], title="Internal share (twin) only").set_xlabel("this label should always be visible")
df['a'].plot(ax=plots[1][1])
df['a'].plot(ax=plots[0][2], title="Both").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][2])
# Plot data to twinned axes
df['b'].plot(ax=twin_ax1, color='green')
df['b'].plot(ax=twin_ax2, color='yellow')
# Do it
plt.show()
```
---
- [ ] closes #27812
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-10-30T14:19:15Z | 2019-10-31T07:55:28Z | 2019-10-31T07:55:23Z | CONTRIBUTOR | null | Part of #29272
Fyi: All changes are generated from a script posted in the issue.
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] | closed | false | null | [] | null | 3 | 2019-10-30T14:21:49Z | 2021-07-22T05:46:36Z | 2021-07-22T05:46:36Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# generate some data and create dataframe
col = {'x': []}
for i in range(10):
col['x'].append(18446744073709500000 - i)
df = pd.DataFrame.from_dict(col, dtype='uint64')
# see the dtype:
print(df.dtypes, end='\n\n')
# see correct and expected behaviour:
print(df.x * -1, end='\n\n')
# see possibly inconsistent behaviour:
print(-df.x, end='\n\n')
```
#### Problem description
please see the example: in two cases which are expected (sorry if the expectation is wrong) to generate the same results we see huge difference. In our case we didn't check the type of a column and assigned it to itself but with a minus value.
#### Expected Output
Probably the expected behaviour should match the logic here: https://github.com/pandas-dev/pandas/issues/15832
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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} | 3 | 2019-10-30T14:31:30Z | 2019-10-31T07:56:24Z | 2019-10-31T07:56:20Z | CONTRIBUTOR | null | Part of #29272
Fyi: All changes are generated from a script posted in the issue.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 12 | 2019-10-30T14:56:55Z | 2019-11-01T03:49:58Z | 2019-10-31T17:45:05Z | MEMBER | null | Motivation: asv runs take way too long. One way to get around that is to make it easier to identify\* which benchmarks to run for a given PR. To that extent, if we can identify benchmarks that don't rely on anything outside of tslibs\**, then a PR that doesn't touch that code can skip those benchmarks. We can do the same thing for other low-dependency modules like _libs.algos.
\* Ideally in an automated way, but I'm not there yet.
\*\* Here and elsewhere, "tslibs" informally includes tseries.offsets and parts of tseries.frequences (until we can get all of that into the tslibs directory)
@TomAugspurger will moving benchmarks cause asv to start new time series, or will it successfully link up the new location to the old? | {
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} | 2 | 2019-10-30T15:33:56Z | 2019-11-02T15:32:27Z | 2019-11-02T15:30:11Z | MEMBER | null | instead of having `except Exception` in many places, we have it in one place, with one helpful comment | {
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} | 2 | 2019-10-30T16:48:32Z | 2020-02-28T19:57:10Z | 2019-10-31T17:04:09Z | CONTRIBUTOR | null | More SS06 (Summary should fit in a single line) errors fixed (#29254):
The following docstrings were examined and fixed:
```
pandas.DataFrame
pandas.DataFrame.transform
pandas.DataFrame.corrwith
pandas.DataFrame.describe
pandas.DataFrame.kurt
pandas.DataFrame.kurtosis
pandas.DataFrame.skew
pandas.DataFrame.align
pandas.DataFrame.drop_duplicates
pandas.DataFrame.duplicated
```
Validated with python scripts/validate_docstrings.py
Also referencing issue: #27977 | {
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} | 4 | 2019-10-30T18:42:05Z | 2019-10-31T01:02:04Z | 2019-10-31T01:02:01Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 2 | 2019-10-30T19:11:07Z | 2020-03-02T13:59:23Z | 2020-03-02T13:59:23Z | NONE | null | #### Code Sample
```python
from pandas import read_pickle
df = read_pickle('test.txt')
print(df)
df.set_index(['ElementID', 'NodeID', 'Location']) # fails
#df.set_index(['ElementID']) # also fails
```
The pickle file (that I renamed to be a .txt file):
[test.txt](https://github.com/pandas-dev/pandas/files/3790733/test.txt)
#### Problem description
I'm trying to set a MultiIndex on a DataFrame. I'm getting an error saying that the index (iblockno) is not an integer array, except it is. Looking at the point in the code Searching for the error message:
block = self.blocks[iblockno]
TypeError: only integer scalar arrays can be converted to a scalar index
brought me to `pandas\core\internals\managers.py", line ~977`. I split out iblockno and iblockloc to see understand the error better.
```python
def iget(self, i):
iblockno = self._blknos[i]
iblockloc = self._blklocs[i]
block = self.blocks[iblockno] # error is here
values = block.iget(iblockloc)
```
where:
iblockno
>>> array([1], dtype=int64)
What I'm confused on is the data is an integer array, so shouldn't I not be getting an error?
#### Expected Output
The dataframe with a MultiIndex of ElementID, NodeID, and Location.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2019-10-30T22:54:05Z | 2019-11-07T21:01:34Z | 2019-11-07T20:48:25Z | MEMBER | null | adding an index to the exception is a nice thought, but ultimately adds more complication than information.
The `except AssertionError`s were needed back when the lines below were `except Exception`, but those have been made more specific, so these are no longer needed | {
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Fixed docstrings for the following:
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} | 6 | 2019-10-31T01:00:28Z | 2019-11-03T21:42:27Z | 2019-11-03T20:46:23Z | MEMBER | null | get rid of is_lexsorted_for_tuple while we're at it | {
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} | 1 | 2019-10-31T06:39:04Z | 2019-10-31T08:07:43Z | 2019-10-31T08:06:04Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/23859 | {
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} | 1 | 2019-10-31T09:05:02Z | 2019-10-31T13:10:40Z | 2019-10-31T13:09:48Z | CONTRIBUTOR | null | Part of #29272
Fyi: All changes are generated from a script posted in the issue.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Fyi: When the remaining PRs of this are merged, I'll add another one to add the linting rule - then we can close the issue | {
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"descript... | closed | false | null | [] | null | 2 | 2019-10-31T09:50:47Z | 2019-11-01T22:43:02Z | 2019-11-01T22:42:57Z | NONE | null | I was confused with **qcut** after runing the following codes:
```python
s = pd.Series(range(19))
qc = pd.qcut(s,
q=[0., .1, .3, .7, .9, 1.],
labels=[.8, .9, 1., 1.1, 1.2])
qc.to_numpy()
```
#### Problem description
after running above codes, I get the result.( **please note, there is two 1.2, seven 1.**)
```python
array([0.8, 0.8, 0.9, 0.9, 0.9, 0.9, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1.1, 1.1, 1.1, 1.1, 1.2, 1.2])
```
As my supposed, the results should be:( **one 1.2, eight 1.**)
```python
array([0.8, 0.8, 0.9, 0.9, 0.9, 0.9, 1., 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1.1, 1.1, 1.1, 1.1, 1.2])
```
so, could you please help me to get the expected result?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
</details>
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"description": ""... | open | false | null | [] | null | 4 | 2019-10-31T11:07:33Z | 2021-07-22T05:47:15Z | null | MEMBER | null | How can you create a copy of the DataFrame without copying the actual data, but having a new DataFrame that when updated (not in place) does not modify the original ("shallow copy")? And how is this expected to behave?
I suppose that in technical terms, this would be a new BlockManager that references the same arrays?
I ran in the above questions, and actually didn't know a clear answer. The context was: I wanted to replace one column of a DataFrame, but without modifying the original one. And so was wondering if I could do that without making a full copy of the DataFrame (as in theory this is not needed, and I just wanted to update one object column before serializing).
---
So you can do something like this with `copy(deep=False)`. Let's explore this somewhat:
Making a normal (deep) and shallow copy:
```
In [1]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [.1, .2, .3]})
In [2]: df_copy = df.copy()
In [3]: df_shallow = df.copy(deep=False)
```
Modifying values in place works as expected: for the copy it does not change the original df, for the shallow copy it does:
```
In [4]: df_copy.iloc[0,0] = 10
In [5]: df_shallow.iloc[1,0] = 20
In [6]: df
Out[6]:
a b
0 1 0.1
1 20 0.2
2 3 0.3
```
Overwriting a full column, however, becomes more tricky (due to our BlockManager ...):
```
# this updates the original df
In [7]: df_shallow['a'] = [10, 20, 30]
In [8]: df
Out[8]:
a b
0 10 0.1
1 20 0.2
2 30 0.3
# this does not update the original
In [9]: df_shallow['b'] = [100, 200, 300]
In [10]: df_shallow
Out[10]:
a b
0 10 100
1 20 200
2 30 300
In [11]: df
Out[11]:
a b
0 10 0.1
1 20 0.2
2 30 0.3
```
This is of course somewhat expected if you know the internals: if the new column is of the same dtype, it seems to modify the array of the block in place, while if it needs to create a new block (because the dtype changed on assignment), the reference with the old data is broken and it doesn't modify the original dataframe.
While writing this down, I am realizing that my question is maybe more: *should assigning a column (`df['a'] = ..`) be seen as an in-place modification of your dataframe that has impact through shallow copies?*
Because in reality, `df['a']` cannot always happen in place (if you are overwriting with a different dtype), this gives rather inconsistent and surprising behaviour depending on the dtypes.
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https://api.github.com/repos/pandas-dev/pandas/issues/29310 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29310/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29310/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29310/events | https://github.com/pandas-dev/pandas/issues/29310 | 515,393,259 | MDU6SXNzdWU1MTUzOTMyNTk= | 29,310 | HDF file compression not working | {
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"gi... | null | 20 | 2019-10-31T12:29:24Z | 2021-07-22T05:48:31Z | null | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/pull/28890#pullrequestreview-304361199
While updating the [performance comparison part of the IO docs](https://dev.pandas.io/docs/user_guide/io.html#performance-considerations) it was found that compressed size values for .hdf file formats were the same as uncompressed .hdf file formats.
```
24009288 Oct 10 06:43 test_fixed.hdf
24009288 Oct 10 06:43 test_fixed_compress.hdf
24458940 Oct 10 06:44 test_table.hdf
24458940 Oct 10 06:44 test_table_compress.hdf
```
This seems to be caused by the next lines saving the same files:
```python
df.to_hdf('test.hdf', 'test', mode='w')
df.to_hdf('test.hdf', 'test', mode='w', complib='blosc')
```
We need to see why the `complib` parameter is being ignored, and fix it so the `hdf5` file is saved compressed when used.
CC @datapythonista | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29311/events | https://github.com/pandas-dev/pandas/issues/29311 | 515,419,856 | MDU6SXNzdWU1MTU0MTk4NTY= | 29,311 | PERF: regression in MultiIndex get_loc indexing performance | {
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} | 3 | 2019-10-31T13:08:40Z | 2019-11-07T21:20:57Z | 2019-11-07T21:20:57Z | MEMBER | null | From our benchmarks (`GetLoc` class in https://github.com/pandas-dev/pandas/blob/master/asv_bench/benchmarks/multiindex_object.py):
```
In [65]: mi_med = pd.MultiIndex.from_product(
...: [np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
...: )
In [66]: %timeit mi_med.get_loc((999, 9, "A"))
9.58 µs ± 106 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [67]: pd.__version__
Out[67]: '0.25.0'
```
vs
```
In [18]: mi_med = pd.MultiIndex.from_product(
...: [np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
...: )
In [19]: %timeit mi_med.get_loc((999, 9, "A"))
34.7 µs ± 454 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [20]: pd.__version__
Out[20]: '0.26.0.dev0+691.g157495696'
```
Not directly sure what recently changed related to MultiIndexes.
And the benchmark suite is giving a rather broad range: https://github.com/pandas-dev/pandas/compare/2b284543...7c8c8c81 (because the benchmarks were not running for a while I suppose) | {
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} | 2 | 2019-10-31T16:03:09Z | 2020-01-16T00:33:38Z | 2019-10-31T18:32:02Z | MEMBER | null | This includes pieces of #27826 except for the whatsnew note, which appeared in 0.25.1. Not sure if we want to move that here or keep as is, but test and implementation were included here | {
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