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} | 0 | "2021-10-08T03:53:14Z" | "2021-11-20T21:49:09Z" | null | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
np.mimum.reduce(frame) -> return Series instead of ndarray
np.maximum.reduce(series[dt64]) -> return Timestamp instead of np.datetime64
np.maximum.reduce(series[td64]) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(DTA) -> return Timestamp instead of np.datetime64
np.maximum.reduce(TDA) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(DTI) -> return Timestamp instead of np.datetime64
np.maximum.reduce(TDI) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(integer_array) works instead of raising NotImplementedError
np.maximum.reduce(floating_array) works instead of raising NotImplementedError
np.maximum.reduce(Series[Int]) works instead of raising NotImplementedError
np.maximum.reduce(Series[Float]) works instead of raising NotImplementedError
Ditto for np.minimum.reduce | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43924/events | https://github.com/pandas-dev/pandas/issues/43924 | 1,020,824,987 | I_kwDOAA0YD8482I2b | 43,924 | BUG: `multicolumn` of `to_latex` does not work correctly when `index==False` | {
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- [X] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame([[1,2,3,4]], columns=pd.MultiIndex.from_tuples([('1', 'a'), ('1', 'b'), ('2', 'a'), ('2', 'b'), ]))
print(df.to_latex(index=False, multicolumn=True,))
\begin{tabular}{rrrr}
\toprule
1 & \multicolumn{2}{l}{2} \\
a & b & a & b \\
\midrule
1 & 2 & 3 & 4 \\
\bottomrule
\end{tabular}
```
### Issue Description
The top level of multicolumns are not located correctly.
### Expected Behavior
\begin{tabular}{rrrr}
\toprule
\multicolumn{2}{l}{1} & \multicolumn{2}{l}{2} \\
a & b & a & b \\
\midrule
1 & 2 & 3 & 4 \\
\bottomrule
\end{tabular}
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: 0.10.0
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43925/events | https://github.com/pandas-dev/pandas/pull/43925 | 1,021,115,247 | PR_kwDOAA0YD84s8fmY | 43,925 | Add Zstandard compression support | {
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] | open | false | null | [] | null | 3 | "2021-10-08T13:44:52Z" | "2021-11-16T22:39:54Z" | null | NONE | null | - [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Haven't found any discussion about Zstandard so far but I think it would be a valuable addition to the Pickle compression options. | {
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} | 5 | "2021-10-08T13:50:09Z" | "2021-10-13T23:00:22Z" | "2021-10-13T15:31:48Z" | CONTRIBUTOR | null | Fixes a bug that no one has reported and probably no one cares about. But overlaps slightly with #42947, so I thought that I would make it a separate PR.
Current behaviour: `df.groupby(df.iloc[1], axis=1).nth(0)` crashes ungracefully. | {
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} | 0 | "2021-10-08T14:51:54Z" | "2021-10-09T01:10:01Z" | "2021-10-09T01:10:01Z" | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
index = pd.date_range('2020', periods=4, freq='s')
inc = pd.Series([1,1,1,1], index=index).rolling('3s', center=True).sum()
dec = pd.Series([1,1,1,1], index=index[::-1]).rolling('3s', center=True).sum()
assert np.all(inc.values == dec.values)
>>> inc
2020-01-01 00:00:00 2.0
2020-01-01 00:00:01 3.0
2020-01-01 00:00:02 3.0
2020-01-01 00:00:03 2.0
Freq: S, dtype: float64
>>> dec
2020-01-01 00:00:03 NaN
2020-01-01 00:00:02 3.0
2020-01-01 00:00:01 3.0
2020-01-01 00:00:00 2.0
Freq: -1S, dtype: float64
```
### Issue Description
the first value is a `NaN` when rolling over a decreasing index with `center=True`, but it shouldnt. This happens for ever value of `closed`.
AFAIK, this comes from the cython implementation of `calculate_valiable_window_bounds`.
I prepared a MR for this.
### Expected Behavior
no `NaN` as first value.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-88-generic
Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.0.0
Cython : None
pytest : 6.2.5
hypothesis : 6.17.4
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.54.0
</details>
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} | 1 | "2021-10-08T14:52:48Z" | "2021-10-09T01:10:20Z" | "2021-10-09T01:10:01Z" | CONTRIBUTOR | null | - [x] closes #43927
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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] | open | false | null | [] | null | 7 | "2021-10-08T15:34:59Z" | "2021-10-16T03:57:44Z" | null | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
>>> import pandas as pd
>>> df = pd.DataFrame([60000.0, 60000.0], dtype="float16")
```
```Python
/Users/username/opt/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:47: RuntimeWarning: overflow encountered in reduce
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
0 inf
dtype: float16
```
### Issue Description
I have read the source code of pandas recently, it seems that when pandas implement DataFrame.mean() method, it calculate the tot = elements.sum() first, and then divide by the count_of_elements secondly to get the mean_value. This could cause a overflow error even if the result shouldn't be a number out of dtype range.
### Expected Behavior
1. The example should return 60000.0
2. For any Float dtype Series/DataFrame, the mean method should work and do not overflow.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Jun 22 19:37:08 PDT 2021; root:xnu-4903.278.70~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.22
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | [] | open | false | null | [] | null | 8 | "2021-10-08T17:02:57Z" | "2021-11-21T04:38:37Z" | null | MEMBER | null | Still TODO
- [x] <s>288</s>15 not-yet-passing tests (with --skip-slow --skip-db)
- [ ] flesh out tests/extension/base/ea_index.py
- [x] <s>deprecation cycle</s> (decided we can probably just change)
- [ ] optimize EAIndexEngine/NullableIndexEngine once #43870 is resolved
- [x] EA.putmask implementation ATM is just a stub (and needs tests)
- [ ] whatsnew
cc @jorisvandenbossche @TomAugspurger
xref https://github.com/pandas-dev/pandas/issues/39133 | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-10-09T02:06:07Z" | "2021-10-10T18:12:42Z" | "2021-10-10T18:12:42Z" | CONTRIBUTOR | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
- https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.replace.html
- https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.replace.html
- and more
### Documentation problem
As title.

### Suggested fix for documentation
In using sphinx experience, the following could fix that.
Note that `numeric` and `replaced` have the same indent level.
```diff
- numeric: numeric values equal to `to_replace` will be
- replaced with `value`
+ replaced with `value`
```
```
- numeric: numeric values equal to `to_replace` will be
replaced with `value`
```
My local test result.

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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Multi-column selection when the column labels are nested tuples, for example atomic labels of form `((1,),)` or MultiIndex having tuples at each level as reported in the issue raised error due to a condition in asarray_tuplesafe.
A key list of form `[((1,))]` converts to `[[[1]]]` through the call to np.array, resulting in ndim>2. However, only the case ndim==2 was further handled, causing a multi-dimensional array to be returned and subsequently an error.
This extends the ndim==2 condition for nested-tuple keys, but excludes multi-dimensional arrays so that errors are raised in accordance with [test_getitem_ndarray_3d](https://github.com/pandas-dev/pandas/blob/d30aeeba0c79fb8e4b651a8f528e87c3de8cb898/pandas/tests/indexing/test_indexing.py#L73).
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] | closed | false | null | [] | null | 4 | "2021-10-09T09:29:03Z" | "2021-11-01T18:41:41Z" | "2021-10-31T00:17:45Z" | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
def map_func(dt):
if dt.month <= 2:
return dt.year + dt.month - 1
return dt.year + dt.month
dict = {
"Name": ["Tom", "Joseph"],
"Date": ["2021-10-01", "2021-11-01"],
}
df = pd.DataFrame(dict)
df = df.set_index("Date")
df.index = pd.to_datetime(df.index)
print(f"Result: {df.index.map(map_func)}")
```
### Issue Description
Running the attached program in debugpy debugger produces different results than if I run it plainly.
Running it plainly with `/usr/bin/env /usr/bin/python3.9 test.py` produces output as expected:
`Result: Int64Index([2031, 2032], dtype='int64', name='Date')`
Running it in debugpy with:
`cd $USER/test_error ; /usr/bin/env /usr/bin/python3.9 $USER/.vscode/extensions/ms-python.python-2021.10.1317843341/pythonFiles/lib/python/debugpy/launcher 37319 -- $USER/test_error/test.py`
produces a crash on line 5. For some reason dt.month is the full index:
`DatetimeIndex(['2021-10-01', '2021-11-01'], dtype='datetime64[ns]', name='Date', freq=None)`
and not only a single date.
Note that the debugpy command above is the auto generated command when using the VS code debugger (Run -> Start Debugging, or F5). Note that it also assumes that you are in the '$USER/test_error' directory.
VS code version is 1.61.0 and Python extension version is v2021.10.1317843341.
I'm posting it here, but I'm not sure if it's a pandas, debugger or python issue.
### Expected Behavior
No crash when running in debugpy, or at least the same result
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-37-generic
Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.2.0
Cython : 0.29.24
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : None
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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] | open | false | null | [] | null | 2 | "2021-10-09T11:22:06Z" | "2021-10-11T19:53:05Z" | null | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
t = pd.to_datetime(["2021-01-01", "2021-01-03", "2021-01-04"])
print(t)
dt = t.to_series().diff()
print(dt)
dt.plot.hist()
```
### Issue Description
I get this crash from the `dt.plot.hist()` call:
```
% python crash.py
DatetimeIndex(['2021-01-01', '2021-01-03', '2021-01-04'], dtype='datetime64[ns]', freq=None)
2021-01-01 NaT
2021-01-03 2 days
2021-01-04 1 days
dtype: timedelta64[ns]
zsh: segmentation fault python crash.py
```
### Expected Behavior
No segfault. Give a plot or some informative exception.
### Installed Versions
This is with Python 3.9 on MacOS installed via conda-forge.
<details>
% cat environment.yml
name: cement
channels:
- conda-forge
- nodefaults
dependencies:
- python==3.9
- jupyterlab==3.1
- pandas==1.3
- scikit-learn==1.0
- missingno
- matplotlib
- seaborn==0.11
- dtale==1.56
- pandas-profiling
- sweetviz==2.1
- hcrystalball==0.1.10
- pip
- pip:
- flaml
</details>
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-10-09T17:00:32Z" | "2021-10-11T16:06:03Z" | "2021-10-11T16:05:59Z" | CONTRIBUTOR | null | Adds simple examples to docstrings for `add_categories`, `remove_categories`, and `remove_unused_categories`.
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-10-09T22:42:18Z" | "2021-10-13T02:05:45Z" | "2021-10-13T02:05:45Z" | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
index = pd.DatetimeIndex([
'2020-01-01', '2020-01-01',
'2020-01-02', '2020-01-02',
'2020-01-03', '2020-01-03',
'2020-01-04', '2020-01-04',
])
s = pd.Series([1]*8, index=index, dtype=float)
s.rolling('2D', center=True).sum()
>> Out[6]:
2020-01-01 4.0
2020-01-01 4.0
2020-01-02 3.0
2020-01-02 4.0
2020-01-03 3.0
2020-01-03 4.0
2020-01-04 2.0
2020-01-04 2.0
dtype: float64
```
### Issue Description
the right window bound is not respected for **centered** datetime windows.
This differs somewhat from issue #20712 and does not change the behavior discussed there..
i prepared a PR for this.
### Expected Behavior
```python
s.rolling('2D', center=True).sum()
Out[4]:
2020-01-01 4.0
2020-01-01 4.0
2020-01-02 4.0
2020-01-02 4.0
2020-01-03 4.0
2020-01-03 4.0
2020-01-04 2.0
2020-01-04 2.0
dtype: float64
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : d30aeeba0c79fb8e4b651a8f528e87c3de8cb898
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-88-generic
Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+857.gd30aeeba0c
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.2.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.1
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.10.0
fastparquet : 0.7.1
gcsfs : 2021.10.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.10.0
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
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} | 3 | "2021-10-09T22:44:19Z" | "2021-10-13T02:05:57Z" | "2021-10-13T02:05:45Z" | CONTRIBUTOR | null | ….
- [x] closes #43944
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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No, the feature is not related to a problem.
#### Describe the solution you'd like
It should be possible to perform the `astype()` operation directly into a dataframe passing an argument `inplace` as True.
#### API breaking implications
The API should not break once the default argument should be false, and the function will keep returning the same result as before.
#### Describe alternatives you've considered
Creating a new function exclusively for doing this kind of operation.
#### Additional context
```python
df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : [1.0, 2.0, 3.0]})
df.astype('float32', inplace=True)
```
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- [] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [X] This commit adds an argument to make it possible to convert the types of a dataframe inplace.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [X] This commits adds the possibility of performing the operation of astype directly in a dataframe using the inplace parameter.
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} | 8 | "2021-10-09T23:34:51Z" | "2021-10-19T15:01:45Z" | "2021-10-19T14:59:57Z" | CONTRIBUTOR | null | closes #25472
- [x] closes #xxxx
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``` python import pandas
d = pandas.DataFrame([['hi,hi 2'],['bye','bye 2']], columns=['one','two'])
print(d.one.str.startswith(d.two))
``` | {
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} | 4 | "2021-10-10T02:23:24Z" | "2021-11-17T02:11:42Z" | "2021-11-17T02:11:38Z" | CONTRIBUTOR | null | Fixes #41610, rebased on top of #43855.
This PR does a few things:
1. break `FilePathOrBuffer` apart to not mix basic types and generics
2. use protocols instead of union of specific classes
3. define many fine-grained protocols for the to/read methods/functions
I tested that the protocols are sufficient (need no additional attributes/methods) using mock classes with:
- read_csv (python/c/pyarrow; w/wo compression) and to_csv (w/wo compression)
- to_json and read_json (each w/wo compression)
- to_pickle, read_pickle
- to_excel (openpyxl/xlsxwriter) and read_excel (openpyxl)
- to_stata and read_stata
Future: use many overloads for `get_handle` to return the (wrapped) fine-grained protocols. | {
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} | 1 | "2021-10-10T04:03:53Z" | "2021-10-10T21:51:37Z" | "2021-10-10T21:41:14Z" | MEMBER | null | ```
import pandas as pd
idx = pd.Index([4, 5, 6])
%timeit idx.insert(1, 19)
26.7 µs ± 299 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
15.5 µs ± 185 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
``` | {
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} | 1 | "2021-10-10T05:09:02Z" | "2021-10-10T12:48:02Z" | "2021-10-10T12:47:36Z" | NONE | null | The *Python Version 3.10* is being released! I request you to update pandas to the 3.10 version of python
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} | 1 | "2021-10-10T10:17:20Z" | "2021-10-10T18:12:46Z" | "2021-10-10T18:12:43Z" | CONTRIBUTOR | null | - [x] closes #43937
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] Cancel replace's doc list item text bold effect, detail could see below.
From

To

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} | 2 | "2021-10-10T14:37:20Z" | "2021-10-31T00:20:04Z" | "2021-10-31T00:20:04Z" | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/pull/43952/checks?check_run_id=3849531985 as an exxample
maybe something got released / renamed? looks like an issue with coverage
```
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 126, in pytest_load_initial_conftests
plugin = CovPlugin(options, early_config.pluginmanager)
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 175, in __init__
self.start(engine.DistMaster)
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 199, in start
self.cov_controller.start()
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/engine.py", line 44, in ensure_topdir_wrapper
return meth(self, *args, **kwargs)
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/engine.py", line 267, in start
self.cov.start()
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/control.py", line 532, in start
self._init()
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/control.py", line 275, in _init
self._plugins = Plugins.load_plugins(self.config.plugins, self.config, self._debug)
File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/plugin_support.py", line 41, in load_plugins
__import__(module)
ModuleNotFoundError: No module named 'Cython'
```
cc @pandas-dev/pandas-core | {
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} | 2 | "2021-10-10T20:02:20Z" | "2021-10-12T22:20:29Z" | "2021-10-12T22:20:29Z" | CONTRIBUTOR | null | # Add `notin` convenience method to `DataFrame`/`Series` classes
This may already have been discussed, but I couldn't find any existing issue/enhancement with this title.
It's trivial to implement, but I think it might be syntactically helpful to write
```python
df.notin(df1)
```
instead of
```python
~df.isin(df1)
```
especially in longer expressions where the boolean array is being used to slice a dataframe.
#### Describe the solution you'd like
Add a convenience function which returns the `not` value of `df.isin`
#### API breaking implications
No impact on API.
#### Additional context
This has already been done for `isna`/`notna`, and I find it very helpful—just borrowing the same idea.
Copied from `frame.py`:
```python
@doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
def isna(self) -> DataFrame:
result = self._constructor(self._mgr.isna(func=isna))
return result.__finalize__(self, method="isna")
@doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
def notna(self) -> DataFrame:
return ~self.isna()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/43960 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43960/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43960/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43960/events | https://github.com/pandas-dev/pandas/issues/43960 | 1,022,092,004 | I_kwDOAA0YD8486-Lk | 43,960 | ENH/Discussion: "event"/"per sample" weights support | {
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] | closed | false | null | [] | null | 3 | "2021-10-10T21:06:46Z" | "2021-10-16T04:08:20Z" | "2021-10-16T04:08:20Z" | NONE | null | #### Why this discussion
Having looked around, I did to my surprise not find any real discussion on per sample weights for pandas DataFrames and therefore opening this thread to have a discussion on it. (*if I am mistaken and the discussion exists somewhere, I must have missed it and this can be gladly closed*)
#### Per sample weights
A datapoint can have a weight assigned to it reflecting the "count" of that point. Weighted data samples are not uncommon in data science (and many APIs in machine learning such as Scikit-learn, Keras, ...) and are an essential part of the data (as far as that a data point can be regarded as a tuple of two points: the data and the weight).
A data weight changes many things fundamentally: plotting of a histogram or scatter plot (or others), calculation of quantities such as mean, std, ...
Pandas, AFAIK, does not offer any support of dealing with weights and calculating e.g. a variance means that one needs to use a function from another library.
#### What it could look like
DataFrame could take an additional argument "weights" and have an attribute with the same name that is technically a Series and behaves like another column in the dataframe (broadcasting, index) but is not accessible over the normal indexing.
A lot of the behavior is straight forward (and has his open questions):
- any kind of slicing will always return a weighted dataframe as well (but what about concat? like two different columns)
- plots will use weights (but what if weights can't be used?)
- calculated quantities will use the weighted version (what if not available? Error?)
- Other libraries (such as sklearn) could access them directly
#### API breaking implications
Since it would constitute a completely new feature, having errors if it can't be handled - or also other considerations of changed behavior - would *not break any existing code* (modulo user defined attribute "weight", but these users will likely want the new feature)
#### Describe alternatives you've considered
This is of course always achievable in other ways such as calling matplotlib, passing a weights argument explicitly etc. But this can be said about anything in pandas, given that it here to help with tabular data.
In fact that would be the main reason to kickoff the discussion: if one has weighted data, pandas (AFAIK) does not support them at all, as it doesn't know about them (in the sense that many features cannot just be used). Adding them would make pandas a platform that can also handle weighted data.
#### Discussion
Needless to say that there are many cases that will need to be sorted out, needless to say that there are many fundamental objections and to be clear: this post is not saying we *should* introduce them. But it discusses it, since
- weights are a common occurrence in data science
- weights are an _essential_ part of a dataset as they change the meaning and a lot of functionality. They are not merely a specialized attribute that can be handled as meta-data but fundamentally part of a data point.
- pandas makes analysis of (tabular) data easier by offering handles for it, yet (AFAIK) does not support weights at all, offering only a minimal help with weighted data
- It doesn't break _any_ backwards compatibility and could be *gradually* introduced (with methods first erroring if weights are not yet supported).
I am not clear on a side myself: It seems to me on the same time an unreasonably big change in so many places that will, realistically, not occur, yet at the same time _actually_ absolutely save (no break, not every feature needs to be supported, no conflict) and a fundamental property to data.
What are your thoughts on this? | {
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} | 2 | "2021-10-10T21:42:10Z" | "2021-10-12T22:20:33Z" | "2021-10-12T22:20:29Z" | CONTRIBUTOR | null | - [x] closes #43959
Updated relevant docs to show usage of ~ to emulate `notin` operation.
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} | 0 | "2021-10-10T21:48:33Z" | "2021-10-11T22:21:06Z" | "2021-10-11T22:06:08Z" | MEMBER | null | - [ ] closes #xxxx
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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- [x] tests added / passed
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I could not find a suitable place to add a `whatsnew` entry, if there is one I will push another commit. | {
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} | 4 | "2021-10-11T02:59:44Z" | "2021-10-15T02:02:53Z" | "2021-10-12T22:28:00Z" | CONTRIBUTOR | null | - [x] closes #43937
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] Cancel nlargest and nsmallest's doc list item text bold effect, detail could see below.
From

To
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
# Setting column data with different dtype on shallow copy causes original to be unchanged
original = pd.DataFrame({'a': ['initial string data']})
shallow = original.copy(deep=False)
shallow['a'] = 5 # Set column to integer dtype
original['a'][0]
>> 'initial string data'
# Whereas if new data has same dtype as existing, original DF is updated too
original = pd.DataFrame({'a': ['initial string data']})
shallow = original.copy(deep=False)
shallow['a'] = 'new string data' # Set column to different str/object value
original['a'][0]
>> 'new string data'
```
### Issue Description
Setting data of same dtype as existing column in shallow DF copy causes change to propagate to original DF. However this does not happen if new data has different dtype.
### Expected Behavior
Assigning column data in DF shallow copy should not also cause change in original DF (regardless of dtype)
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Australia.1252
pandas : 1.2.3
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : 0.29.24
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.54.0
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Doc improvement - added the example section for pandas.DataFrame.to_period
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 7 | "2021-10-11T13:33:38Z" | "2021-10-15T17:10:11Z" | "2021-10-12T14:27:12Z" | NONE | null | La instalacion falla, para la ultima version de pandas, e incluso empieza a buscar otra version y tampoco la encuentra una que funcione todas caen.
C:\Users\ufx>pip install pandas
Collecting pandas
Using cached pandas-1.3.3.tar.gz (4.7 MB)
Installing build dependencies ... error
ERROR: Command errored out with exit status 1:
command: 'C:\Users\ufx\AppData\Local\Programs\Python\Python310\python.exe' 'C:\Users\ufx\AppData\Local\Temp\pip-standalone-pip-hwkn7bp9\__env_pip__.zip\pip' install --ignore-installed --no-user --prefix 'C:\Users\ufx\AppData\Local\Temp\pip-build-env-q28f4203\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- 'setuptools>=51.0.0' wheel 'Cython>=0.29.21,<3' 'numpy==1.17.3; python_version=='"'"'3.7'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.18.3; python_version=='"'"'3.8'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.3; python_version>='"'"'3.9'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.7'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.9'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"''
cwd: None
Complete output (839 lines):
Ignoring numpy: markers 'python_version == "3.7" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_machine == "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
Ignoring numpy: markers 'python_version == "3.9" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
Collecting setuptools>=51.0.0
Using cached setuptools-58.2.0-py3-none-any.whl (946 kB)
Collecting wheel
Using cached wheel-0.37.0-py2.py3-none-any.whl (35 kB) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43970/events | https://github.com/pandas-dev/pandas/pull/43970 | 1,022,718,995 | PR_kwDOAA0YD84tBOQW | 43,970 | TST: changed behavior of df.loc on multiindex | {
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-error occurs due to different versions of pandas
-made the requested changes
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} | 1 | "2021-10-11T13:58:02Z" | "2021-10-11T22:29:44Z" | "2021-10-11T22:29:40Z" | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/43674#discussion_r720831806
There used to be a linting rule for this, but I missed it when moving linting rules over to `pandas-dev-flaker` in https://github.com/pandas-dev/pandas/pull/40906 - sorry about that, I thought I'd checked that everything had been preserved
Would be good to use this one from `pandas-dev-flaker` directly, but without a `--per-file-selects` option it'd be rather cumbersome (https://github.com/PyCQA/flake8/issues/344#issuecomment-940054283) | {
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] | open | false | null | [] | null | 0 | "2021-10-11T15:55:45Z" | "2021-10-16T04:09:45Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# sample data
dates1 = ['2021-08-26', '2021-08-27', '2021-08-30', '2021-08-31',
'2021-09-01', '2021-09-02', '2021-09-03', '2021-09-07',
'2021-09-08', '2021-09-09', '2021-09-10', '2021-09-13',
'2021-09-14', '2021-09-15', '2021-09-16', '2021-09-17',
'2021-09-20', '2021-09-21', '2021-09-22', '2021-09-23',
'2021-09-24', '2021-09-27', '2021-09-28', '2021-09-29',
'2021-09-30', '2021-10-01', '2021-10-04', '2021-10-05',
'2021-10-06', '2021-10-07', '2021-10-08']
dates2 = ['2021-08-29', '2021-09-05', '2021-09-12', '2021-09-19', '2021-09-26']
np.random.seed(365)
y1 = np.random.randn(len(dates1)).cumsum()
y2 = np.random.randn(len(dates2)).cumsum()
# dataframe with more than a month span
df1 = pd.DataFrame({'date':pd.to_datetime(dates1), 'y1':y1})
df1.set_index('date', inplace=True)
# dataframe with less than a month span
df2 = pd.DataFrame({'date':pd.to_datetime(dates2), 'y2':y2})
df2.set_index('date', inplace=True)
```
### Issue Description
- See [SO: Plotting two pandas time-series on the same axes with matplotlib - unexpected behavior](https://stackoverflow.com/q/69516844/7758804)
- Using `pandas.DataFrame.plot` to plot data with a date range span of more than one month with another data set where the date range span is less than a month, on `seconday_y`, produces unexpected results in how the API formats and plots the `xticks`, which results in an incorrect visualization in subplot 0.
- In both cases, it is not clear what format the dates have been converted to for plotting.
- If `dates2` spans at least a month, the issue doesn't occur. (e.g. `dates2 = ['2021-08-29', '2021-09-05', '2021-09-12', '2021-09-19', '2021-09-26', '2021-09-29']`).
```python
fig, axs = plt.subplots(2, 2, figsize=[12, 12])
axs = axs.flat
print('Note the difference in xticks depending on the date span')
df1.plot(ax=axs[0], title='x-axis is incorrect when the dataframe with\nmore than a month of dates is plotted first')
print(f'axs[0]: {axs[0].get_xticks()}')
df2.plot(ax=axs[0], secondary_y=True)
print(f'axs[0]: {axs[0].get_xticks()}')
df2.plot(ax=axs[1], color='tab:orange', title='x-axis is correct when the dataframe with\nless than a month of dates is plotted first')
print(f'axs[1]: {axs[1].get_xticks()}')
df1.plot(ax=axs[1], color='tab:blue', secondary_y=True)
print(f'axs[1]: {axs[1].get_xticks()}')
df1.y1.plot(ax=axs[2], color='tab:blue', title='More than a month of data')
print(f'axs[2]: {axs[2].get_xticks()}')
df2.y2.plot(ax=axs[3], color='tab:orange', title='Less than a month of data')
print(f'axs[3]: {axs[3].get_xticks()}')
plt.tight_layout()
```
- Printed output
```python
Note the difference in xticks depending on the date span
axs[0]: [18871. 18878. 18885. 18892. 18901. 18908.]
axs[0]: [ 2696 4175 6784 9393 12002 14611 17220 18908]
axs[1]: [2696 2697 2700]
axs[1]: [2696 2697 2701 2702]
axs[2]: [18871. 18878. 18885. 18892. 18901. 18908.]
axs[3]: [2696 2697 2700]
```

### Expected Behavior
- Plotting directly with `matplotlib.pyplot.plot` produces the correct result
```python
fig, axs = plt.subplots(2, 2, figsize=[20, 8], sharey=False, sharex=False)
axs = axs.flatten()
axs[0].plot(df1.index, df1.y1, marker='.', color='tab:blue')
print(f'axs[0]: {axs[0].get_xticks()}')
ax4 = axs[0].twinx()
ax4.plot(df2.index, df2.y2, marker='.', color='tab:orange')
print(f'ax4: {ax4.get_xticks()}')
axs[1].plot(df2.index, df2.y2, marker='.', color='tab:orange')
print(f'axs[1]: {axs[1].get_xticks()}')
ax5 = axs[1].twinx()
ax5.plot(df1.index, df1.y1, marker='.', color='tab:blue')
print(f'ax5: {ax5.get_xticks()}')
axs[2].plot(df1.index, df1.y1, marker='.', color='tab:blue')
print(f'axs[2]: {axs[2].get_xticks()}')
axs[3].plot(df2.index, df2.y2, marker='.', color='tab:orange')
print(f'axs[3]: {axs[3].get_xticks()}')
```
- Print output
```python
axs[0]: [18871. 18878. 18885. 18892. 18901. 18908.]
ax4: [18871. 18878. 18885. 18892. 18901. 18908.]
axs[1]: [18868. 18871. 18875. 18879. 18883. 18887. 18891. 18895.]
ax5: [18871. 18878. 18885. 18892. 18901. 18908.]
axs[2]: [18871. 18878. 18885. 18892. 18901. 18908.]
axs[3]: [18868. 18871. 18875. 18879. 18883. 18887. 18891. 18895.]
```

### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.3
numpy : 1.20.3
pytz : 2021.3
dateutil : 2.8.2
pip : 21.0.1
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.27.0
pandas_datareader: 0.10.0
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43973 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43973/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43973/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43973/events | https://github.com/pandas-dev/pandas/pull/43973 | 1,022,877,732 | PR_kwDOAA0YD84tBvFy | 43,973 | ENH: Rolling with steps | {
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] | closed | false | null | [] | null | 8 | "2021-10-11T15:57:18Z" | "2021-10-16T20:54:29Z" | "2021-10-16T20:54:29Z" | CONTRIBUTOR | null | - [x] closes #15354
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
- [x] update docu
- [ ] docu example
- [ ] add *versionadded* to docu if accepted
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After this PD, the following methods should propagate metadata using `__finalize__`:
- `DataFrame.combine, DataFrame.combine_first`
- `DataFrame.explode`
- `DataFrame.isin`
- `DataFrame.merge`
- `DataFrame.melt`
- `DataFrame.mode`
- `DataFrame.quantile`
<br/>
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Contributors:
- [Casper Lundberg](https://github.com/casperlundberg)
- [Rickard Bemm](https://github.com/Rickos99)
- [Simon Lundberg](https://github.com/SimonHotDog)
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] | closed | false | null | [] | null | 5 | "2021-10-11T16:10:17Z" | "2021-10-13T23:45:45Z" | "2021-10-13T23:45:35Z" | NONE | null | #### Pain Point
I feel that extracting unixtimes from date strings is less graceful than it could be, and relies on the user knowing more about the internal format of datetime objects than they should have to.
Currently (at least as of version 1.3.3), a user needs to cast a Timestamp object as an integer to extract the unixtime (in nanoseconds)
```
import pandas as pd
import numpy as np
x = pd.to_datetime("2020")
unixtime_sec = x.astype(np.int64) / 1e9
```
This makes sense if you understand how a Timestamp object is storing its information internally. However, I would argue that this requires the end-user to understand an implementation detail that they shouldn't need to. I shouldn't need to care whether the time is represented internally by unixtime, TAI, GPS time etc. I should be able to ask for unixtime without relying on knowledge of the implementation details.
I would also argue that the current approach also makes code harder to read. While other attributes of time can be obtained by asking for them directly, unixtime requires an more indirect request
```
hour_of_day = x.hour #Easy to read
unixtime_ns = x.astype(np.int64) #Harder to read
```
To add to the confusion, the `DatetimeIndex` object requires the user needs to use `view` instead of `astype` (see https://github.com/pandas-dev/pandas/pull/38544). I don't understand quite why the interface for Timestamps and DatetimeIndices needs to be different .
```
x = pd.date_range("2020", "2021")
unixtime_sec = x.view(np.int64) / 1e9
```
#### Proposed solution
Timestamps and DatetimeIndex objects should have a unixtime accessor consistent with the interface used to access hour of day, day of month, etc.
```
x = pd.to_datetime("2020")
hour_of_day = x.hour # Currently exists
day_of_month = x.day # Currently exists
unixtime_sec = x.unixtime # This proposal
x = pd.date_range("2020", "2021")
unixtime_sec = x.unixtime # This proposal
```
The accessor should return a floating point number for a timestamp, or an iterable (e.g a Series) of floating point numbers for a DatetimeIndex. This float should represent the number of seconds elapsed from the epoch. The unit should be seconds, not nanoseconds, to be consistent with the definition of unixtime (https://pubs.opengroup.org/onlinepubs/9699919799/xrat/V4_xbd_chap04.html#tag_21_04_16).
#### API breaking implications
This will not break any other features of the API, although deprecating the astype() and view() accessors could be considered. If these accessors are deprecated, that would make changing the internal representation in a Timestamp object easier in future.
#### Describe alternatives you've considered
The current methods work, although they are unsatisfactory because
1. They require the end user have knowledge of what is an implementation detail
2. They result in code that is harder to read
3. It is cumbersome to write code that works for both Timestamps and DatetimeIndex objects
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} | 2 | "2021-10-11T16:11:30Z" | "2021-10-15T16:48:20Z" | "2021-10-14T17:07:13Z" | CONTRIBUTOR | null | just updating something that I missed on previous pr when arg name was changed.
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} | 0 | "2021-10-11T19:20:20Z" | "2021-10-14T23:45:48Z" | "2021-10-14T23:21:06Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4),
columns=['A', 'B', 'C', 'D'])
print(df.drop(columns=list(["A", "B"]))) # <-- Works
print(df.drop(columns=np.array(["A", "B"]))) # <-- Works
print(df.drop(columns=tuple(["A", "B"]))) # <-- Fails with KeyError
```
### Issue Description
The documentation says that the `columns=` argument of `DataFrame.drop()` can take a single label or list-like, but it fails when given a `tuple` with more than column name. This method seems to work when the exact same column labels are provided as a `list` or a `np.ndarray`.
Just an observation -- a `tuple` with `len()==1` does seem to work successfully here.
### Expected Behavior
For the above example, `df.drop(columns=("A", "B"))` should produce the same output as `columns=["A", "B"]` or `columns=np.array(["A", "B"])`, resulting in the following DataFrame:
```
C D
0 2 3
1 6 7
2 10 11
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.11.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Fri Oct 30 13:34:27 PDT 2020; root:xnu-4570.71.82.8~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 58.0.4
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
ls = [
1234567890123456789,
np.nan,
1234567890123337789,
]
pd.Series(ls, dtype=np.float128)
```
### Issue Description
When a series is created from a list that contains np.nan values, its type is float.
And if the list contains 19-digit integers the precision of the last few digits is lost.
A similar issue seems to have been fixed in numpy a couple of years ago: https://github.com/numpy/numpy/issues/9006.
Would you please suggest any workaround until this is resolved? Having a series with integer values and nans would work for me in this particular case. Thank you in advance.
### Expected Behavior
The expected behaviour would be to keep the precision exactly.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.5.final.0
pandas : 1.3.3
numpy : 1.20.2
</details>
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} | 9 | "2021-10-11T21:09:30Z" | "2021-10-16T17:30:01Z" | "2021-10-16T17:28:54Z" | CONTRIBUTOR | null | Set `skipna` default values to `True` so that the value matches the
purpose of the variable and its docstring.
- [X] closes #34063
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 0 | "2021-10-11T21:22:41Z" | "2021-10-13T07:45:40Z" | "2021-10-13T07:45:40Z" | CONTRIBUTOR | null | Both modified functions may return a MultiIndex; the return annotation has been updated to reflect that.
Added type hint for `names`.
Minor docstring tweak.
- [ ] closes #xxxx
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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] | closed | false | null | [] | null | 3 | "2021-10-11T21:36:04Z" | "2021-10-13T17:57:07Z" | "2021-10-13T17:57:07Z" | NONE | null | #### Is your feature request related to a problem?
I'm looking for a polymorphic way to test for equality. Right now, ff I want to compare two objects in pandas, I need to know the type so I can use `DataFrame.eq` or `Series.eq`, or if they're scalars then I need to use `x == y` or similar.
It would be nice if there were a `pd.equals(x, y)` method that could handle scalars, series, dataframes, indexes, etc. Right now I'm rolling my own custom version of this code.
I might be missing some obvious solution here, if so please point me there.
#### Describe the solution you'd like
Probably this would just involve some type checking and then dispatch to the proper methods. This should be elementwise, so you get back `bool | np.nan` data of the same shape.
Of course, this also opens the door to a whole lot more questions, like probably then there should be a corresponding `pd.ne`, `pd.lt`, etc for other comparisons.
#### API breaking implications
Would add a lot more API surface area, and there are lots of little questions as to how to make this consistent with the rest of the API, e.g. is `nan == nan`? That's what `DataFrame.equals` uses, but not `DataFrame.eq`.
#### Describe alternatives you've considered
I can add this later if this idea get's any support. | {
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] | closed | false | null | [] | null | 0 | "2021-10-11T21:37:25Z" | "2021-10-15T15:44:03Z" | "2021-10-15T15:44:03Z" | CONTRIBUTOR | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-with-list-with-missing-labels-is-deprecated
### Documentation problem
The `Warning` doesn't explicitly state a `KeyError` will be raised.
The last example displays behaviour exhibited prior to deprecation; a KeyError is now raised.
### Suggested fix for documentation
Using .loc or [] with a list with one or more missing labels will now raise a ``KeyError``—use ``.reindex`` instead.
The last example (*with the warning*) will no longer be required. | {
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} | 2 | "2021-10-11T21:53:04Z" | "2021-10-13T03:24:32Z" | "2021-10-11T22:06:03Z" | CONTRIBUTOR | null | #### Describe the solution you'd like
Have there been any discussions on refactoring `if-else-isinstance` checks with the new `match-case` syntax (*pattern matching*)?
It looks very useful, even if purely used as a `switch-case` stand in.
This should simplify typechecks, of which there are quite a few in the codebase.
#### API breaking implications
Should not have any impact, as purely an internal refactor.
#### Additional context
Sample example of refactor—`between()`, taken from `series.py`:
Current:
```python
if inclusive == "both":
lmask = self >= left
rmask = self <= right
elif inclusive == "left":
lmask = self >= left
rmask = self < right
elif inclusive == "right":
lmask = self > left
rmask = self <= right
elif inclusive == "neither":
lmask = self > left
rmask = self < right
else:
raise ValueError(
"Inclusive has to be either string of 'both',"
"'left', 'right', or 'neither'."
)
```
Refactored (*poorly, probably*):
```python
match inclusive:
case "both":
lmask = self >= left
rmask = self <= right
case "left":
lmask = self >= left
rmask = self < right
case "right":
lmask = self > left
rmask = self <= right
case "neither":
lmask = self > left
rmask = self < right
case _:
raise ValueError(
"Inclusive has to be either string of 'both',"
"'left', 'right', or 'neither'."
)
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 0 | "2021-10-11T22:35:36Z" | "2021-10-16T04:13:23Z" | null | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
ser = pd.Series([1, 2])
arr = np.array([None, None], dtype=object)
arr[0] = ser
arr[1] = ser * 2
parr = pd.array(arr) # <- PandasArray
>>> pd.DataFrame(arr)
0
0 0 1
1 2
dtype: int64
1 0 2
1 4
dtype: int64
>>> pd.DataFrame(parr)
0 1
0 1 2
1 2 4
```
### Issue Description
We un-nest nested data when it is in a PandasArray but not when it is in a ndarray/Index/Series. Aligning this behavior would allow us to simplify the DataFrame constructor.
Only one test where we pass a PandasArray containing Series to DataFrame: test_apply_series_on_date_time_index_aware_series
### Expected Behavior
ndarray vs PandasArray should not matter here.
### Installed Versions
<details>
Replace this line with the output of pd.show_versions()
</details>
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} | 0 | "2021-10-11T22:58:31Z" | "2021-10-16T13:12:03Z" | "2021-10-14T23:33:23Z" | CONTRIBUTOR | null | `DataFrame.__repr__` used different parameters than `DataFrame.to_string` and I've got a case where I want to use the `__repr__` ones, but with a change to one of the parameters. This refactoring makes it possible to extract those parameters so we can do:
```python
>>> params = pd.io.formats.format.get_frame_repr_params()
>>> params[my_param] = new_value
>>> df.to_string(**params)
```
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} | 0 | "2021-10-11T23:50:46Z" | "2021-10-12T19:45:16Z" | "2021-10-12T19:32:17Z" | MEMBER | null | ```
index = pd.Index(range(2))
%timeit index.insert(2, 2)
48.4 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
2.54 µs ± 70.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
``` | {
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} | 1 | "2021-10-12T01:50:11Z" | "2021-10-12T19:31:54Z" | "2021-10-12T19:26:51Z" | MEMBER | null | After this we're close to being able to change the default from "equiv" to True (not that we would since that would be an API change) | {
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} | 2 | "2021-10-12T02:19:13Z" | "2021-10-12T03:01:40Z" | "2021-10-12T02:48:12Z" | CONTRIBUTOR | null | #### Motivation
We often want to make vectorized comparisons between two Categoricals. However, this is not possible unless they have the same categories:
```python
In [45]: A = pd.Categorical([10, 33, 24, 33, 10])
In [46]: A
Out[46]:
[10, 33, 24, 33, 10]
Categories (3, int64): [10, 24, 33]
In [47]: B = pd.Categorical([33, 21, 24, 33, 10])
In [48]: B
Out[48]:
[33, 21, 24, 33, 10]
Categories (4, int64): [10, 21, 24, 33]
In [49]: A == B
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-49-acc3d1f167fc> in <module>
----> 1 A == B
...
143 msg = "Categoricals can only be compared if 'categories' are the same."
144 if not self._categories_match_up_to_permutation(other):
--> 145 raise TypeError(msg)
146
147 if not self.ordered and not self.categories.equals(other.categories):
TypeError: Categoricals can only be compared if 'categories' are the same.
```
#### Proposed solution
It would be useful to have a utility function which takes two or more Categoricals as input and re-encodes each of them using the union of their respective categories as the new categories. This is very similar to what `union_categoricals` does, except that outputs a concatenation of the input Categoricals.
Here is a basic implementation which I have used, with apologies for my lack of knowledge of Pandas internals:
```python
In [50]: def unify_categoricals(*categoricals):
...: all_categories = set(itertools.chain(*[c.categories for c in categoricals]))
...: for categorical in categoricals:
...: new_categories = all_categories.difference(set(categorical.categories))
...: if new_categories:
...: categorical.add_categories(new_categories, inplace=True)
...:
In [51]: unify_categoricals(A, B)
<ipython-input-50-e69ac645c9fb>:6: FutureWarning: The `inplace` parameter in pandas.Categorical.add_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.
categorical.add_categories(new_categories, inplace=True)
In [52]: A
Out[52]:
[10, 33, 24, 33, 10]
Categories (4, int64): [10, 24, 33, 21]
In [53]: B
Out[53]:
[33, 21, 24, 33, 10]
Categories (4, int64): [10, 21, 24, 33]
In [54]: A == B
Out[54]: array([False, False, True, True, True])
```
This could be modified to avoid inplace operations on the input Categoricals and extended to support ordered Categoricals as well. The name is probably too similar to `union_categoricals`; suggestions are appreciated.
#### API breaking implications
None
#### Alternatives
For one-off comparisons, you can get away with simply decoding the Categoricals:
```python
In [55]: A.to_numpy() == B.to_numpy()
Out[55]: array([False, False, True, True, True])
```
There is only a real benefit if you need to make many comparisons (for my application, we persist DataFrames to Feather files and load them into memory for many different tasks, so it is useful to perform this conversion once before storage). Since this is somewhat of a niche use case it could be left to application code.
There might also be some way to chop up the output of `union_categoricals` into multiple Categoricals which are the re-encoded versions of the inputs. | {
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} | 1 | "2021-10-12T04:18:47Z" | "2021-10-12T19:31:13Z" | "2021-10-12T19:27:24Z" | MEMBER | null | The quantile function in algos is a partial implementation of the numpy quantile and there are no pandas objects involved. | {
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} | 0 | "2021-10-12T04:54:51Z" | "2021-10-12T19:44:58Z" | "2021-10-12T19:31:54Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 5 | "2021-10-12T05:55:42Z" | "2021-10-13T02:08:11Z" | "2021-10-13T02:01:23Z" | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
s1 = pd.Series([1,2,3,4,5, None])
s2 = s1.astype('Int16')
s1.dtype
Out[13]: dtype('float64')
s2.dtype
Out[14]: Int16Dtype()
# Equality using extension Dtype first works as expected
s2.dtype == s1.dtype
Out[15]: False
# Equality using non-extension Dtype first raises TypeError
s1.dtype == s2.dtype
File "<ipython-input-16-8f0ca559fe44>", line 1, in <module>
s1.dtype == s2.dtype
TypeError: Cannot interpret 'Int16Dtype()' as a data type
```
### Issue Description
It appears that when doing a dtype comparison with a non-extension Dtype first and extension Dtype second causes TypeError, whereas doing the other way round works as expected
### Expected Behavior
Dtype comparison behaviour should not depend on the order of the dtypes in the statement
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Australia.1252
pandas : 1.3.3
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : 0.29.24
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.54.0
</details>
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] | closed | false | null | [] | null | 6 | "2021-10-12T08:33:16Z" | "2021-10-22T19:44:58Z" | "2021-10-22T19:44:58Z" | CONTRIBUTOR | null | #### Describe the solution you'd like
Just like other methods where we select a subset or reorder, we have a `ignore_index` argument, to reset the index.
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"A": range(10), "B": range(10, 20)})
In [3]: df.nlargest(n=2, columns="A")
Out[3]:
A B
9 9 19
8 8 18
In [4]: df.nlargest(n=2, columns="A", ignore_index=True)
Out[3]:
A B
0 9 19
1 8 18
```
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} | 2 | "2021-10-12T09:40:05Z" | "2021-10-12T22:19:37Z" | "2021-10-12T22:19:37Z" | CONTRIBUTOR | null | Use correct types to avoid downcasts on Windows
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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} | 0 | "2021-10-12T13:15:46Z" | "2021-10-21T02:05:54Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
# Bug in v1.3.0 and later
import pandas as pd
import numpy as np
print(pd.__version__)
X = pd.DataFrame({
"a": pd.Categorical([np.nan], [1]),
"b": [1] # comment this out and it will return a categorical in 1.3.0
})
mask = X["a"].isna()
X.loc[mask, "a"] = np.array([1])
print(X["a"].dtype)
# int, but should remain a category
```
### Issue Description
In the above reproducible example, using pandas v1.3.0 will return a int64 type. However, the column was initially a categorical type.
The behaviour is very esoteric. In v1.2.5, this code works properly and keeps the categorical type. If the column "b" is removed, then it works correctly in v1.3.0. If the np.nan is replaced with a value such as 1 in the "a" column, this code works correctly.
After performing a `git bisect`, the offending commit is 80227439db6b90985b9fbcde2a8f8ea85a4569b2. Its parent (4ecba519dc4d93f6524bde102c97b5ebb91aa3ae) returns a category. Something in 802274... introduces this bug.
### Expected Behavior
The last print returns a category type, not an int64 which is the correct and expected behaviour.
```python
# Works correctly in v1.2.5
import pandas as pd
import numpy as np
print(pd.__version__)
X = pd.DataFrame({
"a": pd.Categorical([np.nan], [1]),
"b": [1]
})
mask = X["a"].isna()
X.loc[mask, "a"] = np.array([1])
print(X["a"].dtype)
# category
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 80227439db6b90985b9fbcde2a8f8ea85a4569b2
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:27 PDT 2021; root:xnu-7195.141.2~5/RELEASE_ARM64_T8101
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0.dev0+435.g80227439db
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3
setuptools : 58.2.0
Cython : 0.29.24
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-10-12T15:36:36Z" | "2021-10-15T00:59:21Z" | "2021-10-15T00:59:21Z" | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
index = pd.date_range("2020", periods=4, freq="1ns")
df = pd.Series([1, 1, 1, 1], index=index, dtype=float)
result = df.rolling('3ns', closed='neither', center=True).sum()
print(result)
# 2020-01-01 00:00:00.000000000 1.0
# 2020-01-01 00:00:00.000000001 1.0
# 2020-01-01 00:00:00.000000002 1.0
# 2020-01-01 00:00:00.000000003 1.0
# Freq: N, dtype: float64
```
### Issue Description
Rolling with smallest possible resolution (currently nanoseconds) **centered** windows and uneven nanoseconds produce wrong results.
Eg. rolling with a centered `window='3ns'`, regardless of `closed`, should result in a forward facing and a backward facing window of `1.5ns`, which is effectively a fwd/bwd facing window of `1ns` with `closed='both'`.
btw. thats exactly the code change in the followup PR will introduce ;)
### Expected Behavior
```py
>>> result
2020-01-01 00:00:00.000000000 2.0
2020-01-01 00:00:00.000000001 3.0
2020-01-01 00:00:00.000000002 3.0
2020-01-01 00:00:00.000000003 2.0
Freq: N, dtype: float64
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 075ec87643130074c9389a17a9d964cd33f779dc
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-88-generic
Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+869.g075ec87643
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.2.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.1
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.10.0
fastparquet : 0.7.1
gcsfs : 2021.10.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.10.0
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
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} | 2 | "2021-10-12T15:44:50Z" | "2021-10-15T00:59:28Z" | "2021-10-15T00:59:21Z" | CONTRIBUTOR | null | - [x] closes #43997
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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] | open | false | null | [] | null | 3 | "2021-10-12T16:09:08Z" | "2021-10-16T04:23:47Z" | null | NONE | null | #### Is your feature request related to a problem?
The default behaviour of `pandas.DataFrame.groupby` is currently different depending on the type of the groupers (when one of the groupers is categorical, unobserved categories are added to the groupby by default. This behaviour can be overriden by setting the `observed` argument to ` False`).
I feel like making the groupby API consistent by default and regardless of the underlying data type would provide a much better user experience.
#### Describe the solution you'd like
Default to `observed=False` in `pandas.DataFrame.groupby`.
#### API breaking implications
Would break backwards-compatibility.
#### Describe alternatives you've considered
So far the only option I can think of is to add `observed=True` to every groupby I write to make sure it will behave correctly no matter what kind of data gets passed to it. | {
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] | open | false | null | [] | null | 3 | "2021-10-12T18:32:34Z" | "2021-10-20T15:20:25Z" | null | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
idx = pd.Index([1, True, 0, False])
idx2 = idx[1:]
>>> idx.union(idx2)
Index([0, 0, 1, 1], dtype='object')
```
### Issue Description
Best guess is union_with_duplicates cc @phofl
Breaks at least one test in #43930
### Expected Behavior
`Index([1, True, 0, False], dtype=object)`
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] | open | false | null | [] | null | 0 | "2021-10-12T19:14:58Z" | "2021-10-16T04:25:10Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = (pd.DataFrame({'gender' : ['male', 'female', 'male', 'female', 'male'],
'country': ['BR', 'BR', 'US', 'BR', 'US']})
.astype('category')
)
df.value_counts() # drops all empty categories
# gender country
# female BR 2
# male US 2
# BR 1
# dtype: int64
# also compare
(df
.query("country != 'BR'")
.value_counts()
) # drops non-observed categories
# gender country
# male US 2
# dtype: int64
(df
.query("country != 'BR'")
['gender']
.value_counts()
) # shows 0 for non-observed catgories
# male 2
# female 0
# Name: gender, dtype: int64
```
### Issue Description
`DataFrame.value_counts()` drops any non observed categories while `Series.value_counts()` shows 0 for non-observed categories.
There was some discussion about the zeroes for `Series.value_counts()` in this issue https://github.com/pandas-dev/pandas/issues/8559, based on that I checked behaviour in R which also keeps the zeros:
``` r
gender <- factor(c('male', 'female', 'male', 'female', 'male'))
country <- factor( c( 'BR', 'BR', 'US', 'BR', 'US') )
df <- data.frame(gender=gender, country=country)
table(df)
#> country
#> gender BR US
#> female 2 0
#> male 1 2
```
### Expected Behavior
The following work-around gives the expected behaviour:
```py
df.groupby("gender").country.value_counts()
# gender
# female BR 2
# US 0
# male US 2
# BR 1
# Name: country, dtype: int64
```
This fails however for more than two categorical variables.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Sep 16 20:58:47 PDT 2021; root:xnu-6153.141.40.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
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 : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/44002 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44002/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44002/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44002/events | https://github.com/pandas-dev/pandas/pull/44002 | 1,024,193,504 | PR_kwDOAA0YD84tF2JK | 44,002 | ENH: Support observed keyword argument in Categorical.value_counts | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-10-13T01:12:19Z" | "2021-10-16T14:46:44Z" | "2021-10-16T14:46:41Z" | CONTRIBUTOR | null | - [x] closes #37348
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
This makes sure that comparing int to empty strings doesn't return an error.
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} | 0 | "2021-10-13T04:01:56Z" | "2021-10-15T14:49:54Z" | "2021-10-15T13:52:08Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-10-13T04:18:04Z" | "2021-10-18T15:57:24Z" | "2021-10-17T20:55:07Z" | CONTRIBUTOR | null | - [x] closes #41676
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Hello! I have added the following information to the source code (docstring of to_datetime method):
1. In case when DataFrame is passed as a parameter, the to_datetime method expects the following column names (minimally "year", "month", "day").
2. The method returns Series when the DataFrame is passed.
I have created Docker Image & Python Environment, then tested my docstring update by using `validate_docstrings.py` script.
Before testing my update, I have run the script on master branch. The script found 2 errors on master branch:
```
2 Errors found:
No extended summary found
flake8 error: E999 SyntaxError: invalid syntax
```
Then, I switched to my [feature branch](https://github.com/abatomunkuev/pandas/tree/issue%2341676) and run the `validate_docstrings.py` script.
Output of the script
```
################################################################################
######################## Docstring (pandas.to_datetime) ########################
################################################################################
Convert argument to datetime.
Parameters
----------
arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
The object to convert to a datetime. If the DataFrame is provided, the method expects
minimally the following columns ( "year", "month", "day") in the DataFrame.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception.
- If 'coerce', then invalid parsing will be set as NaT.
- If 'ignore', then invalid parsing will return the input.
dayfirst : bool, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
.. warning::
dayfirst=True is not strict, but will prefer to parse
with day first. If a delimited date string cannot be parsed in
accordance with the given `dayfirst` option, e.g.
``to_datetime(['31-12-2021'])``, then a warning will be shown.
yearfirst : bool, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
.. warning::
yearfirst=True is not strict, but will prefer to parse
with year first.
utc : bool, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
format : str, default None
The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
exact : bool, True by default
Behaves as:
- If True, require an exact format match.
- If False, allow the format to match anywhere in the target string.
unit : str, default 'ns'
The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : bool, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings based on the first non-NaN element,
and if it can be inferred, switch to a faster method of parsing them.
In some cases this can increase the parsing speed by ~5-10x.
origin : scalar, default 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
cache : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets. The cache is only
used when there are at least 50 values. The presence of out-of-bounds
values will render the cache unusable and may slow down parsing.
.. versionchanged:: 0.25.0
- changed default value from False to True.
Returns
-------
datetime
If parsing succeeded.
Return type depends on input:
- list-like:
- DatetimeIndex, if timezone naive or aware with the same timezone
- Index of object dtype, if timezone aware with mixed time offsets
- Series: Series of datetime64 dtype
- DataFrame: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_timedelta : Convert argument to timedelta.
convert_dtypes : Convert dtypes.
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = pd.DataFrame({'year': [2015, 2016],
... 'month': [2, 3],
... 'day': [4, 5]})
>>> pd.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
NaT
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> %timeit pd.to_datetime(s, infer_datetime_format=True) # doctest: +SKIP
100 loops, best of 3: 10.4 ms per loop
>>> %timeit pd.to_datetime(s, infer_datetime_format=False) # doctest: +SKIP
1 loop, best of 3: 471 ms per loop
Using a unix epoch time
>>> pd.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> pd.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
.. warning:: For float arg, precision rounding might happen. To prevent
unexpected behavior use a fixed-width exact type.
Using a non-unix epoch origin
>>> pd.to_datetime([1, 2, 3], unit='D',
... origin=pd.Timestamp('1960-01-01'))
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
dtype='datetime64[ns]', freq=None)
In case input is list-like and the elements of input are of mixed
timezones, return will have object type Index if utc=False.
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'])
Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
... utc=True)
DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
################################################################################
################################## Validation ##################################
################################################################################
2 Errors found:
No extended summary found
flake8 error: E999 SyntaxError: invalid syntax
```
There are still some errors from master branch.
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] | open | false | null | [] | null | 5 | "2021-10-13T05:17:10Z" | "2021-10-24T03:29:13Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
ser = pd.Series(['1', '2'])
ser.sum() # this returns 12 instead of 3
ser.mean() # this returns 6.0 instead of 1.5
```
### Issue Description
The sum() and mean() methods of the Series and DataFrame return wrong values when applied to integers as strings. This is silently catastrophic since the user is left with apparently good but erroneous results.
### Expected Behavior
```
>>> import pandas as pd
>>> ser = pd.Series(['1', '2'])
>>> ser.sum()
TypeError: ...
```
OR
```
>>> ser.sum()
3
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.3
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.20
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 1 | "2021-10-13T08:21:59Z" | "2021-10-13T13:54:36Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({"A": [2, 1, 2, 2],
"B": [3, 3, 4, 4],
"E": [['10'], ['20'], ['30'], ['40']]})
df["A"].cumsum()
df["E"].cumsum()
df.groupby("B")["A"].cumsum()
df.groupby("B")["E"].cumsum()
```
### Issue Description
Inconsistency between behavior of cumsum() when applied directly, and when applied within groupby().
When used directly on a column of a dataframe that contains lists, cumsum() progressively concatenates the lists.
When used as part of groupby() on the same dataframe cumsum() throws "NotImplementedError: function is not implemented for this dtype: [how->cumsum,dtype->object]"
### Expected Behavior
I'd expect cumsum() to progressively concatenate the lists of all rows within each group defined by groupby(), in a way similar to what it does with numeric values.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-147-generic
Version : #151-Ubuntu SMP Fri Jun 18 19:21:19 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3
Cython : 0.29.21
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.53.1
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
python 3.8.10 (default, Jun 4 2021, 15:09:15)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'1.3.3'
>>> import numpy as np
>>> df = pd.DataFrame({'A':[1,1], 'B':[np.inf,2]})
>>> df.sum()
A 2.0
B inf
dtype: float64
>>> df.groupby(['A']).agg(sum)
B
A
1 NaN
>>> df.groupby(['A']).sum()
B
A
1 NaN
```
### Issue Description
when summing inf and a number from a groupby aggregation the result is NaN
### Expected Behavior
when summing inf and a number from a groupby aggregation the result should be inf
### Installed Versions
<details>
pd.show_version()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/thomas/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/__init__.py", line 244, in __getattr__
raise AttributeError(f"module 'pandas' has no attribute '{name}'")
AttributeError: module 'pandas' has no attribute 'show_version'
</details>
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame([range(5) for _ in range(5)], columns=list('abcde'))
styled = df.style.set_table_styles({
'b': [{'selector': 'th, td', 'props': [('border-left', '2px solid black')]}]
})
# styled = df.style.set_table_styles({
# 'b': [{'selector': 'th', 'props': [('border-left', '2px solid black')]},
# {'selector': 'td', 'props': [('border-left', '2px solid black')]}]
# })
html = styled.render()
with open('css_style_bug.html', 'w') as f:
f.write(html)
```
### Issue Description
This is a minor issue when styling a table for html display. When setting a table style for a specified column using `set_table_styles`, the column is only applied to the final selector tag if they are named in a comma seperated list.
This seems to be similar to issue #34061.
### Expected Behavior
I have commented out, in the above examples, a workaround which does the expected behaviour.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.72-microsoft-standard-WSL2
Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://stackoverflow.com/questions/69555071/is-series-size-mutable
### Question about pandas
>>>s
a 4.0
b 4.0
c 8.0
d 9.0
f NaN
dtype: float64`>>>s
a 4.0
b 4.0
c 8.0
d 9.0
f NaN
dtype: float64
>>>s.shape
(5,)
s.drop('f',inplace=True)
>>> s
a 4.0
b 4.0
c 8.0
d 9.0
dtype: float64
>>> s.size
4
>>> s['f']=9
>>> s
a 4.0
b 4.0
c 8.0
d 9.0
f 9.0
dtype: float64
>>> s['b']=100
>>> s
a 4.0
b 100.0
c 8.0
d 9.0
f 9.0
dtype: float64
>>> s['g']=200
>>> s.size
6
Please go through the above Python commands that imply that Pandas Series are also size mutable. The size of given series is initially 5
Then I delete one element s.drop('f',inplace=True), so the size of the Series will be 4.
Then I again assign a new variable s['f']=9 and s['g']=200, then the size of the Series is 6 now. It means Series is also size mutable. | {
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} | 6 | "2021-10-13T13:12:27Z" | "2021-10-14T03:28:43Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df1 = pd.DataFrame({'a': [4, 4, 4], 'b': [1, 2, 3]}).astype({'a': 'float64', 'b': 'float64'})
# Nullable int 'Int64' gives wrong results. 'int64' works just fine.
df2 = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]}).astype({'a': 'Int64', 'b': 'float64'})
df1.compare(df2, keep_shape=True)
```
### Issue Description
The reproducible example returns
| | ('a', 'self') | ('a', 'other') | ('b', 'self') | ('b', 'other') |
|---:|----------------:|-----------------:|----------------:|-----------------:|
| 0 | nan | 1 | nan | nan |
| 1 | nan | 2 | nan | nan |
| 2 | nan | 3 | nan | nan |
### Expected Behavior
We expect the reproducible example to return
| | ('a', 'self') | ('a', 'other') | ('b', 'self') | ('b', 'other') |
|---:|----------------:|-----------------:|----------------:|-----------------:|
| 0 | 4 | 1 | nan | nan |
| 1 | 4 | 2 | nan | nan |
| 2 | 4 | 3 | nan | nan |
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-37-generic
Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_SG.UTF-8
LOCALE : en_SG.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : None
pymysql : 0.8.1
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.3.24
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] tests added to `pandas/tests/indexing/test_iloc.py`
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [X] During assignment to a .iloc or a .loc indexer, depending on whether the data frame is with mixed types or singular, the typing of the resulting assignment is not consistent.
As explained in issue #43996, the update of v1.3.0 causes a change in dtype casting when assigning a series. The issue contains a minimal working example of this rather inconsistent behaviour. In short, the type of a column changes when the assignment happens on a data frame only containing one column. With multiple columns, assigning the same series retains the original type.
This contribution includes a test case which performs an assignment operation on a single column data frame. Naturally, because of this inconsistency of typing in the current pandas code, this test case fails. Its aim is to serve the bug fix for this inconsistency. | {
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I see a lot of communications about adding upsert features to the to_sql method. I think this is a great idea. I would like to know if there is any possibility of adding functionality to call a stored procedure which takes care of the upsert.
Something like:
df.to_sql('mytable', schema='dbo', con=engine, index=False, method='mytable_upsert', stored_proc=True)
I ask because I want to keep my data in a df to upload but I also would like to handle the upsert issues with a stored procedure for performance reasons.
I am using the pymssql driver.
Yes Matthew, I read #14553 and #29636 before creating this. These are very good efforts and I hope they are adopted. The difference with my request is that I would like to be able to call a stored procedure in the database which would handle inserting or updating rows inside the database itself which is faster than issuing a command in python to insert and then finding it exists and then issuing an update on the row. There are ways to do this, just not with a dataframe using to_sql.
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I went ahead and modified the `.github/workflows/ci.yml` so that the cheatsheets are copied to the docs build directory once the Sphinx site is built | {
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} | 3 | "2021-10-13T22:44:08Z" | "2021-10-18T19:54:31Z" | "2021-10-18T19:54:31Z" | CONTRIBUTOR | null | `DatetimeIndex.std` is missing a doc-string. Add doc-string to `DatetimeArray.std` as `DatetimeIndex` is re-using `DatetimeArray.std`.
There are a few more keywords, but they seem to be not implemented (numpy-compatiblity?)
```py
pd.DatetimeIndex([1, 2]).std(out=[])
ValueError: the 'out' parameter is not supported in the pandas implementation of std()
pd.DatetimeIndex([1, 2]).std(keepdims=True)
ValueError: the 'keepdims' parameter is not supported in the pandas implementation of std()
pd.DatetimeIndex([1, 2]).std(dtype=np.dtype("datetime64[ns]"))
ValueError: the 'dtype' parameter is not supported in the pandas implementation of std()
```
For some reason, this doc-string is needed for #43828. | {
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} | 4 | "2021-10-13T23:22:51Z" | "2021-11-16T13:00:01Z" | "2021-11-16T00:55:59Z" | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import io
import pandas as pd
fwf_data = (
"a b c d\n"
"id8141 360.242940 149.910199 11950.7\n"
"id1594 444.953632 166.985655 11788.4\n"
"id1849 364.136849 183.628767 11806.2\n"
"id1230 413.836124 184.375703 11916.8\n"
"id1948 502.953953 173.237159 12468.3\n"
"id8141 360.242940 149.910199 11950.7\n"
"id1594 444.953632 166.985655 11788.4\n"
"id1849 364.136849 183.628767 11806.2\n"
)
df1 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5])
print(df1)
'''Output
a b c d
0 id8141 360.242940 149.910199 11950.7
1 id1849 364.136849 183.628767 11806.2
2 id1230 413.836124 184.375703 11916.8
3 id8141 360.242940 149.910199 11950.7
4 id1594 444.953632 166.985655 11788.4
5 id1849 364.136849 183.628767 11806.2
'''
df2 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5], nrows=7)
print(df2)
'''Output
a b c d
0 id8141 360.242940 149.910199 11950.7
1 id1849 364.136849 183.628767 11806.2
2 id1230 413.836124 184.375703 11916.8
3 id1948 502.953953 173.237159 12468.3
4 id8141 360.242940 149.910199 11950.7
5 id1594 444.953632 166.985655 11788.4
6 id1849 364.136849 183.628767 11806.2
'''
```
### Issue Description
For `df2`, not all specified lines are skipped.
### Expected Behavior
```python
df2 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5], nrows=7)
print(df2)
'''Expected output
a b c d
0 id8141 360.242940 149.910199 11950.7
1 id1849 364.136849 183.628767 11806.2
2 id1230 413.836124 184.375703 11916.8
3 id8141 360.242940 149.910199 11950.7
4 id1594 444.953632 166.985655 11788.4
'''
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.3.3
numpy : 1.21.1
pytz : 2020.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 57.4.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/44022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44022/events | https://github.com/pandas-dev/pandas/pull/44022 | 1,025,795,774 | PR_kwDOAA0YD84tK-65 | 44,022 | ENH: Ability to coerce floats when reading csv files GH2570 | {
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] | closed | false | null | [] | null | 2 | "2021-10-14T00:02:58Z" | "2021-11-14T03:07:59Z" | "2021-11-14T03:07:58Z" | NONE | null | - [x] closes #2570
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Ran the `io.csv.Read*` benchmarks which resulted in `BENCHMARKS NOT SIGNIFICANTLY CHANGED.`
I believe that `Dict[Hashable, Tuple[Dtype, str]]` should be added to `DtypeArg` to fix the failing typing check. However, that results in 20 additional errors in io\sql.py and io\parsers\base_parser.py with the additional tuple type. Perhaps there should be a distinction and an additional `CSVDtypeArg`. Looking for advice on how best to approach fixing this. | {
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