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} | 0 | 2020-11-04T00:30:58Z | 2020-11-04T04:02:09Z | 2020-11-04T02:05:28Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/37620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37620/events | https://github.com/pandas-dev/pandas/issues/37620 | 735,741,380 | MDU6SXNzdWU3MzU3NDEzODA= | 37,620 | BUG: ParserError while reading a csv file generated by pandas dataframe | {
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"description": "Issue that has not been ... | closed | false | null | [] | null | 2 | 2020-11-04T01:21:27Z | 2021-08-14T04:49:42Z | 2021-08-14T04:49:42Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
`temp.parquet`(Please rename the file to `temp.parquet`, attached by renaming to zip file) :[temp.parquet.zip](https://github.com/pandas-dev/pandas/files/5484945/temp.parquet.zip)
```python
In[68]: import pandas as pd
In[69]: df = pd.read_parquet('temp.parquet')
In[70]: df
Out[70]:
0 1 2 ... 26 27 28
0 <NA> <NA> <NA> ... 2028304185033190912 <NA> <NA>
1 <NA> True <NA> ... <NA> <NA> <NA>
2 <NA> True 5054 ... <NA> <NA> <NA>
3 <NA> <NA> -31346 ... -4248617305981604864 <NA> 3122060888
4 <NA> True <NA> ... -7541642907968846848 <NA> 747124512
.. ... ... ... ... ... ... ...
813 <NA> True <NA> ... <NA> <NA> 62110627
814 <NA> False -14583 ... <NA> <NA> <NA>
815 <NA> <NA> <NA> ... <NA> <NA> 3767148217
816 <NA> False 1403 ... <NA> <NA> 1941864748
817 <NA> True <NA> ... <NA> <NA> 3137914998
[818 rows x 29 columns]
In[71]: df.to_csv('temp.csv')
In[72]: pd.read_csv('temp.csv')
Traceback (most recent call last):
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-72-7315a31c9d2d>", line 1, in <module>
pd.read_csv('temp.csv')
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 686, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 458, in _read
data = parser.read(nrows)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 1196, in read
ret = self._engine.read(nrows)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 2155, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 862, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 918, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 2042, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
```
#### Problem description
The parse error occurs when we try to read the csv file generated by pandas dataframe. This error occurs to this specific dataframe. Not sure what the reason is. Hence attaching the dataframe in parquet file.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-52-generic
Version : #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.3
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
Cross referencing to cudf fuzz test thread for tracking: https://github.com/rapidsai/cudf/issues/6001 | {
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} | 2 | 2020-11-04T04:43:10Z | 2020-11-05T00:47:18Z | 2020-11-05T00:40:04Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Apply memory mapping first and then compression. The existing order didn't work.
This adds support for memory mapping in `read_csv` when compression is specified (python and c engine). | {
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```python
import pandas as pd
import numpy as np
iterables = [['bar', 'baz'], ['one', 'two'], ['dog', 'cat']]
df = pd.DataFrame(np.random.randn(2, 8), columns=pd.MultiIndex.from_product(iterables, names=['first', 'second', 'third']))
df.sum(axis=1, level=['first', 'third'], skipna=False)
>>> df
first bar baz
second one two one two
third dog cat dog cat dog cat dog cat
0 -1.093149 1.087846 -0.067161 1.129021 -1.024021 1.519433 -0.349039 1.215196
1 0.622675 -1.479139 -0.057143 -0.377272 -1.061766 1.606416 -0.172099 -0.564745
```
#### Problem description
Dataframe.sum returns an error with MultiIndex columns, several levels and skipna=False. However it appears to work fine with the transposed version of the dataframe (and axis=0), or with a single level, or with skipna=True.
```python
>>> df.sum(axis=1, level=['first', 'third'], skipna=False)
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 368, in _get_axis_number
return cls._AXIS_TO_AXIS_NUMBER[axis]
KeyError: 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 11419, in stat_func
return self._agg_by_level(
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 10258, in _agg_by_level
return grouped.aggregate(applyf)
File "/usr/lib/python3/dist-packages/pandas/core/groupby/generic.py", line 959, in aggregate
return self._python_agg_general(func, *args, **kwargs)
File "/usr/lib/python3/dist-packages/pandas/core/groupby/groupby.py", line 1083, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "/usr/lib/python3/dist-packages/pandas/core/groupby/ops.py", line 641, in agg_series
return self._aggregate_series_pure_python(obj, func)
File "/usr/lib/python3/dist-packages/pandas/core/groupby/ops.py", line 701, in _aggregate_series_pure_python
res = func(group, *args, **kwargs)
File "/usr/lib/python3/dist-packages/pandas/core/groupby/groupby.py", line 1060, in <lambda>
f = lambda x: func(x, *args, **kwargs)
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 10257, in <lambda>
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 11422, in stat_func
return self._reduce(
File "/usr/lib/python3/dist-packages/pandas/core/series.py", line 4223, in _reduce
self._get_axis_number(axis)
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 370, in _get_axis_number
raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
ValueError: No axis named 1 for object type Series
```
#### Expected Output
I would expect a Dataframe with the sum of the columns. Please note that it works fine with MultiIndex index (i.e with the transposed Dataframe and axis=0)
```python
>>> df.transpose().sum(level=['first', 'third'], skipna=False)
0 1
first third
bar dog -1.160310 0.565531
cat 2.216866 -1.856411
baz dog -1.373060 -1.233865
cat 2.734629 1.041672
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.0-1-amd64
Version : #1 SMP Debian 5.9.1-1 (2020-10-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.1.3
numpy : 1.19.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 50.3.0
Cython : None
pytest : 4.6.11
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/37623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37623/events | https://github.com/pandas-dev/pandas/pull/37623 | 735,870,320 | MDExOlB1bGxSZXF1ZXN0NTE1MTgzMjEz | 37,623 | TST: 32bit dtype compat test_groupby_dropna | {
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} | 1 | 2020-11-04T07:07:07Z | 2020-11-06T15:38:24Z | 2020-11-04T11:04:33Z | MEMBER | null | Part of #36579
Related to ``test_groupby_nan_included``. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37624/events | https://github.com/pandas-dev/pandas/issues/37624 | 735,905,151 | MDU6SXNzdWU3MzU5MDUxNTE= | 37,624 | BUG: DataFrame doesn't roundtrip with HDFStore(..., format='table', dropna=True) | {
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... | open | false | null | [] | null | 0 | 2020-11-04T08:12:47Z | 2021-08-14T04:50:49Z | null | MEMBER | null | xref #37564
#### Example
``` python
In [3]: import numpy as np
...: import pandas as pd
...: import pandas._testing as tm
...: from pandas.tests.io.pytables.common import ensure_clean_path
...:
...: df_with_missing = pd.DataFrame(
...: {"col1": [0, np.nan, 2], "col2": [1, np.nan, np.nan]}
...: )
...: df_without_missing = pd.DataFrame({"col1":[0, 2], "col2": [1, np.nan]})
...:
...: setup_path = '/tmp/store'
...: with ensure_clean_path(setup_path) as path:
...: df_with_missing.to_hdf(path, "df_with_missing", dropna=True, index=False, format="table")
...: reloaded = pd.read_hdf(path, "df_with_missing")
...: tm.assert_frame_equal(df_without_missing, reloaded)
...:
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-3-77cd279acb0b> in <module>
13 df_with_missing.to_hdf(path, "df_with_missing", dropna=True, index=False, format="table")
14 reloaded = pd.read_hdf(path, "df_with_missing")
---> 15 tm.assert_frame_equal(df_without_missing, reloaded)
16
[... skipping hidden 2 frame]
/workspaces/pandas/pandas/_libs/testing.pyx in pandas._libs.testing.assert_almost_equal()
44
45
---> 46 cpdef assert_almost_equal(a, b,
47 rtol=1.e-5, atol=1.e-8,
48 bint check_dtype=True,
/workspaces/pandas/pandas/_libs/testing.pyx in pandas._libs.testing.assert_almost_equal()
159 msg = (f"{obj} values are different "
160 f"({np.round(diff * 100.0 / na, 5)} %)")
--> 161 raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
162
163 return True
/workspaces/pandas/pandas/_testing.py in raise_assert_detail(obj, message, left, right, diff, index_values)
1053 msg += f"\n[diff]: {diff}"
1054
-> 1055 raise AssertionError(msg)
1056
1057
AssertionError: DataFrame.index are different
DataFrame.index values are different (50.0 %)
[left]: RangeIndex(start=0, stop=2, step=1)
[right]: Int64Index([0, 2], dtype='int64')
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37625/events | https://github.com/pandas-dev/pandas/issues/37625 | 735,975,251 | MDU6SXNzdWU3MzU5NzUyNTE= | 37,625 | ENH: support none in header list | {
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"description": "read_exc... | open | false | null | [] | null | 4 | 2020-11-04T09:57:27Z | 2021-08-14T04:51:43Z | null | NONE | null | I have a file with two sheets, one has a header, and another hasn't.
The following code will raise `TypeError: list indices must be integers or slices, not NoneType.`
```python
# Your code here, if applicable
df = pd.read_excel(file_path, sheet_name=None, header=[0, None])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/37626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37626/events | https://github.com/pandas-dev/pandas/issues/37626 | 736,004,530 | MDU6SXNzdWU3MzYwMDQ1MzA= | 37,626 | BUG: Collection of inconsistencies in .astype conversions | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 5 | 2020-11-04T10:38:56Z | 2021-09-08T07:14:15Z | null | CONTRIBUTOR | null | I have a use case where (automatic) casting between the following `pandas` dtypes is necessary;
`bool`, `boolean`, `int64`, `Int64`, `float64`, `object` and `string`.
Note that `boolean`, `Int64` and `string` are the new pandas 1.0 nullable dtypes.
The default approach for this would be `series.astype(target_dtype)`, for `target_dtype` one of the above dtypes as strings. This works (given no issues with missings) but for the inconsistencies below:
```python
In [1]: import pandas as pd
...: import numpy as np
```
#### `pd.NA` to `float`:
Summary: Casting `float(pd.NA)` raises a `TypeError` (as does casting `float(None)`). While `np.array([None, "1"]).astype("float")` works (and gets called [here](https://github.com/pandas-dev/pandas/blob/31db14c489224e92fcc8729ad44a2ebb5114db22/pandas/core/dtypes/cast.py#L1122)), the same call with `pd.NA` fails.
```python
In [2]: pd.Series(["1", "2", pd.NA], dtype="object").astype("float")
TypeError: float() argument must be a string or a number, not 'NAType'
In [3]: pd.Series(["1", "2", pd.NA], dtype="string").astype("float")
TypeError: float() argument must be a string or a number, not 'NAType'
In [4]: pd.Series(["1", "2", pd.NA], dtype="string").astype("category").astype("float") # magic workaround
Out[4]:
0 1.0
1 2.0
2 NaN
dtype: float64
```
Edit: Fixed with https://github.com/pandas-dev/pandas/pull/37974.
#### `object` / `string` to `Int64` (nullable)
Summary: `object` columns cannot be casted to `Int64`. Casting
- `object` -> `string` -> `Int64` works.
- `object` -> `float` -> `Int64` works if the data does not contain `pd.NA` (see above)
- `object` -> `int64` -> `Int64` works if the data does not contain missings.
```python
In [5]: pd.Series(["1", "2", "3"]).astype("Int64")
TypeError: object cannot be converted to an IntegerDtype
In [5]: pd.Series(["1", "2", "3"], dtype="string").astype("Int64")
Out[5]:
0 1
1 2
2 3
dtype: Int64
In [6]: pd.Series(["1", "2", "3"]).astype("int").astype("Int64")
Out[6]:
0 1
1 2
2 3
dtype: Int64
In [7]: pd.Series(["1", "2", None]).astype("int").astype("Int64") # int columns cannot contain missings
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
In [8]: pd.Series(["1", "2", None]).astype("float").astype("Int64") # magic workaround
Out[8]:
0 1
1 2
2 <NA>
dtype: Int64
In [9]: pd.Series(["1", "2", pd.NA], dtype="object").astype("float").astype("Int64") # see pd.NA -> float
TypeError: float() argument must be a string or a number, not 'NAType'
In [10]: pd.Series(["1", "2", pd.NA], dtype="object").astype("string").astype("Int64")
Out[10]:
0 1
1 2
2 <NA>
dtype: Int64
```
Related: https://github.com/pandas-dev/pandas/issues/25472#issuecomment-630752912
#### `string` / `object` to `bool` or `boolean`
Summary: Casting `string` or `object` columns to `bool` or `boolean` behaves strangely. I am not sure what the expected behaviour for `string` / `object` to `bool` / `boolean` should be. It would be nice to have consistent behaviour.
- `string` / `object` -> `bool` works if there are no missings, but yields only `True`
- `string` / `object` -> `boolean` raises
```python
In [11]: pd.Series(["True", "False", "bogus"], dtype="string").astype("bool") # everything is True
Out[11]:
0 True
1 True
2 True
dtype: bool
In [12]: pd.Series(["True", "False", "bogus"], dtype="object").astype("bool")
Out[12]:
0 True
1 True
2 True
dtype: bool
In [13]: pd.Series(["True", "False", "bogus"], dtype="string").astype("boolean")
TypeError: data type not understood
In [14]: pd.Series(["True", "False", "bogus"], dtype="object").astype("boolean")
TypeError: Need to pass bool-like values
```
#### `int` (non-nullable) to `boolean`
Summary: Casting from (non-nullable) `int64` to (nullable) `boolean` raises.
- `int64` -> `Int64` -> `boolean` works
- `int64` -> `bool` -> `boolean` works as long as there are no missings.
```python
In [15] pd.Series([-1, 0, 1], dtype="int").astype("boolean")
TypeError: Need to pass bool-like values
In [16]: pd.Series([-1, 0, 1], dtype="Int64").astype("boolean")
Out[16]:
0 True
1 False
2 True
dtype: boolean
In [17]: pd.Series([-1, 0, 1], dtype="int").astype("bool").astype("boolean")
Out[17]:
0 True
1 False
2 True
dtype: boolean
```
Related: https://github.com/pandas-dev/pandas/issues/37614
While there exist separate issues for the first and last report, I gathered that it might be nice to have a collection of these somewhere, which I did not find.
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 15f843ab102d7a0cd7f1c7870dfec72d0e28d252
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-52-generic
Version : #57~18.04.1-Ubuntu SMP Thu Oct 15 14:04:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+1059.g15f843ab1.dirty
numpy : 1.18.5
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.41.1
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
```
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
d = pd.DataFrame({'a':[1,1,2,1], 'b':[(1,1,0)]*4})
d.loc[d['a']==1, 'b'] = [[(0, 0, 1)]]
```
```python-traceback
Traceback (most recent call last):
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3418, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-7-9e18787827bf>", line 1, in <module>
d.loc[d['a']==1, 'b'] = [[(0, 0, 1)]]
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/pandas/core/indexing.py", line 670, in __setitem__
iloc._setitem_with_indexer(indexer, value)
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/pandas/core/indexing.py", line 1666, in _setitem_with_indexer
raise ValueError(
ValueError: cannot set using a multi-index selection indexer with a different length than the value
```
And similarly:
```python
import pandas as pd
d = pd.DataFrame({'a':[1,1,2,1], 'b':[(1,1,0)]*4})
ixs = [0,1,3]
vals = [[(0,0,1)]*len(ixs)]
d.loc[ixs,['b']] = vals
```
```python-traceback
Traceback (most recent call last):
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3418, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-8-73385df8ecbc>", line 1, in <module>
d.loc[ixs,['b']] = vals
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/pandas/core/indexing.py", line 670, in __setitem__
iloc._setitem_with_indexer(indexer, value)
File "/opt/anaconda3/envs/test/lib/python3.8/site-packages/pandas/core/indexing.py", line 1666, in _setitem_with_indexer
raise ValueError(
ValueError: cannot set using a multi-index selection indexer with a different length than the value
```
Also see related [SO question](https://stackoverflow.com/questions/59922621/set-pandas-dataframe-entries-by-boolean-index-to-tuple-values).
#### Problem description
The code posted above works until pandas version 1.0.5, and fails in 1.1.0. I checked deprecation notes for 1.1.0 and could not identify if this is expected behaviour now.
#### Expected Output
After both assignment operations, `d` should have the following contents:
```
>>> print(d)
a b
0 1 (0, 0, 1)
1 1 (0, 0, 1)
2 2 (1, 1, 0)
3 1 (0, 0, 1)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201103
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 : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2020-11-04T14:39:25Z | 2020-11-04T16:58:27Z | 2020-11-04T16:53:39Z | CONTRIBUTOR | null | I'm not entirely sure about this one, but "If an ndarray is passed, the values are used as-is determine the groups." does sound odd to me, so I do believe it was missing the "to".
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} | 6 | 2020-11-04T15:09:52Z | 2020-11-09T00:38:08Z | 2020-11-09T00:38:08Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
s1 = pd.Series(list("abc")).astype("category").iloc[[0]]
s2 = pickle.loads(pickle.dumps(s1))
print(s1.dropna())
print(s2.dropna())
```
```bash
0 a
dtype: category
Categories (3, object): ['a', 'b', 'c']
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-1-c7b1204ccdb7> in <module>
6 s2 = pickle.loads(pickle.dumps(s1))
7 print(s1.dropna())
----> 8 print(s2.dropna())
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/series.py in dropna(self, axis, inplace, how)
4883
4884 if self._can_hold_na:
-> 4885 result = remove_na_arraylike(self)
4886 if inplace:
4887 self._update_inplace(result)
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in remove_na_arraylike(arr)
564 """
565 if is_extension_array_dtype(arr):
--> 566 return arr[notna(arr)]
567 else:
568 return arr[notna(np.asarray(arr))]
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/series.py in __getitem__(self, key)
904 key = check_bool_indexer(self.index, key)
905 key = np.asarray(key, dtype=bool)
--> 906 return self._get_values(key)
907
908 return self._get_with(key)
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/series.py in _get_values(self, indexer)
966 def _get_values(self, indexer):
967 try:
--> 968 return self._constructor(self._mgr.get_slice(indexer)).__finalize__(self,)
969 except ValueError:
970 # mpl compat if we look up e.g. ser[:, np.newaxis];
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/internals/managers.py in get_slice(self, slobj, axis)
1559
1560 blk = self._block
-> 1561 array = blk._slice(slobj)
1562 block = blk.make_block_same_class(array, placement=slice(0, len(array)))
1563 return type(self)(block, self.index[slobj])
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/internals/blocks.py in _slice(self, slicer)
1756 if not isinstance(first, slice):
1757 raise AssertionError(
-> 1758 "invalid slicing for a 1-ndim ExtensionArray", first
1759 )
1760 # GH#32959 only full-slicers along fake-dim0 are valid
AssertionError: ('invalid slicing for a 1-ndim ExtensionArray', array([ True]))
```
#### Problem description
Not sure what changes in the serialization roundtrip through pickle, but it seems the copied Series cannot be indexed with a Boolean slice anymore, tripping up `dropna()` as a result. The following code more directly exposes the error:
```python
s1 = pd.Series(list("abc")).astype("category").iloc[[0]]
s2 = pickle.loads(pickle.dumps(s1))
s2[[True]]
```
The error happens e.g. when processing multiple Series in parallel (triggering serialization with pickle), and when a categorical Series has been filtered down to a single row. With another dtype, or more than one row, this error doesn't get triggered.
The regression must been introduced in version 1.1.0, as in 1.0.5 the above code works as expected.
#### Expected Output
Behaviour of slicing, `dropna` etc. should be same before and after pickling a Series, and independent of the number of rows.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 46.1.1.post20200322
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.46.0
</details>
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} | 1 | 2020-11-04T15:53:54Z | 2020-11-04T17:34:04Z | 2020-11-04T17:26:23Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Added dataclass constructor for dataframe into the Pandas in pandas/init.py line 363 and 426. | {
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} | 5 | 2020-11-04T16:46:33Z | 2020-12-05T17:18:21Z | 2020-12-05T17:18:21Z | MEMBER | null | Backport PR #37613
will update milestones on PR and associated issues once this is green-ish. Will also need to update release notes on master. (move from 1.2 to 1.1.5) | {
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} | 5 | 2020-11-04T19:44:14Z | 2020-11-14T02:21:09Z | 2020-11-14T02:21:09Z | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Minimal Working Example
One of the documentation examples, slightly modified:
```python
import pandas as pd
df = pd.DataFrame({
"lev1": [1, 1, 1, 2, 2, 2],
"lev2": [1, 1, 2, 1, 1, 2],
"lev3": [1, 2, 1, 2, 1, 2],
"lev4": [1, 2, 3, 4, 5, 6],
"values": [0, 1, 2, 3, 4, 5]})
a = ["lev1", "lev2"]
b=["lev3"]
df.pivot(index=a, columns=b)
print(a)
```
Displays:
```
['lev1', 'lev2', 'lev3']
```
#### Problem description
I would expect that my arguments passed to `.pivot` remain unchanged. I understand that lists, being mutable, can be modified by a function when they are used as arguments. I also understand that I can pass a set here instead of a list to keep it from being modified. However, I think it's reasonable to expect functions to be side-effect free in this case, especially when the documentation asks for a list and does not mention side effects. (FWIW, this behavior does not occur if I set the `values=` parameter).
The issue cropped up when I wanted to `.pivot` multiple DataFrames using the same index. To do so, I created a partial function of `.pivot` with an anonymous list as the `index=` paramter. Here's an MWE using the dataframe from above:
```python
from functools import partial
alt_pivot = lambda df, index, columns: df.pivot(index=index, columns=columns)
ppivot = partial(alt_pivot, index=["lev1", "lev2"])
print(ppivot)
ppivot(df, columns=b)
print(ppivot)
```
produces the output:
```
functools.partial(<function <lambda> at 0x00000159C45EAEE8>, index=['lev1', 'lev2'])
functools.partial(<function <lambda> at 0x00000159C45EAEE8>, index=['lev1', 'lev2', 'lev3'])
```
#### Expected Output
In the MWE above,
```python
print(a)
```
produces
```
['lev1', 'lev2']
```
#### Output of ``pd.show_versions()``
(Version in my project. Confirmed same behavior in `pandas==1.1.4` and `master` branch.)
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.16299
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2020-11-04T20:41:02Z | 2020-11-05T14:00:30Z | 2020-11-05T12:35:47Z | CONTRIBUTOR | null | "columns(s)" sounded odd, I believe it was supposed to be just "column(s)".
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} | 3 | 2020-11-04T23:49:27Z | 2020-11-11T15:35:04Z | 2020-11-11T00:19:19Z | MEMBER | null | Documenting the MO behind the test organization we've been doing.
1) I expect this is not valid rst ATM
2) The style does not match the rest of the docs, so probably needs a re-write.
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https://api.github.com/repos/pandas-dev/pandas/issues/37639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37639/events | https://github.com/pandas-dev/pandas/pull/37639 | 736,573,305 | MDExOlB1bGxSZXF1ZXN0NTE1NzYyODEz | 37,639 | REF: move get_filepath_buffer into get_handle | {
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} | 7 | 2020-11-05T03:10:46Z | 2020-12-11T04:34:31Z | 2020-11-13T13:39:29Z | CONTRIBUTOR | null | - [x] closes #33987
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This PR makes `get_filepath_buffer` a private function and is called inside `get_handle` - one function to rule all of IO ;)
This PR will make it easier for future PRs to make the IO-related interface of `read/to_*` more consistent as most of them should support compression/memory mapping (for reading)/(binary) file handles/storage options.
Notes to keep track of future follow-up PRs:
- context manager for `get_handle`
- `storage_options` for `to_excel` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37640/events | https://github.com/pandas-dev/pandas/pull/37640 | 736,576,519 | MDExOlB1bGxSZXF1ZXN0NTE1NzY1NDYy | 37,640 | REF: de-duplicate _validate_insert_value with _validate_scalar | {
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} | 0 | 2020-11-05T03:19:43Z | 2020-11-06T00:39:03Z | 2020-11-06T00:31:07Z | MEMBER | null | There is only one place where `_validate_insert_value` isnt identical to `_validate_scalar`, and that is in `DatetimeLikeArrayMixin._validate_scalar`, which ATM does not unbox (which all the other `_validate_scalar` methods do). This updates `_validate_scalar` to unbox.
If we stopped there, `TimedeltaIndex.get_loc` would take a pretty significant performance hit, so I added a `unbox=True` kwarg to `_validate_scalar` that TDI.get_loc uses to keep perf flat.
Made `_validate_scalar` keyword-only past the first (non-self) argument.
Last, renamed `Index._validate_scalar` to `Index._require_scalar` to disambiguate the names. (one of the two uses of `Index._validate_scalar` is wrong and will be removed, but thats a whole different can of worms) | {
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} | 2 | 2020-11-05T03:48:46Z | 2020-11-09T18:56:37Z | 2020-11-09T18:56:37Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
arrays = [['val1', 'val1', 'val2'], ['val1', 'val1', 'val2']]
index = pd.MultiIndex.from_arrays(arrays, names=('idx1', 'idx2'))
s = pd.Series([1, 2, 3], index=index)
print(
s.groupby(["idx1", "idx2"], group_keys=False).rolling(1).mean()
)
```
#### Problem description
the group_keys parameter in Seried.groupby() is broken since 1.1.0 when using .rolling()
It does not seem to be an issue if another function instead of *rolling* is used, eg
```python
s.groupby(["idx1", "idx2"], group_keys=False).mean()
```
In pandas=1.1.{0,1} the output is
idx1 idx2
val1 val1 (val1, val1) 1.0
val1 (val1, val1) 2.0
val2 val2 (val2, val1) 3.0
In pandas=1.1.{2,3,4} the output is
idx1 idx2 idx1 idx2
val1 val1 val1 val1 1.0
val1 2.0
val2 val2 val2 val2 3.0
#### Expected Output (as per pandas 1.0.*)
idx1 idx2
val1 val1 1.0
val1 2.0
val2 val2 3.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.6.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17134
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.1
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 10.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.0
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 3 | 2020-11-05T03:59:54Z | 2020-11-06T23:55:19Z | null | MEMBER | null | On 1.2 master:
```python
In [15]: import pandas as pd
...: import pyarrow as pa
...:
...: df = pd.DataFrame({'a': pa.scalar(7)}, index=['a'])
...: print(type(df.to_dict(orient="records")[0]['a']))
...:
<class 'pyarrow.lib.Int64Scalar'>
```
This is inconsistent with the corresponding op for NumPy backed types (xref #37571)
```
In [13]: import pandas as pd
...: import numpy as np
...:
...: df = pd.DataFrame({'a': np.int(7)}, index=['a'])
...: print(type(df.to_dict(orient="records")[0]['a']))
...:
<class 'int'>
```
In general we'd like `to_dict` to return Python native types if possible. | {
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"gists... | null | 13 | 2020-11-05T04:26:23Z | 2021-10-02T04:09:06Z | 2021-10-02T04:09:05Z | MEMBER | null | - [x] `Categorical.remove_unused_categories` https://github.com/pandas-dev/pandas/pull/37918
- [x] `Cateogrical.remove_categories` #37981
- [x] `Categorical.add_categories` https://github.com/pandas-dev/pandas/pull/41118
- [x] `Categorical.reorder_categories` https://github.com/pandas-dev/pandas/pull/41133
- [x] `Categorical.rename_categories` #41186
- [x] `Categorical.set_categories` https://github.com/pandas-dev/pandas/pull/41307
The ability to alter an existing array's dtype is a footgun. We can return a new object without having to make a copy. | {
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} | 5 | 2020-11-05T09:54:34Z | 2020-11-09T13:10:19Z | 2020-11-08T02:22:48Z | MEMBER | null | - [X] closes #37610
- [X] 1 tests added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Problem
We currently lose `fold` information (whether the Timestamp corresponds to the first or second instance of wall clock time in a DST transition) when calling `Timestamp.replace`.
## Solution
A simple addition to the code of `Timestamp.replace` should fix this.
## Test
I added the test for the use case I came up with in the issue discussion. The OP example is losing `fold` when deleting timezone information with `Timetsamp.replace`, and that's not really a bug, but replacing a valid dateutil timezone with itself and losing `fold` definitely is.
The proposed solution fixes the original example as well. I just don't think we should be tracking it in tests, as it's not clear to me why `fold` must be preserved in a `Timestamp` with no timezone information (but it is the convention recommended in [PEP 495, fold section](https://www.python.org/dev/peps/pep-0495/#the-fold-attribute) to let users keep invalid fold and to just ignore it).
## Some details
IIRC, we ignore `fold`, when it doesn't do anything, so we should be safe preserving `fold` while replacing tzinfo with None, as in the OP example. I remember this coming up when we introduced fold support, and we made sure that the fold-aware functions don't care what fold is outside of a DST transition with a dateutil timezone (this was done to satisfy the requirements of [PEP 495, fold section](https://www.python.org/dev/peps/pep-0495/#the-fold-attribute)). | {
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"description": "IO issue... | open | false | null | [] | null | 0 | 2020-11-05T12:26:08Z | 2021-08-14T04:54:02Z | null | NONE | null | #### Is your feature request related to a problem?
Currently, dates and datetimes are written correctly to ODS, but the cells are not marked as containing datetime data (LibreOffice Calc interprets them as numbers), and also the `date_format` and `datetime_format` arguments to ExcelWriter are currently ignored by the odswriter.
#### Describe the solution you'd like
`_odswriter.py` should make use of these arguments.
#### API breaking implications
None.
#### Describe alternatives you've considered
I cannot think of alternatives.
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reproducer:
DataFrame([{"B": 1, "A": 2}]).to_dict(orient='records', into=OrderedDict)
> [OrderedDict([('A', 2), ('B', 1)])]
expected behavior:
DataFrame([{"B": 1, "A": 2}]).to_dict(orient='records', into=OrderedDict)
> [OrderedDict([('B', 1), ('A', 2)])]
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I would like to be able to use Pandas datetime datatype column for datetimes beyond the bounds for `ns` units (which are `1677-09-21 00:12:43.145225` and `2262-04-11 23:47:16.854775807`).
As will often be dealing with dates above and below these thresholds. Apologies if already open on another issue but couldn't quite find this. Not sure if tied in with [this issue](https://github.com/pydata/xarray/issues/789)
#### Describe the solution you'd like
```python
df = pd.DataFrame({"col": ["2020-01-01", "2999-01-01", "1990-01-01", None]})
```
One of the following:
```python
import pandas as pd
df = pd.DataFrame({"col": ["2020-01-01", "2999-01-01", "1990-01-01", None]})
df.col = pd.to_datetime(df.col, base_unit="ms")
df.col.dtype # datetime64[ms]
```
or
```python
import pandas as pd
import numpy as np
df = pd.DataFrame()
df["col"] = np.array(["2020-01-01", "2999-01-01", "1990-01-01", None], dtype='datetime64[ms]')
df.col.dtype # datetime64[ms]
```
#### API breaking implications
Not sure, I assume this might be a big ask depending on how the column casting internals work.
#### Describe alternatives you've considered
At the moment the only workaround is having an object column with datetimes. However I believe this stops you from using the `.dt` method on the column which is really handy.
```
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} | 15 | 2020-11-05T15:01:59Z | 2021-02-19T01:59:09Z | 2021-02-19T01:12:56Z | MEMBER | null | - [x] xref #25969
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This resolves the issue of return types from `to_dict`. #25969 also discusses return types from `.items()`, which relates to an outstanding NumPy issue numpy/numpy#14139, and I don't address that part here atm | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 0 | 2020-11-05T16:06:59Z | 2020-11-06T00:11:38Z | 2020-11-06T00:09:03Z | MEMBER | null | Seeing a bunch of suppressed exceptions in the py38 windows tests. Hopefully this will cause them to surface correctly. | {
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} | 9 | 2020-11-05T17:25:11Z | 2021-11-20T18:32:47Z | null | NONE | null | #### Is your feature request related to a problem?
With the increasing support for PyPy3 in NumPy, it would be helpful if pandas started testing against PyPy3.
#### Describe the solution you'd like
Please consider adding `pypy3` to the `.travis.yml` (allowed to fail for now).
#### API breaking implications
No expected API breaks
#### Describe alternatives you've considered
This is more of a portability concern than anything else.
#### Additional context
Something like:
```yaml
matrix:
- python: pypy3
env:
- JOB="pypy3" PATTERN="(not slow and not network and not clipboard)"
allow_failures:
- python: pypy3
env:
- JOB="pypy3" PATTERN="(not slow and not network and not clipboard)"
``` | {
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... | null | 22 | 2020-11-05T21:11:44Z | 2021-10-13T16:07:09Z | null | MEMBER | null | ATM we support pytz, dateutil, and datetime.timezone tzinfos. Now that it is part of the stdlib py39, we should also support zoneinfo tzinfos. | {
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} | 16 | 2020-11-05T21:29:47Z | 2020-11-13T05:52:50Z | 2020-11-13T02:51:08Z | CONTRIBUTOR | null | Created instead of #37538
- [x] closes #34769
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I moved the file to a new directory.
What is the best structure for this folder?
[before_test_ploting_frame](https://gist.github.com/Mikhaylov-yv/999de1451e8ecc836215b30db34ef841)
[after_test_ploting_frame](https://gist.github.com/Mikhaylov-yv/98eca32c21f59cc9e7653cd96d5fabd2) | {
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- [x ] passes `black pandas`
- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry - not sure how to do this. I guess I have to wait until pull request is complete.
This change is a a feature I often want, which is the ability to add raw data on top of a boxplot to get a better idea of data distribution.
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- [x] tests added / passed
- [x] passes `black pandas`
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 6 | 2020-11-06T12:40:35Z | 2020-11-07T15:22:04Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([0,1,2]).append(pd.DataFrame([3,4,5]))
df.columns = ['a']
print(df - df.a)
```
#### Problem description
Why we can not to do binary operations when we work with `DataFrame` and `Series`(dataframe has 1 columns, `len(df.index)==len(series.index)) with duplicated indexes? If left and right operands have the same type (`DataFrame/DataFrame` for example) it works.
#### Expected Output
```
0 0
1 0
2 0
0 0
1 0
2 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-50-generic
Version : #54-Ubuntu SMP Mon May 6 18:46:08 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US
LOCALE : en_US.ISO8859-1
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : 0.14.0
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2020-11-06T13:23:06Z | 2020-11-09T11:33:41Z | 2020-11-08T02:11:13Z | MEMBER | null | - [ ] closes #37604
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I tried to address #37604, but it turns out that on some occasions it is absolutely mandatory to return more colors than ``num_colors`` says.
In particular, this is the test that cannot be updated and the underlying code cannot be easily modified as well.
```
pandas/tests/plotting/test_frame.py
...
class TestDataFramePlots(TestPlotBase):
...
def test_bar_user_colors(self):
df = DataFrame(
{"A": range(4), "B": range(1, 5), "color": ["red", "blue", "blue", "red"]}
)
# This should *only* work when `y` is specified, else
# we use one color per column
ax = df.plot.bar(y="A", color=df["color"])
result = [p.get_facecolor() for p in ax.patches]
expected = [
(1.0, 0.0, 0.0, 1.0),
(0.0, 0.0, 1.0, 1.0),
(0.0, 0.0, 1.0, 1.0),
(1.0, 0.0, 0.0, 1.0),
]
assert result == expected
```
When "y" is specified, then the number of colors must be the length of the series df["A"],
but inside the BarPlot class there is no opportunity to check for that condition.
So, let ``get_standard_colors`` return maximum of ``num_colors`` and ``len(colors)``.
This way all tests work without modifications,
just minor improvement in the cycling function. | {
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- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 4 | 2020-11-06T15:47:11Z | 2020-11-09T11:34:18Z | 2020-11-07T14:43:52Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Follow up on #36982.
Since, currently ``assert_produces_warning`` now allows matching warning message,
instead of the comment we will match the message. | {
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} | 2 | 2020-11-06T16:04:19Z | 2020-11-08T01:05:39Z | 2020-11-08T00:34:52Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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} | 0 | 2020-11-06T16:10:31Z | 2020-11-08T03:00:20Z | 2020-11-08T03:00:20Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```bash
pytest -Werror pandas/tests/plotting/tests_frame.py::TestDataFramePlots::test_pie_df_nan
```
#### Problem description
When testing ``test_frame.py`` the following warning is issued:
```
============================================================================================================= warnings summary =============================================================================================================
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_pie_df_nan
pandas\plotting\_matplotlib\core.py:1551: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False
results = ax.pie(y, labels=blabels, **kwds)
-- Docs: https://docs.pytest.org/en/stable/warnings.html
```
This code is the reason for the warnings:
```
matplotlib/axes/_axes.py
...
def pie(...):
...
if normalize is None:
if sx < 1:
cbook.warn_deprecated(
"3.3", message="normalize=None does not normalize "
"if the sum is less than 1 but this behavior "
"is deprecated since %(since)s until %(removal)s. "
"After the deprecation "
"period the default value will be normalize=True. "
"To prevent normalization pass normalize=False ")
else:
normalize = True
```
#### Expected Output
No warnings.
#### Versions
The issue is present starting from matplotlib version 3.3.
Before that ``normalize`` kwarg was not applicable to ``plt.pie``.
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 210c06e2f29bdb968fa715756bef9dcf1dfdc818
python : 3.8.6.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_United States.1251
pandas : 1.2.0.dev0+1079.g210c06e2f
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.4
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details> | {
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} | 1 | 2020-11-06T16:13:48Z | 2020-11-09T11:33:52Z | 2020-11-08T03:00:21Z | MEMBER | null | - [ ] closes #37668
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fix MatplotlibDeprecationWarning for pie chart by specifically passing ``normalize=True``. | {
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The `DataFrame` constructor doesn't appear to be working correctly with
```python
In [3]: pd.DataFrame([[1], [2]], index=[["a", "b"], ["c", "d"]])
Out[3]:
0
a c 1
b d 2
```
I *think* we might expect
```python
In [3]: pd.DataFrame([[1], [2]], index=[["a", "b"], ["c", "d"]])
Out[3]:
0
a b 1
c d 2
```
FTR I'm not versant with the context here (whether this is in fact expected and we should maybe update the docs or if there is something to change) | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This fixes the bug, but I am not sure, if the solution is good enough.... | {
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Additionally checking for the label inside values, this fixes the problem. | {
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"color": "e1... | closed | false | null | [] | null | 14 | 2020-11-07T00:31:20Z | 2021-06-16T13:46:26Z | 2021-06-16T13:46:26Z | MEMBER | null | - [x] closes #26395
- [x] closes #20643
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
We have to keep the get_loc statement to check, if the series._set_value function does the trick. I added the dtype conversion there, to avoid implementing the same thing twice | {
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```
ci = pd.CategoricalIndex([(0, 1), (1, 2), pd.NA])
>>> ci.fillna((0, 1))
[...]
TypeError: 'value' must be a scalar, passed: tuple
>>> ci._data.fillna((0, 1))
[(0, 1), (1, 2), (0, 1)]
Categories (2, object): [(0, 1), (1, 2)]
```
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} | 1 | 2020-11-07T03:48:34Z | 2020-12-29T18:09:25Z | 2020-12-29T18:09:25Z | MEMBER | null | If in merge._maybe_add_join_keys we replace a call to Index.where with a call to Series.where, we get a test failure because of the following mismatch:
```
dr = pd.date_range("2001-01-01", periods=3)
lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT])._data
rvals = pd.Categorical([dr[0], pd.NaT, dr[2]])
mask = np.array([False, False, True])
>>> pd.Series(lvals).where(~mask, rvals)._values
array([Timestamp('2001-01-01 00:00:00'), Timestamp('2001-01-02 00:00:00'),
978480000000000000], dtype=object)
>>> pd.Index(lvals).where(~mask, rvals)._values
<DatetimeArray>
['2001-01-01 00:00:00', '2001-01-02 00:00:00', '2001-01-03 00:00:00']
Length: 3, dtype: datetime64[ns]
```
The Index version looks better behaved. These should share an implementation. | {
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} | 5 | 2020-11-07T04:52:12Z | 2020-11-08T02:08:02Z | 2020-11-08T01:47:27Z | MEMBER | null | Confirmed that the `regex` kwarg is always bool, which lets us remove some gymnastics (L2570-L2586). Then removed always-True `convert` kwarg. | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Resubmitting #35751 to see if I can get to green here | {
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No
#### Describe the solution you'd like
Let's imagine a excel database with multiple sheets
We can do `pd.read_excel(db, sheet_name=[0, 1], usecols=[0, 1])` and we would read the first and second columns of the first and second sheets.
Imagine we need to read the first and second columns from the first sheet and the second and third columns from the second sheet. A solution could be to pass to `usecols` a list of list, for example: `pd.read_excel(db, sheet_name=[0, 1], usecols=[[0,1], [1,2]])`. Both integer and string should be used inside the inner-list
#### API breaking implications
No, well I guess
#### Describe alternatives you've considered
Multiple run of `read_excel`
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- [x] tests added / passed
- [x] passes `black pandas`
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cc @jreback Added a test. The test above was the only one explicitly added for the ``KeyError``
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} | 4 | 2020-11-07T23:10:53Z | 2020-12-08T18:45:16Z | 2020-12-08T18:32:45Z | CONTRIBUTOR | null | This commit mainly adds examples on usage. This commit also fills in
information suggested by `validate_docstrings.py` script.
Ran
```
python scripts/validate_docstrings.py pandas.DataFrame.insert
```
to check docs and added in suggested information (e.g., See Also section).
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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We should add further tests for iloc/iat and different dtypes for all cases (loc, at, iloc, iat)
cc @jreback
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`df = pd.read_stata('MucA.dta')` produces the following df:
```
tinh huyen xa diaban hoso matv m1ac2 m1ac3 ... m1ac12 m1ac13 m1ac14a m1ac14b m1ac15a m1ac15b m1ac15c m1ac15d
0 Thµnh phè Hµ Néi 1 4 8 13 2 Nam Vî chång ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Cã Kh«ng
1 Thµnh phè Hµ Néi 1 4 8 13 1 N÷ Chñ hé ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Cã Kh«ng
2 Thµnh phè Hµ Néi 1 4 8 14 2 Nam Kh¸c ... N¬i kh¸c trong tØnh NaN NaN NaN Kh«ng Kh«ng Kh«ng Kh«ng
3 Thµnh phè Hµ Néi 1 4 8 14 1 N÷ Chñ hé ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Kh«ng Kh«ng
4 Thµnh phè Hµ Néi 1 4 8 15 2 N÷ Vî chång ... Trong x· phêng NaN NaN NaN Kh«ng Cã Kh«ng Kh«ng
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36076 TØnh Cµ Mau 973 32248 2 15 4 N÷ Con ... Trong x· phêng NaN NaN NaN NaN NaN NaN NaN
36077 TØnh Cµ Mau 973 32248 2 15 5 N÷ Con ... Trong x· phêng NaN NaN NaN NaN NaN NaN NaN
36078 TØnh Cµ Mau 973 32248 2 15 1 Nam Chñ hé ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Kh«ng Kh«ng
36079 TØnh Cµ Mau 973 32248 2 15 2 N÷ Vî chång ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Kh«ng Kh«ng
36080 TØnh Cµ Mau 973 32248 2 15 3 N÷ Con ... Trong x· phêng NaN NaN NaN Kh«ng Kh«ng Kh«ng Kh«ng
```
I want to try other encodings to restore the Vietnamese characters, how may I do so? | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-11-08T05:28:39Z | 2020-11-10T02:03:22Z | 2020-11-09T21:10:46Z | CONTRIBUTOR | null | Gregorian is a proper noun
(https://en.wikipedia.org/wiki/Proleptic_Gregorian_calendar). Also,
"proleptic" is modifying the compound noun "Gregorian epoch" rather than
"Gregorian" modifying a "proleptic epoch".
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 3 | 2020-11-08T07:56:26Z | 2021-02-28T13:02:30Z | 2021-02-28T13:02:30Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
self.conn = pymysql.connect(**info_dic)
pd.read_sql(sql, self.conn)
```
#### Problem description
line 53, in read_data_frame return pd.read_sql(sql, self.conn) File "/usr/local/lib/python3.6/site-packages/pandas/io/sql.py", line 394, in read_sql pandas_sql = pandasSQL_builder(con) File "/usr/local/lib/python3.6/site-packages/pandas/io/sql.py", line 532, in pandasSQL_builder if _is_sqlalchemy_connectable(con): File "/usr/local/lib/python3.6/site-packages/pandas/io/sql.py", line 86, in _is_sqlalchemy_connectable return isinstance(con, sqlalchemy.engine.Connectable) AttributeError: module 'sqlalchemy' has no attribute 'engine'
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.224-1.el7.elrepo.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8
pandas: 0.20.3
pytest: None
pip: 10.0.1
setuptools: 28.8.0
Cython: 0.28.1
numpy: 1.14.0
scipy: 1.0.0
xarray: None
IPython: None
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.2
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: 4.5.2
bs4: None
html5lib: None
sqlalchemy: 1.2.4
pymysql: 0.8.0
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2020-11-08T11:20:49Z | 2020-11-10T04:08:07Z | 2020-11-10T03:38:45Z | CONTRIBUTOR | null | - [x] closes #37490
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Going to add axis=1 tests as part of resolution of #9772, simplifying test first. | {
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} | 1 | 2020-11-08T15:44:52Z | 2020-11-11T20:31:02Z | 2020-11-11T20:30:48Z | CONTRIBUTOR | null | - [x] ref #31942
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} | 1 | 2020-11-08T18:16:28Z | 2020-11-09T00:39:32Z | 2020-11-09T00:37:00Z | MEMBER | null | This makes it so that we only call BlockManager.replace from one spot, so we can rule out more cases when reasoning about the arguments in ObjectBlock.replace.
This will have a merge conflict with #37696; doesnt matter which one goes in first. One both are in, there are further simplifications available.
I'm hopeful that we'll be able to get rid of Categorical.replace altogether (and with it, CategoricalBlock.replace, CategoricalBlock._replace_list), but that's a few steps away at best.
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} | 3 | 2020-11-08T20:47:34Z | 2020-11-11T02:30:17Z | 2020-11-11T02:30:16Z | NONE | null | ```python
import pandas as pd
from pathlib import Path
file_path_string = r'C:\Users\Desktop\my_file.html'
file_path = Path(file_path_string)
df1 = pd.read_html(file_path_string)[0] # works fine
df2 = pd.read_html(file_path)[0] # raises TypeError: cannot parse 'from WindowsPath'
```
Why do `read_excel()`, `read_csv()`, `to_excel()`, `to_csv()`, ... support `pathlib.Path` while `read_html()` doesn't? | {
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} | 2 | 2020-11-08T22:31:11Z | 2020-11-09T20:29:14Z | 2020-11-09T20:27:35Z | MEMBER | null | - [x] closes #27104
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This confused ``-1`` representing missing values in input with the ``-1`` signaling which components to exclude from output. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37707/events | https://github.com/pandas-dev/pandas/pull/37707 | 738,601,642 | MDExOlB1bGxSZXF1ZXN0NTE3NDA3MTM1 | 37,707 | Bug in loc raised Error when non-integer slice was given for MultiIndex | {
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} | 7 | 2020-11-09T01:05:41Z | 2020-11-24T22:01:54Z | 2020-11-24T21:59:58Z | MEMBER | null | - [x] closes #25165
- [x] closes #24263
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A slice return caused issues everywhere, when this function was used. So I unpacked it right in there. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37708/events | https://github.com/pandas-dev/pandas/issues/37708 | 738,614,722 | MDU6SXNzdWU3Mzg2MTQ3MjI= | 37,708 | BUG: Wrong index after monthly resampling | {
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"description": "Issue that has not been ... | closed | false | null | [] | null | 3 | 2020-11-09T01:53:36Z | 2020-11-09T02:54:31Z | 2020-11-09T02:54:31Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Reproduce
```python
df_sent = df_date.groupby('name').resample('M').count()
```
**df_date**
**df_sent**
#### Problem description
When i resample my dataframe to month, it gives wrong index. As you can see my dataframe (__df_date__) has dates from __2020-06-13 10:23:00__ to __2020-09-11 00:22:00__ which has month **6** to **9** so if i resample to month, i expect new dataframe's months from __2020-06__ to __2020-09__.
#### Expected Output
After resampling, new dataframe needs to has these months just like the above
I have searched a solution to this unexpected behaviour and couldn't find anything. If i have wrong intuition about resampling, please correct me. Also i have encountered the same problem in [stackoverflow](https://stackoverflow.com/questions/52527619/wrong-index-after-monthly-resampling-of-dataframe-pandas) which hasn't got a solution
#### Output of ``pd.show_versions()``
<details>
pandas version= 1.1.3
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.4-arch1-1
Version : #1 SMP PREEMPT Wed, 04 Nov 2020 21:41:09 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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"descript... | closed | false | null | [] | null | 2 | 2020-11-09T02:17:57Z | 2021-02-28T13:00:47Z | 2021-02-28T13:00:47Z | NONE | null | - [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).
---
#### Question about pandas
I tried two different lines of code.
Both of them gave me the desired outcome, which replaced the NANs by '0'. However, the first line did not give me settingwithcopy warning, the second one did. I am so confused. Can anyone tell me why? Is that because the X.loc[0:, 'Type'] returned a view and X.loc[:, 'Type'] returned a copy? If the second one returned a copy, why X's NANs have been replaced?
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
X.loc[0:, 'Type'].fillna('0', inplace=True)
X.loc[:, 'Type'].fillna('0', inplace=True)
```
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} | 4 | 2020-11-09T09:20:16Z | 2020-11-15T03:36:03Z | 2020-11-14T16:51:58Z | NONE | null | I often use `.loc[(slice(...),),]` for selection and modifying when dealing with both series and dataframe: see
[advanced-indexing-with-hierarchical-index](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-indexing-with-hierarchical-index)
> In [43]: df.loc['bar']
Out[43]:
A B C
second
one 0.895717 0.410835 -1.413681
two 0.805244 0.813850 1.607920
> This is a shortcut for the slightly more verbose notation df.loc[('bar',),] (equivalent to df.loc['bar',] in this example).
Every thinkg is OK in `0.25.4`, but in `1.1.4` (also `1.0.3` and `1.1.3`), only selection is valid and modifying will raise errors:
```python
import pandas as pd
s = pd.Series([1,2,3], index=pd.MultiIndex.from_tuples([('a','A'), ('b','B'), ('c', 'C')]))
df = s.to_frame()
print(s.loc[('a', ), ])
print(df.loc[('a', ), ])
```
but when I try to modify a subset of rows using `.loc[(slice(...),),]`
```python
In [2]: df.loc[('a', ), ] = 0
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-2-480fc1763dde> in <module>
----> 1 df.loc[('a', ), ] = 0
~/opt/anaconda3/envs/edge/lib/python3.8/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
664 else:
665 key = com.apply_if_callable(key, self.obj)
--> 666 indexer = self._get_setitem_indexer(key)
667 self._has_valid_setitem_indexer(key)
668
~/opt/anaconda3/envs/edge/lib/python3.8/site-packages/pandas/core/indexing.py in _get_setitem_indexer(self, key)
591 """
592 if self.name == "loc":
--> 593 self._ensure_listlike_indexer(key)
594
595 if self.axis is not None:
~/opt/anaconda3/envs/edge/lib/python3.8/site-packages/pandas/core/indexing.py in _ensure_listlike_indexer(self, key, axis)
645 # key may be a tuple if we are .loc
646 # in that case, set key to the column part of key
--> 647 key = key[column_axis]
648 axis = column_axis
649
IndexError: tuple index out of range
```
and
```python
In [3]: s.loc[('a', ), ] = 0
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-3-c3a2e38a0417> in <module>
----> 1 s.loc[('a', ), ] = 0
~/opt/anaconda3/envs/edge/lib/python3.8/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
668
669 iloc = self if self.name == "iloc" else self.obj.iloc
--> 670 iloc._setitem_with_indexer(indexer, value)
671
672 def _validate_key(self, key, axis: int):
~/opt/anaconda3/envs/edge/lib/python3.8/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1633 if take_split_path:
1634 # Above we only set take_split_path to True for 2D cases
-> 1635 assert self.ndim == 2
1636 assert info_axis == 1
1637
AssertionError:
```
Note that “partial” indexing `.loc[slice(...)]` is still valid in ``1.1.4`:
```python
In [4]: df.loc['a'] = 0
In [5]: s.loc['a'] = 0
In [6]: df
Out[6]:
0
a A 0
b B 2
c C 3
In [7]: s
Out[7]:
a A 0
b B 2
c C 3
dtype: int64
```
Of course, `.loc[('a', )]` is valid for series and `.loc[('a', ), : ]` is vaild for dataframe, but I have to use different codes for series and dataframe.
Update on 20201113:
-----------------------
- For series, `s.loc[('a')] = 0`, `s.loc[('a', )] = 0` and `s.loc[('a'), ] = 0` are vaild, but `s.loc[('a', ), ] = 0` will raise similar AssertionError like above.
- For dataframe, `df.loc[('a')] = 0`, `df.loc[('a'), :] = 0` and `df.loc[('a',), :] = 0` are vaild, but `df.loc[('a', )] = 0` , `df.loc[('a'), ] = 0` and `df.loc[('a', ), ] = 0` will raise similar IndexError like above. | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 5))
df = df.rolling(5).cov().dropna()
df.index.levels[0]
```
#### Current Output
```python
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
```
#### Problem description
Since doing a rolling covariance would cause the first 4 entries to the dataframe to be nan, which I dropped. However, if I try to access the first level of the index, I would get the full original index, being from 0 to 9. One would expect to receive an index for which the corresponding nan entries was removed for the new first level index. However,
```python
df.index.get_level_values(0)
```
gives the expected output.
#### Expected Output
```python
Int64Index([4, 5, 6, 7, 8, 9], dtype='int64')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.4
numpy : 1.19.3
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/37714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37714/events | https://github.com/pandas-dev/pandas/pull/37714 | 738,982,486 | MDExOlB1bGxSZXF1ZXN0NTE3NzI1MDkw | 37,714 | CLN: cleanup, deduplicate plotting/test_frame | {
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} | 1 | 2020-11-09T12:23:17Z | 2020-11-10T04:44:38Z | 2020-11-09T18:57:51Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
1. Make more reasonable var names
2. Get rid of one test in favor of parametrization in another one | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37715/events | https://github.com/pandas-dev/pandas/issues/37715 | 739,014,311 | MDU6SXNzdWU3MzkwMTQzMTE= | 37,715 | TYP: investigate/fix ignored mypy errors | {
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} | 19 | 2020-11-09T13:08:21Z | 2021-11-08T18:03:32Z | null | MEMBER | null | In #37556 `# type: ignore` were added to silence mypy errors to be fixed 'later'. If an ignore is needed due to a error with the type checker, a comment with a reference to the mypy issue on the github mypy issue tracker should be included.
Further investigation or PRs welcome removing these ignores or adding comments with links to mypy issues if applicable. PRs should include 'xref #37715'
PRs could address just one error, a small handful of related errors or a complete module.
`grep -Ern "type: ?ignore" pandas/` currently gives 207 matches
<details>
```
pandas/tests/internals/test_internals.py:1258: assert df._mgr.blocks[0].values is arr # type:ignore[union-attr]
pandas/tests/indexes/object/test_indexing.py:73: (pd.IndexSlice["b":"y":-1], ""), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:74: (pd.IndexSlice["b"::-1], "b"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:75: (pd.IndexSlice[:"b":-1], "yxdcb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:76: (pd.IndexSlice[:"y":-1], "y"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:77: (pd.IndexSlice["y"::-1], "yxdcb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:78: (pd.IndexSlice["y"::-4], "yb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:80: (pd.IndexSlice[:"a":-1], "yxdcb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:81: (pd.IndexSlice[:"a":-2], "ydb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:82: (pd.IndexSlice["z"::-1], "yxdcb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:83: (pd.IndexSlice["z"::-3], "yc"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:84: (pd.IndexSlice["m"::-1], "dcb"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:85: (pd.IndexSlice[:"m":-1], "yx"), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:86: (pd.IndexSlice["a":"a":-1], ""), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:87: (pd.IndexSlice["z":"z":-1], ""), # type: ignore[misc]
pandas/tests/indexes/object/test_indexing.py:88: (pd.IndexSlice["m":"m":-1], ""), # type: ignore[misc]
pandas/tests/frame/indexing/test_setitem.py:229: + ["datetime64[ns, UTC]", "period[D]"], # type: ignore[list-item]
pandas/tests/io/xml/test_xml.py:683: filename, names="Col1, Col2, Col3", parser=parser # type: ignore[arg-type]
pandas/conftest.py:330: params=[pd.Index, pd.Series], ids=["index", "series"] # type: ignore[list-item]
pandas/_testing/_io.py:98: args = [] # type: ignore[assignment]
pandas/_testing/_io.py:219: errno = getattr(err.reason, "errno", None) # type: ignore[attr-defined]
pandas/_testing/__init__.py:574: idx = idx_func(nentries) # type: ignore[operator]
pandas/util/_decorators.py:93: return wrapper # type: ignore[return-value]
pandas/util/_decorators.py:341: func.__signature__ = sig # type: ignore[attr-defined]
pandas/util/_decorators.py:380: docstring._docstring_components # type: ignore[union-attr]
pandas/util/_decorators.py:396: decorated._docstring_components = ( # type: ignore[attr-defined]
pandas/core/generic.py:755: new_values, *new_axes # type: ignore[arg-type]
pandas/core/generic.py:4344: return self.rename(**mapper) # type: ignore[return-value, arg-type]
pandas/core/generic.py:4408: return self.rename(**mapper) # type: ignore[return-value, arg-type]
pandas/core/generic.py:10600: cls.any = any # type: ignore[assignment]
pandas/core/generic.py:10620: cls.all = all # type: ignore[assignment]
pandas/core/generic.py:10625: NDFrame.mad.__doc__, # type: ignore[arg-type]
pandas/core/generic.py:10638: cls.mad = mad # type: ignore[assignment]
pandas/core/generic.py:10661: cls.sem = sem # type: ignore[assignment]
pandas/core/generic.py:10683: cls.var = var # type: ignore[assignment]
pandas/core/generic.py:10706: cls.std = std # type: ignore[assignment]
pandas/core/generic.py:10721: cls.cummin = cummin # type: ignore[assignment]
pandas/core/generic.py:10736: cls.cummax = cummax # type: ignore[assignment]
pandas/core/generic.py:10751: cls.cumsum = cumsum # type: ignore[assignment]
pandas/core/generic.py:10766: cls.cumprod = cumprod # type: ignore[assignment]
pandas/core/generic.py:10793: cls.sum = sum # type: ignore[assignment]
pandas/core/generic.py:10819: cls.prod = prod # type: ignore[assignment]
pandas/core/generic.py:10836: cls.mean = mean # type: ignore[assignment]
pandas/core/generic.py:10852: cls.skew = skew # type: ignore[assignment]
pandas/core/generic.py:10871: cls.kurt = kurt # type: ignore[assignment]
pandas/core/generic.py:10890: cls.median = median # type: ignore[assignment]
pandas/core/generic.py:10908: cls.max = max # type: ignore[assignment]
pandas/core/generic.py:10926: cls.min = min # type: ignore[assignment]
pandas/core/generic.py:11049: return self._inplace_method(other, type(self).__add__) # type: ignore[operator]
pandas/core/generic.py:11052: return self._inplace_method(other, type(self).__sub__) # type: ignore[operator]
pandas/core/generic.py:11055: return self._inplace_method(other, type(self).__mul__) # type: ignore[operator]
pandas/core/generic.py:11059: other, type(self).__truediv__ # type: ignore[operator]
pandas/core/generic.py:11064: other, type(self).__floordiv__ # type: ignore[operator]
pandas/core/generic.py:11068: return self._inplace_method(other, type(self).__mod__) # type: ignore[operator]
pandas/core/generic.py:11071: return self._inplace_method(other, type(self).__pow__) # type: ignore[operator]
pandas/core/generic.py:11074: return self._inplace_method(other, type(self).__and__) # type: ignore[operator]
pandas/core/generic.py:11077: return self._inplace_method(other, type(self).__or__) # type: ignore[operator]
pandas/core/generic.py:11080: return self._inplace_method(other, type(self).__xor__) # type: ignore[operator]
pandas/core/dtypes/generic.py:43: @classmethod # type: ignore[misc]
pandas/core/dtypes/common.py:160: return arr.astype("int64", copy=copy, casting="safe") # type: ignore[call-arg]
pandas/core/dtypes/common.py:165: return arr.astype("uint64", copy=copy, casting="safe") # type: ignore[call-arg]
pandas/core/dtypes/cast.py:588: dtype, fill_value, type(fill_value) # type: ignore[arg-type]
pandas/core/dtypes/dtypes.py:680: unit, tz = unit.unit, unit.tz # type: ignore[attr-defined]
pandas/core/arrays/datetimelike.py:539: other = self._scalar_type(other) # type: ignore[call-arg]
pandas/core/arrays/datetimelike.py:590: fill_value = self._scalar_type(fill_value) # type: ignore[call-arg]
pandas/core/arrays/datetimelike.py:598: new_fill = self._scalar_type(fill_value) # type: ignore[call-arg]
pandas/core/arrays/datetimelike.py:660: value = self._scalar_type(value) # type: ignore[call-arg]
pandas/core/arrays/datetimelike.py:953: return self._resolution_obj.attrname # type: ignore[union-attr]
pandas/core/arrays/datetimelike.py:1777: uniques = uniques[::-1] # type: ignore[assignment]
pandas/core/arrays/string_.py:213: self._dtype = StringDtype() # type: ignore[assignment]
pandas/core/arrays/period.py:326: @property # type: ignore[misc]
pandas/core/arrays/boolean.py:81: def type(self) -> Type: # type: ignore[override]
pandas/core/arrays/interval.py:1350: @property # type: ignore[misc]
pandas/core/arrays/interval.py:1572: left = self._combined.view("complex128") # type:ignore[attr-defined]
pandas/core/arrays/floating.py:81: [t.numpy_dtype for t in dtypes], [] # type: ignore[union-attr]
pandas/core/internals/array_manager.py:151: def axes(self) -> List[Index]: # type: ignore[override]
pandas/core/internals/array_manager.py:457: if hasattr(arr, "tz") and arr.tz is None: # type: ignore[union-attr]
pandas/core/internals/array_manager.py:459: arr = arr._data # type: ignore[union-attr]
pandas/core/internals/array_manager.py:462: arr = arr._data # type: ignore[union-attr]
pandas/core/series.py:669: @Appender(base.IndexOpsMixin.array.__doc__) # type: ignore[misc]
pandas/core/series.py:1097: self._mgr = self._mgr.setitem( # type: ignore[assignment]
pandas/core/apply.py:563: new_func[k] = [v] # type:ignore[list-item]
pandas/core/nanops.py:1751: result = type(values)._simple_new( # type: ignore[attr-defined]
pandas/core/groupby/categorical.py:108: return ci.set_categories(c.categories) # type: ignore[attr-defined]
pandas/core/groupby/categorical.py:113: return ci.add_categories(new_cats) # type: ignore[attr-defined]
pandas/core/groupby/base.py:60: subset = self.obj # type: ignore[attr-defined]
pandas/core/groupby/base.py:69: groupby = self._groupby[key] # type: ignore[attr-defined]
pandas/core/groupby/base.py:72: groupby = self._groupby # type: ignore[attr-defined]
pandas/core/groupby/base.py:78: subset, groupby=groupby, parent=self, **kwargs # type: ignore[call-arg]
pandas/core/groupby/grouper.py:306: self.grouper, _, self.obj = get_grouper( # type: ignore[type-var]
pandas/core/groupby/grouper.py:393: return self.grouper.groups # type: ignore[union-attr]
pandas/core/indexes/datetimelike.py:85: @staticmethod # type: ignore[misc]
pandas/core/indexes/datetimelike.py:131: DatetimeLikeArrayMixin._hasnans.fget # type: ignore[attr-defined]
pandas/core/indexes/period.py:193: @property # type:ignore[misc]
pandas/core/indexes/period.py:200: @property # type:ignore[misc]
pandas/core/indexes/period.py:207: @property # type:ignore[misc]
pandas/core/indexes/category.py:347: ("ordered", self.ordered), # type: ignore[attr-defined]
pandas/core/indexes/base.py:6174: name = index_like.name # type: ignore[union-attr, attr-defined]
pandas/core/indexes/frozen.py:107: __setitem__ = __setslice__ = _disabled # type: ignore[assignment]
pandas/core/indexes/frozen.py:108: __delitem__ = __delslice__ = _disabled # type: ignore[assignment]
pandas/core/indexes/frozen.py:109: pop = append = extend = _disabled # type: ignore[assignment]
pandas/core/indexes/frozen.py:110: remove = sort = insert = _disabled # type: ignore[assignment]
pandas/core/indexes/multi.py:1479: self._names[lev] = name # type: ignore[has-type]
pandas/core/common.py:497: return values # type: ignore[return-value]
pandas/core/reshape/merge.py:1028: join_names = [] # type: ignore[var-annotated]
pandas/core/ops/array_ops.py:372: res_values = filler(res_values) # type: ignore[operator]
pandas/core/base.py:118: mem = self.memory_usage(deep=True) # type: ignore[attr-defined]
pandas/core/base.py:229: self.obj, ABCSeries # type: ignore[attr-defined]
pandas/core/base.py:232: return self.obj # type: ignore[attr-defined]
pandas/core/base.py:235: return self.obj[self._selection] # type: ignore[attr-defined]
pandas/core/base.py:245: self.obj, ABCDataFrame # type: ignore[attr-defined]
pandas/core/base.py:248: return self.obj.reindex( # type: ignore[attr-defined]
pandas/core/base.py:253: if len(self.exclusions) > 0: # type: ignore[attr-defined]
pandas/core/base.py:256: return self.obj.drop(self.exclusions, axis=1) # type: ignore[attr-defined]
pandas/core/base.py:259: return self.obj # type: ignore[attr-defined]
pandas/core/base.py:268: self.obj.columns.intersection(key) # type: ignore[attr-defined]
pandas/core/base.py:272: set(key).difference(self.obj.columns) # type: ignore[attr-defined]
pandas/core/base.py:279: if key not in self.obj.columns: # type: ignore[attr-defined]
pandas/core/base.py:285: if key not in self.obj: # type: ignore[attr-defined]
pandas/core/base.py:611: return self.array.to_numpy( # type: ignore[call-arg]
pandas/core/base.py:924: self = cast("Categorical", self) # type: ignore[assignment]
pandas/core/base.py:927: return self._values.map(mapper) # type: ignore[union-attr]
pandas/core/base.py:945: values = self.astype(object)._values # type: ignore[attr-defined]
pandas/core/base.py:1183: return self.array.memory_usage(deep=deep) # type: ignore[attr-defined]
pandas/core/base.py:1318: return self[~duplicated] # type: ignore[index]
pandas/core/frame.py:1219: itertuple = collections.namedtuple( # type: ignore[misc]
pandas/core/frame.py:1236: def dot(self, other: Series) -> Series: # type: ignore[misc]
pandas/core/frame.py:2339: from pandas.io.stata import ( # type: ignore[no-redef]
pandas/core/frame.py:2344: from pandas.io.stata import ( # type: ignore[no-redef]
pandas/core/frame.py:2357: writer = statawriter( # type: ignore[call-arg]
pandas/core/frame.py:4082: unique_dtype.type, tuple(dtypes_set) # type: ignore[arg-type]
pandas/core/frame.py:5100: arrays.append(col) # type:ignore[arg-type]
pandas/core/frame.py:5144: def reset_index( # type: ignore[misc]
pandas/core/frame.py:5783: def sort_values( # type: ignore[override]
pandas/core/frame.py:6385: right._mgr, array_op # type: ignore[arg-type]
pandas/core/resample.py:142: self.grouper = None # type: ignore[assignment]
pandas/core/resample.py:465: self.loffset, # type: ignore[has-type]
pandas/core/resample.py:474: result.index = result.index + self.loffset # type: ignore[has-type]
pandas/core/resample.py:911: return self._downsample("std", ddof=ddof) # type: ignore[call-arg]
pandas/core/resample.py:929: return self._downsample("var", ddof=ddof) # type: ignore[call-arg]
pandas/core/resample.py:989: return self._downsample("quantile", q=q, **kwargs) # type: ignore[call-arg]
pandas/core/resample.py:1042: super().__init__(None) # type: ignore[call-arg]
pandas/core/resample.py:1109: and len(self.grouper.binlabels) > len(ax) # type: ignore[attr-defined]
pandas/core/config_init.py:332: ip = get_ipython() # type: ignore[name-defined]
pandas/core/computation/pytables.py:88: @property # type: ignore[misc]
pandas/core/computation/ops.py:89: supr_new = super(Term, klass).__new__ # type: ignore[misc]
pandas/core/computation/ops.py:610: operands = [op(env) for op in self.operands] # type: ignore[operator]
pandas/core/computation/expr.py:677: f"Invalid function call {node.func.id}" # type: ignore[attr-defined]
pandas/core/computation/expr.py:701: "keyword error in function call " # type: ignore[attr-defined]
pandas/core/computation/expressions.py:241: return _evaluate(op, op_str, a, b) # type: ignore[misc]
pandas/core/computation/scope.py:136: self.scope = self.scope.new_child( # type: ignore[assignment]
pandas/core/computation/scope.py:142: self.scope = self.scope.new_child( # type: ignore[assignment]
pandas/core/computation/scope.py:151: resolvers += tuple(local_dict.resolvers.maps) # type: ignore[has-type]
pandas/core/computation/scope.py:239: mapping[new_key] = new_value # type: ignore[index]
pandas/core/computation/scope.py:260: self.scope = self.scope.new_child(d) # type: ignore[assignment]
pandas/core/computation/scope.py:333: + self.resolvers.maps # type: ignore[operator]
pandas/core/computation/scope.py:334: + self.scope.maps # type: ignore[operator]
pandas/compat/pickle_compat.py:74: def __new__(cls) -> Series: # type: ignore[misc]
pandas/compat/pickle_compat.py:92: def __new__(cls) -> DataFrame: # type: ignore[misc]
pandas/compat/pickle_compat.py:191:class Unpickler(pkl._Unpickler): # type: ignore[name-defined]
pandas/plotting/_matplotlib/core.py:608: self.legend_handles = reversed( # type: ignore[assignment]
pandas/plotting/_matplotlib/core.py:614: self.legend_labels = reversed( # type: ignore[assignment]
pandas/plotting/_matplotlib/core.py:708: args = (x, y) # type: ignore[assignment]
pandas/plotting/_matplotlib/core.py:1161: plotf = self._plot # type: ignore[assignment]
pandas/plotting/_matplotlib/core.py:1611: blabels = None # type: ignore[assignment]
pandas/plotting/_matplotlib/timeseries.py:68: series.index = series.index.asfreq( # type: ignore[attr-defined]
pandas/plotting/_matplotlib/timeseries.py:248: weekdays = np.unique(index.dayofweek) # type: ignore[attr-defined]
pandas/plotting/_misc.py:534: self.__init__() # type: ignore[misc]
pandas/io/formats/excel.py:632: gen2 = ( # type: ignore[assignment]
pandas/io/formats/excel.py:838: writer = ExcelWriter( # type: ignore[abstract]
pandas/io/formats/printing.py:507: return head, tail # type: ignore[return-value]
pandas/io/formats/printing.py:532: attrs.append(("dtype", f"'{obj.dtype}'")) # type: ignore[attr-defined]
pandas/io/formats/printing.py:535: attrs.append(("name", default_pprint(obj.name))) # type: ignore[attr-defined]
pandas/io/formats/printing.py:538: obj.names # type: ignore[attr-defined]
pandas/io/formats/printing.py:541: attrs.append(("names", default_pprint(obj.names))) # type: ignore[attr-defined]
pandas/io/formats/console.py:73: return __IPYTHON__ or check_main() # type: ignore[name-defined]
pandas/io/formats/console.py:88: ip = get_ipython() # type: ignore[name-defined]
pandas/io/formats/style.py:1847: class MyStyler(cls): # type:ignore[valid-type,misc]
pandas/io/formats/csvs.py:252: handles.handle, # type: ignore[arg-type]
pandas/io/formats/xml.py:278: handles.handle.write(xml_doc) # type: ignore[arg-type]
pandas/io/formats/format.py:1384: float_format(value=v) # type: ignore[operator,call-arg]
pandas/io/pytables.py:2362: itemsize = dtype.itemsize # type: ignore[attr-defined]
pandas/io/pytables.py:3440: return dict(d1 + d2 + d3) # type: ignore[operator]
pandas/io/pytables.py:4025: blocks = list(mgr.blocks) # type: ignore[union-attr]
pandas/io/pytables.py:4029: blocks.extend(mgr.blocks) # type: ignore[union-attr]
pandas/io/pickle.py:107: pickle.dumps(obj, protocol=protocol) # type: ignore[arg-type]
pandas/io/pickle.py:112: obj, handles.handle, protocol=protocol # type: ignore[arg-type]
pandas/io/pickle.py:207: return pickle.load(handles.handle) # type: ignore[arg-type]
pandas/io/excel/_base.py:927: for extension in cls.supported_extensions # type: ignore[attr-defined]
pandas/io/excel/_base.py:1003: zf = zipfile.ZipFile(stream) # type: ignore[arg-type]
pandas/io/stata.py:914: self.TYPE_MAP = list(range(251)) + list("bhlfd") # type: ignore[arg-type]
pandas/io/stata.py:1094: self.path_or_buf = BytesIO(handles.handle.read()) # type: ignore[arg-type]
pandas/io/stata.py:2211: to_write.encode(self._encoding) # type: ignore[arg-type]
pandas/io/stata.py:2218: self.handles.handle.write(value) # type: ignore[arg-type]
pandas/io/stata.py:2542: self.handles.handle.write(bio.getvalue()) # type: ignore[arg-type]
pandas/io/json/_json.py:1045: ) # type:ignore[misc]
pandas/io/common.py:518: ) # type: ignore[operator]
pandas/io/common.py:628: fileobj=handle, # type: ignore[arg-type]
pandas/io/common.py:636: handle, # type: ignore[arg-type]
pandas/io/common.py:693: handle, # type: ignore[arg-type]
pandas/io/common.py:731:class _BytesZipFile(zipfile.ZipFile, BytesIO): # type: ignore[misc]
pandas/io/common.py:755: super().__init__(file, mode, **kwargs_zip) # type: ignore[arg-type]
pandas/io/common.py:852: wrapped = cast(mmap.mmap, _MMapWrapper(handle)) # type: ignore[arg-type]
pandas/io/parsers/c_parser_wrapper.py:48: self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr]
pandas/io/parsers/readers.py:976: return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
pandas/io/parsers/python_parser.py:243: self.data = reader # type: ignore[assignment]
pandas/io/parsers/python_parser.py:301: size = self.chunksize # type: ignore[attr-defined]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/37716 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37716/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37716/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37716/events | https://github.com/pandas-dev/pandas/issues/37716 | 739,049,252 | MDU6SXNzdWU3MzkwNDkyNTI= | 37,716 | df.melt() overwriting column named 'variable' | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 6 | 2020-11-09T13:55:23Z | 2020-11-12T10:26:53Z | null | NONE | null | when melting df using a column named 'variable' as an id_var.
resultant frame has original 'variable' column overwritten with the newly melted 'variable' column.
would suggest checking for presence of 'variable' column and appending '_i' to newly created columns.
[example_melt_overwrite.zip](https://github.com/pandas-dev/pandas/files/5510886/example_melt_overwrite.zip)
foo = pd.read_pickle('example_melt_overwrite.pkl')
foo.melt(id_vars=['DateTime','variable'])
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https://api.github.com/repos/pandas-dev/pandas/issues/37717 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37717/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37717/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37717/events | https://github.com/pandas-dev/pandas/pull/37717 | 739,058,481 | MDExOlB1bGxSZXF1ZXN0NTE3Nzg5MTMz | 37,717 | TST: move and reformat latex_na_rep test | {
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} | 1 | 2020-11-09T14:07:30Z | 2020-11-10T04:44:18Z | 2020-11-09T18:56:08Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Move test to a more suitable location (``class TestToLatexFormatters``)
and reformat using ``_dedent`` and raw string for better readability. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/37718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/37718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/37718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/37718/events | https://github.com/pandas-dev/pandas/pull/37718 | 739,069,714 | MDExOlB1bGxSZXF1ZXN0NTE3Nzk4MjQ5 | 37,718 | TST: improve readability using dedent in test_to_string.py | {
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} | 2 | 2020-11-09T14:21:22Z | 2020-11-10T04:43:51Z | 2020-11-09T18:57:10Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Improve readability of expected outputs using dedent in ``pandas/tests/io/formats/test_to_string.py`` | {
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