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MDExOlB1bGxSZXF1ZXN0NTEwMTAxNjQx
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fix windows issue pre-commit
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| 2020-10-26T15:13:43Z
| 2020-10-26T17:17:23Z
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|
MEMBER
| null |
By not explicitly using `python` in the entry, we would get
```
Executable `python3` not found
```
on Windows
xref https://github.com/pandas-dev/pandas/pull/37023#issuecomment-716607730
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MDExOlB1bGxSZXF1ZXN0NTEwMTEwMTc4
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TST/REF: collect tests by method from tests.internals
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TST/REF: collect tests by method from generic
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REF: avoid special-casing inside DTA/TDA.mean, flesh out tests
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REF: avoid special case in DTA/TDA.median, flesh out tests
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Also fixes currently-incorrect empty case.
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TST/REF: misplaced tests in frame.test_dtypes
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MEMBER
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After moving them, test_dtypes is specific to DataFrame.dtypes, so move that file to tests/frame/methods/test_dtypes.py
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BUG: DataFrame.min/max dt64 with skipna=False
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| 2020-10-26T17:52:06Z
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MEMBER
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PERF: reverted change from commit 7d257c69 to solve issue #37081
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CONTRIBUTOR
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reverted change from commit 7d257c697 to solve issue #37081
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BUG: NBC change in reindexing logic of Series.__setitem__ in pandas=1.1.0
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| 9
| 2020-10-26T19:33:28Z
| 2020-12-11T12:51:07Z
| 2020-10-30T08:14:54Z
|
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.
Hi,
The following example of using `Series.__setitem__` shows different results on **pandas=1.0.5** and **pandas=1.1.3**:
```
import pandas as pd
import numpy as np
S1 = pd.Series([0, 1, -12, 3, -10, 5, 6, 7, 8, -11, -13], dtype=np.int32)
idx = pd.Series([4, 9, 2, 10], dtype=np.int64)
value = pd.Series([-10, -11, -12, -13], dtype=np.int32)
S1[idx] = value
S1
```
It looks like [#33643](https://github.com/pandas-dev/pandas/pull/33643) changed the semantics of setitem operation when assigned values are `pandas.Series`, i.e. the old-behavior was to first align value with idx and then assign value items to S1 basing on idx.values as index labels, so result was:
```
>>> S1
0 0
1 1
2 -12
3 3
4 -10
5 5
6 6
7 7
8 8
9 -11
10 -13
dtype: int32
```
And now the result is as if `S1[idx]` took precedence before the actual assignment, so that labels in `idx.values` which are absent in `value.index` are assigned NaNs:
```
>>> S1
0 0.0
1 1.0
2 -12.0
3 3.0
4 NaN
5 5.0
6 6.0
7 7.0
8 8.0
9 NaN
10 NaN
dtype: float64
```
Can you please confirm this was intentional change (as I don't see any tests updated to reflect that, nor there are mentions in "What’s new in 1.1.0" section of the documentation)? From the user perspective it looks like with new implementation, user will have to ensure that `value.index == idx.values` to achieve the same result, which is not a big deal of course, but still it's some additional code.
First version where the issue appears:
```
INSTALLED VERSIONS
------------------
commit : 4071c3b872e4ef98a2a2d7c752dd5d2030c4df6e
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0.dev0+1307.g4071c3b87
```
Kind Regards,
Alexey.
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| 1
| 2020-10-26T19:57:06Z
| 2020-10-27T12:57:40Z
| 2020-10-27T12:49:45Z
|
MEMBER
| null |
- [ ] closes #33999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
tested at https://github.com/simonjayhawkins/pandas-release/actions/runs/329803554 ( patched from Gist https://gist.github.com/simonjayhawkins/f26d3e26fd55159299b38204f7f3295c and skips for test_missing_required_dependencies removed for conda and pip tests)
following merge can also remove from https://github.com/MacPython/pandas-wheels/blob/5543626b261f5cb55cebfb300c156718c1796ce3/config.sh#L34
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| 2021-08-13T14:43:50Z
| 2021-08-13T05:27:03Z
|
NONE
| null |
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(3,4), columns= list('ABCD'))
df_int = pd.to_numeric(df['A'], errors='coerce').astype('Int64')
```
#### Problem description
cannot safely cast non-equivalent float64 to Int64, it should happen like when you convert from float64 to int64, which is rounding down the number
#### Expected Output
0 0
1 0
2 0
Name: A, dtype: Int64
#### Output of ``pd.show_versions()``
pandas : 1.0.1
You can pass to Int64 safely by doing:
```python
df_int = np.floor(pd.to_numeric(df['A'], errors='coerce')).astype('Int64')
```
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\arrays\integer.py in safe_cast(values, dtype, copy)
143 try:
--> 144 return values.astype(dtype, casting="safe", copy=copy)
145 except TypeError:
TypeError: Cannot cast array from dtype('float64') to dtype('int64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-14-8151dc1b5846> in <module>
----> 1 df_int = pd.to_numeric(df['A'], errors='coerce').astype('Int64')
2 df_int
~\anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
5696 else:
5697 # else, only a single dtype is given
-> 5698 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
5699 return self._constructor(new_data).__finalize__(self)
5700
~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
580
581 def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
583
584 def convert(self, **kwargs):
~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~\anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
623 vals1d = values.ravel()
624 try:
--> 625 values = astype_nansafe(vals1d, dtype, copy=True)
626 except (ValueError, TypeError):
627 # e.g. astype_nansafe can fail on object-dtype of strings
~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
819 # dispatch on extension dtype if needed
820 if is_extension_array_dtype(dtype):
--> 821 return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
822
823 if not isinstance(dtype, np.dtype):
~\anaconda3\lib\site-packages\pandas\core\arrays\integer.py in _from_sequence(cls, scalars, dtype, copy)
348 @classmethod
349 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 350 return integer_array(scalars, dtype=dtype, copy=copy)
351
352 @classmethod
~\anaconda3\lib\site-packages\pandas\core\arrays\integer.py in integer_array(values, dtype, copy)
129 TypeError if incompatible types
130 """
--> 131 values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
132 return IntegerArray(values, mask)
133
~\anaconda3\lib\site-packages\pandas\core\arrays\integer.py in coerce_to_array(values, dtype, mask, copy)
245 values = safe_cast(values, dtype, copy=False)
246 else:
--> 247 values = safe_cast(values, dtype, copy=False)
248
249 return values, mask
~\anaconda3\lib\site-packages\pandas\core\arrays\integer.py in safe_cast(values, dtype, copy)
150
151 raise TypeError(
--> 152 f"cannot safely cast non-equivalent {values.dtype} to {np.dtype(dtype)}"
153 )
154
TypeError: cannot safely cast non-equivalent float64 to int64
</details>
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Run tests in ``indexes`` and ``indexing`` locally without failure.
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ENH: std for dt64 dtype
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| 2021-06-13T10:09:02Z
| 2020-10-27T07:37:49Z
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CONTRIBUTOR
| null |
The available accessors are already supplied by the `__dir__` method's `super().__dir__()` call, so simplifications are possible.
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|
xlrd is required for handle excel in pandas but it is EOL and yield warnings on py37
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NONE
| null |
Hello,
To use `pandas.read_excel` and `pandas.DataFrame.to_excel` we need xlrd.
xlrd is EOL, and can be replaced with OpenPyXL Do you plan this migration ?
Moreover, now xlrd yield warnings with python 3.7
```
xlrd/xlsx.py:266: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
for elem in self.tree.iter() if Element_has_iter else self.tree.getiterator():
```
```
xlrd/xlsx.py:312: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
for elem in self.tree.iter() if Element_has_iter else self.tree.getiterator()
```
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REGR: fix rank algo for read-only data
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| 5
| 2020-10-27T08:05:41Z
| 2020-10-28T07:23:30Z
| 2020-10-27T22:09:06Z
|
MEMBER
| null |
Closes #37290
|
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MDExOlB1bGxSZXF1ZXN0NTEwNTc5NDkx
| 37,440
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Upgrade pygrep, use in-built rst-directive-colons
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| 1
| 2020-10-27T08:47:40Z
| 2020-10-27T13:28:49Z
| 2020-10-27T12:52:15Z
|
MEMBER
| null |
I got a PR in to pre-commit/pygrep-hooks to make the "incorrect-sphinx-directives" hook a built-in one (as it's not specific to pandas) :tada: So, let's use it?
They also have another useful hook (`rst-inline-touching-normal`), which caught a couple of errors which I've fixed below.
The reason why I've kept `rst-backticks` to just `rst` files (as opposed to `types: [text]` like the other two) is that in docstrings, the numpy guide instructs to use single backticks for variable (though if it was ever desirable to change that, then this hook would make it pretty easy to check for consistency)
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BUG: Excessive memory usage loading Dataframe with mixed data types from HDF5 file saved in "table" format
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| 2020-10-27T09:47:48Z
| 2021-08-18T23:02:14Z
| null |
NONE
| null |
#### Code Sample
Environment setup:
```
conda create -n bug_test python=3.8 pandas pytables numpy psutil
conda activate bug_test
```
Test code:
```python
import psutil
import numpy as np
import pandas as pd
import os
import gc
random_np = np.random.randint(0, 1e16, size=(25000000,4))
random_df = pd.DataFrame(random_np)
random_df['Test'] = np.random.rand(25000000,1)
random_df.set_index(0, inplace=True)
random_df.sort_index(inplace=True)
random_df.to_hdf('test.h5', key='random_df', mode='w', format='table')
del random_np
del random_df
gc.collect()
initial_memory_usage = psutil.Process(os.getpid()).memory_info().rss
random_df = pd.read_hdf('test.h5')
print(f'Memory Usage According to Pandas: {random_df.__sizeof__()/1000000000:.2f}GB')
print(f'Real Memory Usage: {(psutil.Process(os.getpid()).memory_info().rss - initial_memory_usage)/1000000000:.2f}GB')
random_df.index = random_df.index.copy(deep=True)
print(f'Memory Usage After Temp Fix: {(psutil.Process(os.getpid()).memory_info().rss - initial_memory_usage)/1000000000:.2f}GB')
del random_df
gc.collect()
print(f'Memory Usage After Deleting Table: {(psutil.Process(os.getpid()).memory_info().rss - initial_memory_usage)/1000000000:.2f}GB')
```
#### Problem description
The above code generates a 1GB df table with mixed data types and saves it to a HDF5 file in "table" format.
Loading the HDF5 file back, we expect it to use 1GB of memory instead it uses 1.8GB of memory. I have found that the issue is with the index of the df. If I do a deep copy and replace it with the copy, the excessive memory usage goes away and memory usage is 1GB as expected.
I have initially encountered this issue when using Pandas 1.0.5 but I have tested this on Pandas 1.1.3 and the issue still exists.
When I was investigating the bug by going through the code of Pandas 1.0.5, I noticed that PyTables was used to read the HDF5 file and returns a NumPy structured array. Dataframes were created using this NumPy array and pd.concat was used to combine them into a single df. The pd.concat makes a copy of the original data instead of just pointing to the NumPy array. However, the index of the combined table still points to the original NumPy array. I think this explains the excessive memory usage because GC cannot collect the NumPy array since there still a reference to it.
Due to significant code changes to the read_hdf in Pandas 1.13, I did not have time to find out if this is still the same problem or another problem.
#### Expected Output
1GB df table should use 1GB of memory instead of 1.8GB
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.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 : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MzAzMzA3ODg=
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|
BUG: read csv with enclosed double quotes
|
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[] | null | 1
| 2020-10-27T10:42:48Z
| 2020-11-04T09:50:28Z
| 2020-11-04T09:50:28Z
|
NONE
| null |
- [ ✔] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
def read_as_df(self, file_path):
return pd.read_csv(
file_path,
sep='|',
quoting=csv.QUOTE_ALL,
encoding='utf-16'
)
```
#### Problem description
I have a csv with some data with separator "|" and all columns are enclosed with double quotes:
```
"Code"|"Owner"|"OwnerName"|"Age"|"Description"|"AdditionalRemarks"
"122"|"100"|"Random owner 1"|"52"|"’Some random description""|"Some addtitional remarks"
"123"|"101"|"Owner 2"|"32"|"Data1 | Data2 | Data3"|"Addtitional remarks ++"
```
When I read the csv using the code above: I got this result:
#### Shown Output:
#### Expected Output
For data row 1: the addtional remarks column is read as part of the description column
For data row 2: the data looks fine
#### 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 : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2019.2
dateutil : 2.8.0
pip : 20.2.3
setuptools : 40.8.0
Cython : None
pytest : 5.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : 0.10.1
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 6.3.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.3.2
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.3.4
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.45.1
</details>
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|
ENH: to_latex(): add posibility to define the latex environment (instead of table)
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| 2021-07-29T04:37:39Z
| 2021-07-28T01:15:43Z
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NONE
| null |
#### Is your feature request related to a problem?
I wish pandas's to_latex() function could create table* environment instead of table for wide tables. The generated table sometimes does not fit into one column of a two column document.
#### Describe the solution you'd like
A parameter where one can define the name of the environment. E.g. `df.to_latex(env="foobar*")` to generate: `\begin{foobar*} table stuff here \end{foobar*}`.
#### API breaking implications
One more parameter.
#### Describe alternatives you've considered
Write a code, which replaces begin{table} and end{table} to the desired environment. (table* in most of the cases)
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Backport PR #37433 on branch 1.1.x (REGR: fix groupby std() with nullable dtypes)
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NONE
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Backport PR #37433: REGR: fix groupby std() with nullable dtypes
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Backport PR #37428 on branch 1.1.x (Failing test_missing_required_dependency in pandas-wheels)
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NONE
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Backport PR #37428: Failing test_missing_required_dependency in pandas-wheels
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Backport PR #37221 on branch 1.1.x (Fix regression for is_monotonic_increasing with nan in MultiIndex)
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NONE
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Backport PR #37221: Fix regression for is_monotonic_increasing with nan in MultiIndex
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| 2020-10-27T13:08:40Z
| 2020-10-28T15:43:56Z
| 2020-10-28T15:38:22Z
|
NONE
| null |
If i use:
df.to_csv('dir1/dir2/test.csv.zip')
or
df.to_csv('dir1/dir2/test.csv', compression='zip')
to save a csv file, i found that
1. the path name no matter reletive or absolute will be included in the zip archive
2. the extension name of the actual file in the zip archive will be kept the same with the one you passed in which means if you pass in 'zip' you get a 'zip' in the archive.
None of the above are true if extension name is one of [‘.gz’, ‘.bz2’, or ‘.xz’] or compression mode is one of [‘gzip’, ‘bz2’], are those desirable behavior rather than keep them all the same?
Additionly if you set compression mode with one of ['gzip', 'bz2'] and passin the file name with extension 'csv' you get the compressed archive end with '.csv' which is confusing because you will get all hex code if you open it with a text editor.
pd.show_version() output
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
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 : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.3.1.post20200810
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : 0.10.0
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
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| 37,448
|
BUG: Inconsistent correlation between constant series (varies with number of rows)
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| 3
| 2020-10-27T13:23:34Z
| 2020-10-30T19:57:22Z
| 2020-10-30T19:57:22Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported (might be another variant of #20954).
- [X] I have confirmed this bug exists on the latest version of pandas (`1.1.3`).
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
for length in [2, 3, 5, 10, 20]:
print(pd.DataFrame(length*[[0.42, 0.1]], columns=["A", "B"]).corr())
```
gives
```
A B
A NaN NaN
B NaN NaN
A B
A NaN NaN
B NaN 1.0
A B
A 1.0 NaN
B NaN NaN
A B
A 1.0 -1.0
B -1.0 1.0
A B
A 1.0 1.0
B 1.0 1.0
```
#### Problem description
Inconsistent output with slightly varying number of rows. Would expect correlation between series where at least one of them is constant, to be `NaN`.
This makes e.g. code dependent on `dropna()` usage after calculating `corr()` difficult/error prone, as behaviour is inconsistent.
#### Expected Output
Either consistent `NaN` output when calculating correlation with constant data, or a warning in [`pandas.DataFrame.corr` documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html) stating that returned correlation between constant series can be anything from `[1.0, -1.0, NaN]`.
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TST/REF: collect tests by method
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PERF: faster dir() calls
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| 1
| 2020-10-27T18:27:26Z
| 2020-10-29T14:01:01Z
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|
CONTRIBUTOR
| null |
I experience slow tab-completion in iPython, so I've optimized `dir` calls in pandas, e.g.
```python
>>> n = 100_000
>>> ser = pd.Series(['a'] * n]
>>> %timeit dir(ser)
3.73 ms ± 34.7 µs per loop # master
253 µs ± 4.3 µs per loop # this PR
```
It does this by caching the output for the info axis, when the info axis may have string, and returning an empty set for info axes that cannot have strings (numeric indexes etc.)
The above didn't actually improve the subjective speed of tab completion for me, so the problem there probably is in Ipython, but the change in this PR can't hurt either.
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CLN: Breakup agg
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Extracted dictionary parts to `agg_dict_like`, and changed from `isinstance(arg, dict)` to `is_dict_like(arg)`. Also renamed `aggregate_multiple_funcs` to `agg_list_like`.
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ENH: Improve numerical stability for Pearson corr() and cov()
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MEMBER
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Floating number issues when summing the same number often enough...
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ValueError raised when plottting reversed fixed-frequency TimedeltaIndex
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| 0
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|
CONTRIBUTOR
| null |
When plotting with a `TimedeltaIndex` with a fixed frequency and `xmin > xmax`, a ValueError gets raised.
```python
>>> pd.Series([1,2], index=pd.timedelta_range(start=0, periods=2, freq='D')).plot(xlim=(3, 1))
```
> C:\ProgramData\Miniconda3\envs\env_name\lib\site-packages\pandas\plotting\_matplotlib\converter.py in __call__(self, x, pos)
> 1066 def __call__(self, x, pos=0) -> str:
> 1067 (vmin, vmax) = tuple(self.axis.get_view_interval())
>-> 1068 n_decimals = int(np.ceil(np.log10(100 * 1e9 / (vmax - vmin))))
> 1069 if n_decimals > 9:
> 1070 n_decimals = 9
>
> ValueError: cannot convert float NaN to integer
Seems that there should just be an `abs` in [this line](https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_matplotlib/converter.py#L1075).
I would expect this to be able to output a plot successfully.
Works fine with a `TimedeltaIndex` with `freq=None`:
```python
pd.Series([1,2], index=[pd.Timedelta(days=1), pd.Timedelta(days=2)]).plot(xlim=(3, 1))
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
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 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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CI: Troubleshoot PY38 windows build
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| 2020-10-31T15:20:28Z
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MEMBER
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Some of the error messages look like a OOM, so this is just trying cutting down the number of tests collected/run by ~40%
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BUG: BusinessDay.apply_index with offset
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CLN: de-duplicate validator
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NONE
| null |
Backport PR #37439: REGR: fix rank algo for read-only data
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ENH: support casting Int arrays with nulls to np.float?
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| 2020-10-28T05:42:47Z
| 2021-08-13T05:27:37Z
| null |
MEMBER
| null |
xref https://github.com/numpy/numpy/issues/17659
> I would expect numpy to automatically convert such arrays into a float type and fill with np.nan. However, for some reason it converts it to object:
>
> `pd.DataFrame({'col': [1, np.nan, 3]}).astype('UInt8').values.dtype`
>
> which leads to errors like this one
>
> `np.nanmax(pd.DataFrame({'col': [1, np.nan, 3]}).astype('UInt8').values)`
>
> returns "TypeError: boolean value of NA is ambiguous"
>
> Is that expected ?
Personally I don't have a strong opinion re: whether it's a good idea to support this
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BUG: Metadata propagation for groupby iterator
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| 12
| 2020-10-28T07:30:05Z
| 2020-11-04T14:19:10Z
| 2020-11-04T13:22:55Z
|
CONTRIBUTOR
| null |
Addresses part of #28283
This PR ensures that `__finalize__` is called for objects returned by iterator over `.groupby`-results.
Based on the discussion and benchmarks in PR #35688 (see https://github.com/pandas-dev/pandas/pull/35688#issuecomment-675367517), `__finalize__` is _not_ called for intermediate objects used by `.apply` (and maybe other code paths).
The implemented solution is the safe choice with regard to performance: An alternative solution would be to call `__finalize__` immediately after initialization (in `DataSplitter._chop()`).
The downside to doing so would be the performance hit due to the overhead of finalizing intermediate objects in `.apply` (and maybe other code paths). In the benchmarks referenced above, this amounted to around 4%.
The upside of finalizing immediately after initialization would be reduced complexity and that it would allow `.apply` (and any other code paths using `DataSplitter._chop` to access metadata)..
- [x] closes #37343
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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PERF: performance regression in timeseries/timestamp comparison (ASV)
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| 1
| 2020-10-28T07:46:09Z
| 2020-12-03T14:19:35Z
| 2020-12-03T14:19:35Z
|
MEMBER
| null |
See https://pandas.pydata.org/speed/pandas/#arithmetic.Timeseries.time_series_timestamp_compare?python=3.8&Cython=0.29.21&p-tz=None&commits=ac7ca239-038aab96
This seems to be caused by https://github.com/pandas-dev/pandas/pull/37313 cc @jbrockmendel
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Backport PR #37432 on branch 1.1.x
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| 1
| 2020-10-28T08:27:23Z
| 2020-10-28T09:32:25Z
| 2020-10-28T09:27:50Z
|
MEMBER
| null |
(cherry picked from commit 52925335fe6d5f1ea6c79bb03da70654d9be9668)
Tried to merge it. Hopefully nothing was broken in the process. Run the indexing tests locally, but have to wait for ci
cc @simonjayhawkins
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MDU6SXNzdWU3MzEyOTc5OTE=
| 37,464
|
BUG: `pd.concat(..., axis="columns")` inconsistently keeps/drops index name
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| 2020-10-28T10:06:04Z
| 2020-10-28T16:50:46Z
| 2020-10-28T16:48:46Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df1 = pd.DataFrame({"IndexColumn": [1, 2, 3], "DataColumn1": ["x", "y", "z"]})
df2 = pd.DataFrame({"IndexColumn": [4, 1, 3], "DataColumn2": ["j", "k", "l"]})
df3 = pd.DataFrame({"IndexColumn": ["a", "b", "c"], "DataColumn1": ["x", "y", "z"]})
df4 = pd.DataFrame({"IndexColumn": ["d", "a", "c"], "DataColumn2": ["j", "k", "l"]})
df1_2_merged = pd.concat([df1.set_index("IndexColumn"), df2.set_index("IndexColumn")], axis="columns")
print("Integer index retained name:", df1_2_merged.index.name == "IndexColumn")
# True
df3_4_merged = pd.concat([df3.set_index("IndexColumn"), df4.set_index("IndexColumn")], axis="columns")
print("String index retains name:", df3_4_merged.index.name == "IndexColumn")
# False
```
#### Problem description
`pd.concat([df, other_df], axis="columns")` with two dataframes with identical index names sometimes retains the index name, but at other times drops it. The index values themselves are kept, and the behaviour is otherwise as expected.
It seems logical to always keep the index name if it is the same between both, rather than having inconsistent behaviour.
This _may_ be the same as #35847. However, that discusses `Index.union()`, and taking the union of the `df3` and `df4` indices directly results in the name being propagated to the output, unlike the example above:
```
>>> df3_index = df3.set_index("IndexColumn").index
>>> df4_index = df4.set_index("IndexColumn").index
>>> df3_index.union(df4_index)
Index(['a', 'b', 'c', 'd'], dtype='object', name='IndexColumn')
```
(I've not been able to test the master branch as it appears to now require Python 3.7, and I haven't got a Py3.7 environment handy currently)
#### Expected Output
In the code example above, I'd expect both to output "True", i.e. that the index name is copied through to the output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.19.1.el7.x86_64
Version : #1 SMP Thu Aug 20 14:39:03 CDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 45.2.0
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.16.0
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.0
</details>
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BUG: crosstab fails with categoricals on master
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| 6
| 2020-10-28T10:31:34Z
| 2020-10-31T15:16:56Z
| 2020-10-31T15:16:56Z
|
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.
---
crosstab no longer works correctly with categorical inputs. This is a regression from 1.1.x.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
g = np.random.default_rng(840812492384587325982704)
a = pd.Series(g.integers(0,3,size=100)).astype("category")
b = pd.Series(g.integers(0,2,size=100)).astype("category")
pd.crosstab(a,b,margins=True,dropna=False)
```
Produces
```
Traceback (most recent call last):
File "C:\git\pandas\pandas\core\indexes\base.py", line 2965, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
File "pandas\_libs\index.pyx", line 98, in pandas._libs.index.IndexEngine.get_loc
self._check_type(val)
File "pandas\_libs\index_class_helper.pxi", line 93, in pandas._libs.index.Int64Engine._check_type
raise KeyError(val)
KeyError: 'All'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "pandas\_libs\index.pyx", line 705, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
indices = [0 if checknull(v) else lev.get_loc(v) + 1
File "C:\git\pandas\pandas\core\indexes\base.py", line 2963, in get_loc
casted_key = self._maybe_cast_indexer(key)
File "C:\git\pandas\pandas\core\indexes\category.py", line 527, in _maybe_cast_indexer
return self._data._unbox_scalar(key)
File "C:\git\pandas\pandas\core\arrays\categorical.py", line 1728, in _unbox_scalar
code = self.categories.get_loc(key)
File "C:\git\pandas\pandas\core\indexes\base.py", line 2967, in get_loc
raise KeyError(key) from err
KeyError: 'All'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\git\pandas\pandas\core\generic.py", line 3776, in _set_item
loc = self._info_axis.get_loc(key)
File "C:\git\pandas\pandas\core\indexes\multi.py", line 2795, in get_loc
return self._engine.get_loc(key)
File "pandas\_libs\index.pyx", line 708, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
raise KeyError(key)
KeyError: ('__dummy__', 'All')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-3-ba4a70b3d31b>", line 7, in <module>
pd.crosstab(a,b,margins=True,dropna=False)
File "C:\git\pandas\pandas\core\reshape\pivot.py", line 623, in crosstab
**kwargs,
File "C:\git\pandas\pandas\core\frame.py", line 6972, in pivot_table
observed=observed,
File "C:\git\pandas\pandas\core\reshape\pivot.py", line 180, in pivot_table
fill_value=fill_value,
File "C:\git\pandas\pandas\core\reshape\pivot.py", line 242, in _add_margins
table, data, values, rows, cols, aggfunc, observed, margins_name
File "C:\git\pandas\pandas\core\reshape\pivot.py", line 331, in _generate_marginal_results
piece[all_key] = margin[key]
File "C:\git\pandas\pandas\core\frame.py", line 3083, in __setitem__
self._set_item(key, value)
File "C:\git\pandas\pandas\core\frame.py", line 3160, in _set_item
NDFrame._set_item(self, key, value)
File "C:\git\pandas\pandas\core\generic.py", line 3779, in _set_item
self._mgr.insert(len(self._info_axis), key, value)
File "C:\git\pandas\pandas\core\internals\managers.py", line 1173, in insert
new_axis = self.items.insert(loc, item)
File "C:\git\pandas\pandas\core\indexes\multi.py", line 3667, in insert
level = level.insert(lev_loc, k)
File "C:\git\pandas\pandas\core\indexes\category.py", line 695, in insert
code = self._data._validate_insert_value(item)
File "C:\git\pandas\pandas\core\arrays\categorical.py", line 1186, in _validate_insert_value
return self._validate_fill_value(value)
File "C:\git\pandas\pandas\core\arrays\categorical.py", line 1222, in _validate_fill_value
f"'fill_value={fill_value}' is not present "
ValueError: 'fill_value=All' is not present in this Categorical's categories
```
#### Problem description
Previously produced the correct output. A simple regression.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d89331b96e919acc2a83f7f6201830d246018e36
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.0.dev0+948.gd89331b96
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 : None
lxml.etree : 4.4.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: v0.10.0dev0+30.gadb67b2
bs4 : 4.8.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NTExNDUxMDYx
| 37,466
|
Backport PR #35898 on branch 1.1.x: CI: docker 32-bit linux build
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Backport PR #35898
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BUG: Correct crosstab for categorical inputs
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CONTRIBUTOR
| null |
Change catch types to reflect error changes
closes #37465
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BUG: Fix for #37454: allow reversed axis when plotting with TimedeltaIndex
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TST/REF: finish collecting sample tests
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Parametrizes a few tests from the frame.methods.test_sample file, splits up giant tests from the generic file.
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Backport PR #35875 on branch 1.1.x (COMPAT: Ensure rolling indexers return intp during take operations)
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NONE
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Backport PR #35875: COMPAT: Ensure rolling indexers return intp during take operations
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TST: collect tests by method from test_api
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CI: 32 bit maybe_indices_to_slice
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MEMBER
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CLN: Add init files in test folders
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was not sure why they were missing there, so I added them
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REF: helper to ensure column indexer is iterable
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MEMBER
| null |
Helper method separated out from upcoming PR to split _setitem_with_indexer into more manageable pieces, which in turn is part of fixing a goes-through-two-separate-paths problem.
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CI: troubleshoot ResourceWarnings
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BUG: setting float on integer column with DataFrame.at casts the value to integer.
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| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [5]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [3, 4, 5]})
In [6]: df
Out[6]:
A B
0 1 3
1 2 4
2 3 5
In [7]: df.at[1, 'B'] = 1.2
In [8]: df
Out[8]:
A B
0 1 3
1 2 1
2 3 5
```
#### Problem description
`DataFrame.at` sets the new value but casts it to integer.
#### Expected output
This is not the issue with `loc`:
```python
In [9]: df.loc[1, 'B'] = 1.2
In [10]: df
Out[10]:
A B
0 1 3.0
1 2 1.2
2 3 5.0
```
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ENH: Unhelpful output from assert_frame_equal when indexes differ and check_like=True
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|
CONTRIBUTOR
| null |
#### Problem:
Calling ```testing.assert_frame_equal``` with mismatched indexes and ```check_like=True``` generates unhelpful output.
If you run:
```python
import pandas as pd
df1 = pd.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, 5.0, 6.0]}, index=["a", "b", "c"])
df2 = pd.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, 5.0, 6.0]}, index=["a", "b", "d"])
pd.testing.assert_frame_equal(df1, df2, check_like=True)
```
The output will be:
```
AssertionError: DataFrame.iloc[:, 0] (column name="A") are different
DataFrame.iloc[:, 0] (column name="A") values are different (33.33333 %)
[index]: [a, b, d]
[left]: [1.0, 2.0, nan]
[right]: [1.0, 2.0, 3.0]
```
The data of the input DataFrames are not actually different (there is no nan), but when ```check_like=True``` the code calls ```left.reindex_like(right)``` before comparing indexes (and columns), in order to ensure that both frames are ordered the same.
However, if the indexes contain different values (rather than the same values in a different order),
the ```reindex_like``` function fills the data values (row or column) for the mismatched index entries with NaNs.
This results in the subsequent index checks passing, but the ```assert_frame_equals``` function failing
with a data not equal error (as above).
Even more confusingly, if the values being compared are not floats then you get a dtype not equal error:
```
AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different
Attribute "dtype" are different
[left]: float64
[right]: int64
```
These messages are quite unhelpful, as the mismatch is in the index, and the error should logically be the same as you would get if you ran with ```check_like=False```.
#### Applies to:
The code above was run against the latest code from master.
```python
>>> print(pd.__version__)
1.2.0.dev0+950.gd321be6
```
#### Solution:
The message for the above assertion failure should be something like:
```
AssertionError: DataFrame.index are different
DataFrame.index values are different (33.33333 %)
[left]: Index(['a', 'b', 'c'], dtype='object')
[right]: Index(['a', 'b', 'd'], dtype='object')
```
Which is what you get if you run with ```check_like=False```.
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ENH: Fix output of assert_frame_equal if indexes differ and check_like=True
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|
CONTRIBUTOR
| null |
- [x] closes #37478
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As described in #37478 This PR fixes some very misleading output when ```assert_frame_equal``` is called with differing index values and ```check_like=True```
I have added a ```check_order``` parameter to the ```assert_index_equal``` function. This does essentially the same thing as the ```check_like``` parmeter on ```assert_frame_equal``` (but for indexes). I think ```check_order``` gives a much clearer idea of what the parameter does, but it is a break from the previous naming. I am unsure whether I should have stuck with ```check_like``` for the new parameter.
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BUG: category index levels casted to non-category dtype in merge
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open
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[] | null | 1
| 2020-10-29T01:59:25Z
| 2021-08-14T04:04:45Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
df1 = pd.DataFrame({'x': [i for i in range(100)], 'y': [i for i in range(100)],
'z': [i for i in range(100)], 'd': [i for i in range(100)]})
df2 = df1.astype({"x":'category', "y":'category', "z":'category'})
df3 = df2.iloc[:10, :].groupby(['z', 'x'], observed=True).agg({'d': 'sum'})
df4 = df2.iloc[90:, :].groupby(['z', 'x', 'y'], observed=True).agg({'d': 'sum'})
pd.merge(df3, df4, left_index=True, right_index=True,
how='outer').index.to_frame().dtypes
```
#### Problem description
I have 2 data frames, df3, and df4. Index of df3 is z, x and index of df4 is z, x, y.
All x, y, z are categorical, and index x is of the same category-dtype in df3 as in df4, since df3 and df4 are created from groupby on the same data frame df2. For the same reason, index z is of the same category-dtype in df3 as in df4.
So, I think after the merge, index x and z should stay as category.
But they are not, this is what I get:
z int64
x int64
y category
dtype: object
#### Expected Output
z category
x category
y category
dtype: object
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.1
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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REF: separate out cases in setitem_with_indexer
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| 0
| 2020-10-29T02:50:57Z
| 2020-10-30T17:10:45Z
| 2020-10-30T16:06:06Z
|
MEMBER
| null |
Preparatory to fixing a multiple-paths issue in setitem_with_indexer
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|
ENH: Allow tz-aware origin parameter in pandas.to_datetime
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[] | null | 2
| 2020-10-29T13:21:15Z
| 2021-01-05T04:57:34Z
| 2021-01-05T04:57:34Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I wish I could use pandas to map back and forth between tz-aware timestamps and floats.
This has been raised before
[#16842](https://github.com/pandas-dev/pandas/issues/16842)
however it was argued that the incumbent behaviour did not need altering, but I'd like to plead my case below.
#### Describe the solution you'd like
Currently if *origin* parameter in pd.to_datetime is tz-aware an exception is produced;
*ValueError: origin offset ..... must be tz-naive*
I believe if a tz-aware origin is used then the result of pd.to_datetime should be a tz-aware timestamp.
#### API breaking implications
I believe an implementation is possible which does not break the api, where the timezone is extracted from the origin parameter (and it could be None) and used to localize other timestamps in the code where necessary.
#### Describe alternatives you've considered
I have considered writing my own to_datetime to accept timezone aware origins, or perhaps using tz_convert as a mediator between the mapping, however I would still argue the default behaviour of to_datetime is not ideal.
#### Additional context
Consider the following code, and for context in the Australia/Sydney timezone clocks were wound forward an hour at 2am on the 4th of October 2020.
```python
import pandas as pd
tz = pytz.timezone('Australia/Sydney')
origin = pd.Timestamp('2020-10-04', tz=tz)
test_date_1 = pd.Timestamp('2020-10-04 1:00', tz=tz)
test_date_2 = pd.Timestamp('2020-10-04 3:00', tz=tz)
print(f'There is {(test_date_1 - origin).total_seconds()/3600} hours from origin to {test_date_1}')
print(f'There is {(test_date_2 - origin).total_seconds()/3600} hours from origin to {test_date_2}')
```
The code will correctly calculate the time delta, with an origin set to the start of the day:
*There is 1.0 hours from origin to 2020-10-04 01:00:00+10:00*
*There is 2.0 hours from origin to 2020-10-04 03:00:00+11:00*
How can I map the number 2 back to test_date_2?
This code
```python
pd.to_datetime(2, unit='h', origin=origin)
```
produces
*ValueError: origin offset 2020-10-04 00:00:00+10:00 must be tz-naive*
This code
```python
pd.to_datetime(2, unit='h', origin=origin.tz_localize(None))
```
produces a tz-naive Timestamp: *Timestamp('2020-10-04 02:00:00')*
Trying to localize it of course does not work, and raises an exception
```python
pd.to_datetime(2, unit='h', origin=origin.tz_localize(None)).tz_localize(tz)
```
*NonExistentTimeError: 2020-10-04 02:00:00*
The answer to "What time is 2hrs past midnight on the 4th of October 2020 in Sydney. Australia" is not ambiguous. It has an answer and I believe pandas should be able to accomodate this.
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|
BUG: Rolling calls custom function with wrong index
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[] | null | 4
| 2020-10-29T13:31:56Z
| 2021-08-14T04:08:41Z
| 2021-08-14T04:08:41Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
def test_count(x):
last_index = None
count = 0
print(" - test_count")
for index, value in x.items():
print(" - - index, value: {}, {}".format(index, value))
last_index = index
count += 1
res = pd.Series([count], index=[last_index])
return res
def test():
df = pd.DataFrame({'B': [3030, 3030, 1010],
'C': [1, 1, 1]},
index = [ pd.Timestamp('2020-04-13 04:30:30'),
pd.Timestamp('2020-04-13 04:30:30'),
pd.Timestamp('2020-04-13 05:10:10'),])
print(df)
df_grouped_by_B = df.groupby('B')
df_tmp = pd.DataFrame(df_grouped_by_B.rolling("1H").B.agg(
{
"count" : test_count,
}
))
print(df_tmp)
print("pandas {}".format(pd.__version__))
test()
```
#### Problem description
The problem in the calling of test_count function with wrong Series objects, the first call of test_count should be with Series (2020-04-13 04:30:30, 1010) as in previous pandas version (1.0.5), but the new version calls function with wrong argument (index, value: 2020-04-13 05:10:10, 1010). This wrong Series object is not possible for above DataFrame.
(This is minimal example for repeat the problem. I use more difficult processing in agg function.)
#### Expected Output (1.0.5 version works right)
```
pandas 1.0.5
B C
2020-04-13 04:30:30 3030 1
2020-04-13 04:30:30 3030 1
2020-04-13 05:10:10 1010 1
- test_count
- - index, value: 2020-04-13 05:10:10, 1010.0
- test_count
- - index, value: 2020-04-13 04:30:30, 3030.0
- test_count
- - index, value: 2020-04-13 04:30:30, 3030.0
- - index, value: 2020-04-13 04:30:30, 3030.0
count
B
1010 2020-04-13 05:10:10 1.0
3030 2020-04-13 04:30:30 1.0
2020-04-13 04:30:30 2.0
```
#### Output with pandas 1.1.3
```
pandas 1.1.3
B C
2020-04-13 04:30:30 3030 1
2020-04-13 04:30:30 3030 1
2020-04-13 05:10:10 1010 1
- test_count
- - index, value: 2020-04-13 04:30:30, 1010.0
- test_count
- - index, value: 2020-04-13 04:30:30, 3030.0
- test_count
- - index, value: 2020-04-13 04:30:30, 3030.0
- - index, value: 2020-04-13 05:10:10, 3030.0
count
B
1010 2020-04-13 05:10:10 1.0
3030 2020-04-13 04:30:30 1.0
2020-04-13 04:30:30 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-51-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.51.2
</details>
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BUG (Performance): Performance of to_csv varies significantly depending on when/how index is set
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[] | null | 6
| 2020-10-29T13:55:08Z
| 2021-06-06T19:53:57Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
There are some historic "to_csv is slow" reports, but none mention the specific behaviour I've seen with regard to indices.
- [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 cProfile
import time
import pstats
from io import BytesIO, StringIO
import pandas as pd
import numpy as np
NUM_INDEX_COLS = 3
NUM_COLS = 50
NUM_ROWS = 1000000
# Generate a large-ish dummy dataframe
data = {}
idx_cols = []
for idx_id in range(NUM_INDEX_COLS):
idx_name = f"Index_{idx_id}"
idx_cols.append(idx_name)
if idx_id == 0:
data[idx_name] = np.random.randint(0, NUM_ROWS, NUM_ROWS)
else:
data[idx_name] = np.full(NUM_ROWS, 1, dtype=np.int)
for col_id in range(NUM_COLS):
col_name = f"Column_{col_id}"
data[col_name] = np.random.uniform(0, 100000.0, NUM_ROWS)
source_df = pd.DataFrame(data)
source_df_indexed = source_df.set_index(idx_cols)
# Default index - fast
df1 = source_df.head(10000)
# MultiIndex from idx_cols, then take head - slow
df2 = source_df_indexed.head(10000)
# Take head, then set MultiIndex from idx_cols - fast
df3 = source_df.head(10000).set_index(idx_cols)
# Deepcopy of df2 - slow (docs say that Index is not copied, so not surprising)
df4 = df2.copy(deep=True)
# Will run each df twice, and take timings from second run.
dataframes = [df1, df1, df2, df2, df3, df3, df4, df4]
#### WRITE TO CSV WITH EXISTING INDEX
#
# df1: 0.53s, with no index set
# df2: 2.35s, with index set before calling .head()
# df3: 0.54s, with index set after calling .head()
# df4: 2.12s, deep copy of df2
#
for i, df in enumerate(dataframes):
out = StringIO()
t1 = time.time()
df.to_csv(out)
t2 = time.time()
if (i % 2) == 1:
print(t2-t1)
#### WRITE TO CSV WITH INCLUDED CALL TO RESET INDEX
#
# df1: 0.55s, with no index set
# df2: 0.53s, with index set before calling .head()
# df3: 0.54s, with index set after calling .head()
# df4: 0.54s, deep copy of df2
for i, df in enumerate(dataframes):
out = StringIO()
t1 = time.time()
df.reset_index().to_csv(out)
t2 = time.time()
if (i % 2) == 1:
print(t2-t1)
```
#### Problem description
The speed of `to_csv` varies significantly depending on whether an Index was set, and when/how that Index was set. This is not the case with e.g. `to_parquet`. The original case where I noticed this speed difference is not so trivial, but it involves a MultiIndexed dataframe that has been manipulated before being output to CSV, taking approximately 50 minutes to output a ~30GB file.
(Yes, CSV is slow and horrible for many other reasons even at the best of times, and we avoid it as much as possible.)
In the example above, I create a million row dataframe `source_df`, with three integer columns (two have the value `1` throughout), and 50 float columns. I then create four subsets of the first 10000 rows, doing different things with the index:
* df1: Just the source dataframe, no index modification (so it'll have the default RangeIndex)
* df2: Set the three integer columns as a MultiIndex, and then take .head(10000)
* df3: Take .head(10000), and then set the three integer columns as a MultiIndex
* df4: Deep copy of df2. (The Pandas documentation suggests that at least the index won't actually be copied).
I then time how long it takes to write each out to CSV (using a StringIO, to avoid IO speed concerns). df1 (no index) and df3 (index set after taking .head) are fast; df2 (index set before taking .head) and df4 (deepcopy of df2) are much slower. It appears that the index being a subset of a larger dataframe significantly slows down the writing.
I then do a separate test, whereby I reset the index of each dataframe immediately before writing. In this case, all write out as fast as df1/df3 in the original test.
I have also tested with `to_parquet` by changing `to_csv` to `to_parquet` and providing a BytesIO. All four dataframes are output in 0.08s, regardless of index origin and whether the index is reset or not.
Surprisingly (for me), profiling indicates that `{method 'astype' of 'numpy.ndarray' objects}` is where the extra time originates from. There aren't many extra calls - they just take much, much longer. If you're lucky, this might even be an upstream issue!
Summarised profiling output for df1, df2, and df3 in the don't-reset-index case below.
<details>
```
df1 - Default index, fast
1928 function calls (1910 primitive calls) in 0.530 seconds
Ordered by: internal time
List reduced from 126 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
12 0.285 0.024 0.285 0.024 {method 'astype' of 'numpy.ndarray' objects}
6 0.242 0.040 0.242 0.040 {pandas._libs.writers.write_csv_rows}
19 0.001 0.000 0.001 0.000 missing.py:193(_isna_ndarraylike)
6 0.000 0.000 0.530 0.088 csvs.py:330(_save_chunk)
20/13 0.000 0.000 0.000 0.000 {built-in method numpy.array}
384/382 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}
278 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
6 0.000 0.000 0.282 0.047 blocks.py:1938(to_native_types)
1 0.000 0.000 0.530 0.530 csvs.py:313(_save)
200 0.000 0.000 0.000 0.000 generic.py:10(_check)
12 0.000 0.000 0.000 0.000 blocks.py:292(getitem_block)
19 0.000 0.000 0.001 0.000 missing.py:130(_isna)
6 0.000 0.000 0.000 0.000 generic.py:3551(_slice)
12 0.000 0.000 0.000 0.000 range.py:697(__getitem__)
6 0.000 0.000 0.000 0.000 managers.py:757(get_slice)
19 0.000 0.000 0.000 0.000 dtypes.py:903(is_dtype)
12 0.000 0.000 0.000 0.000 range.py:153(_data)
7 0.000 0.000 0.004 0.001 base.py:984(_format_native_types)
6 0.000 0.000 0.000 0.000 {built-in method numpy.arange}
6 0.000 0.000 0.001 0.000 blocks.py:656(to_native_types)
df2 - MultiIndex set before head, slow
10629 function calls (10518 primitive calls) in 2.135 seconds
Ordered by: internal time
List reduced from 182 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
20 1.630 0.082 1.630 0.082 {method 'astype' of 'numpy.ndarray' objects}
5 0.247 0.049 0.247 0.049 {pandas._libs.writers.write_csv_rows}
122/91 0.163 0.001 0.163 0.002 {built-in method numpy.array}
6 0.042 0.007 1.588 0.265 base.py:954(to_native_types)
15 0.027 0.002 0.027 0.002 {pandas._libs.lib.infer_dtype}
1 0.016 0.016 2.135 2.135 csvs.py:313(_save)
15 0.001 0.000 0.001 0.000 {pandas._libs.algos.take_1d_object_object}
21 0.001 0.000 0.002 0.000 missing.py:193(_isna_ndarraylike)
2323/2321 0.001 0.000 0.001 0.000 {built-in method builtins.isinstance}
5 0.000 0.000 0.001 0.000 {pandas._libs.lib.fast_zip}
30/15 0.000 0.000 0.192 0.013 base.py:293(__new__)
1386 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
16 0.000 0.000 1.350 0.084 base.py:984(_format_native_types)
5 0.000 0.000 2.119 0.424 csvs.py:330(_save_chunk)
5 0.000 0.000 1.546 0.309 multi.py:1201(_format_native_types)
910 0.000 0.000 0.000 0.000 generic.py:10(_check)
90 0.000 0.000 0.000 0.000 base.py:498(_shallow_copy)
105 0.000 0.000 0.000 0.000 base.py:463(_simple_new)
171 0.000 0.000 0.000 0.000 common.py:1460(is_extension_array_dtype)
217 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}
df3 - MultiIndex set after head, fast
10629 function calls (10518 primitive calls) in 0.554 seconds
Ordered by: internal time
List reduced from 182 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
20 0.301 0.015 0.301 0.015 {method 'astype' of 'numpy.ndarray' objects}
5 0.243 0.049 0.243 0.049 {pandas._libs.writers.write_csv_rows}
122/91 0.002 0.000 0.002 0.000 {built-in method numpy.array}
15 0.001 0.000 0.001 0.000 {pandas._libs.lib.infer_dtype}
21 0.001 0.000 0.001 0.000 missing.py:193(_isna_ndarraylike)
2323/2321 0.000 0.000 0.001 0.000 {built-in method builtins.isinstance}
1386 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
6 0.000 0.000 0.028 0.005 base.py:954(to_native_types)
30/15 0.000 0.000 0.004 0.000 base.py:293(__new__)
5 0.000 0.000 0.000 0.000 {pandas._libs.lib.fast_zip}
1 0.000 0.000 0.554 0.554 csvs.py:313(_save)
5 0.000 0.000 0.554 0.111 csvs.py:330(_save_chunk)
910 0.000 0.000 0.000 0.000 generic.py:10(_check)
5 0.000 0.000 0.027 0.005 multi.py:1201(_format_native_types)
15 0.000 0.000 0.000 0.000 {pandas._libs.algos.take_1d_object_object}
171 0.000 0.000 0.000 0.000 common.py:1460(is_extension_array_dtype)
90 0.000 0.000 0.000 0.000 base.py:498(_shallow_copy)
105 0.000 0.000 0.000 0.000 base.py:463(_simple_new)
171 0.000 0.000 0.000 0.000 base.py:413(find)
15 0.000 0.000 0.001 0.000 algorithms.py:1616(take_nd)
```
</details>
The simplest (but silliest) suggestion would just be to call `.reset_index()` within `df.to_csv()` if `index=True` is set. However, I suspect this indicates something more fundamental that can be improved.
#### Expected Output
Speed of outputting to CSV does not vary significantly depending on how the index was created, as is the case when outputting to Parquet, etc.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.19.1.el7.x86_64
Version : #1 SMP Thu Aug 20 14:39:03 CDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 45.2.0
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.16.0
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.0
</details>
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| 37,485
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BUG: round() on tz-aware DateTimeIndex (spanning DST switch) throws AmbiguousTimeError as if tz-unaware
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[] | null | 6
| 2020-10-29T15:28:25Z
| 2021-11-13T00:49:05Z
| 2021-11-13T00:49:04Z
|
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
dt = pd.Timestamp("2020-10-25 02:00:00").tz_localize("Europe/Brussels", ambiguous=False).round('min')
```
```log
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 489, in pandas._libs.tslibs.timestamps.Timestamp.round
File "pandas/_libs/tslibs/timestamps.pyx", line 443, in pandas._libs.tslibs.timestamps.Timestamp._round
File "pandas/_libs/tslibs/timestamps.pyx", line 826, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
File "pandas/_libs/tslibs/tzconversion.pyx", line 232, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from 2020-10-25 02:00:00, try using the 'ambiguous' argument
```
```python
import pandas as pd
dt = pd.Timestamp("2020-10-25 02:00:00").tz_localize("Europe/Brussels", ambiguous=True).round('min')
```
```log
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 489, in pandas._libs.tslibs.timestamps.Timestamp.round
File "pandas/_libs/tslibs/timestamps.pyx", line 443, in pandas._libs.tslibs.timestamps.Timestamp._round
File "pandas/_libs/tslibs/timestamps.pyx", line 826, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
File "pandas/_libs/tslibs/tzconversion.pyx", line 232, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from 2020-10-25 02:00:00, try using the 'ambiguous' argument
```
#### Problem description
A localized Timestamp does not give an error when rounding.
A Series of a localized DateTimeIndex shouldn't either.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
Output is expected to be identical as for
```python
pandas.Timestamp("2020-10-25 02:00:00+01:00").round('min')
```
respectively
```python
pandas.Timestamp("2020-10-25 02:00:0+02:00").round('min')
```
i.e. without AmbiguousTimeError
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.1-050901-generic
Version : #202010170731 SMP Sat Oct 17 07:42:59 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 : 1.19.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
rest is not installed
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BUG: ValueError when assigning list in a single-row object dataframe
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| 0
| 2020-10-29T15:31:16Z
| 2020-12-05T17:14:05Z
| 2020-12-05T17:14:05Z
|
NONE
| null |
- [ X ] I have checked that this issue has not already been reported. #15490 looks kind of similar, but seems like it was fixed. #17256 is also similar but it uses deprecated methods.
- [ X ] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([['a', '1', '2.0']], columns=['a', 'b', 'c'], dtype=object)
df['c'].iloc[0] = [df['c'].iloc[0]]
```
#### Problem description
The current behaviour raises a ValueError, even though the assignment happened. Stack trace:
<details>
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-12824bd17b66> in <module>()
2
3 df = pd.DataFrame([['a', '1', '2.0']], columns=['a', 'b', 'c'], dtype=object)
----> 4 df['c'].iloc[0] = [df['c'].iloc[0]]
4 frames
/usr/local/lib/python3.6/dist-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):
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1800 # actually do the set
1801 self.obj._consolidate_inplace()
-> 1802 self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
1803 self.obj._maybe_update_cacher(clear=True)
1804
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py in setitem(self, indexer, value)
532
533 def setitem(self, indexer, value) -> "BlockManager":
--> 534 return self.apply("setitem", indexer=indexer, value=value)
535
536 def putmask(
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, **kwargs)
404 applied = b.apply(f, **kwargs)
405 else:
--> 406 applied = getattr(b, f)(**kwargs)
407 result_blocks = _extend_blocks(applied, result_blocks)
408
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py in setitem(self, indexer, value)
885 values[indexer] = value
886
--> 887 values = values.astype(arr_value.dtype, copy=False)
888
889 # set
ValueError: setting an array element with a sequence
```
</details>
#### Expected Output
As the `dtype` is `object`, I would have expected this assignment to happen without raising a ValueError. If the dataframe has more than one row:
```python
df = pd.DataFrame([['a', '1', '2.0']]*2, columns=['a', 'b', 'c'])
df['c'].iloc[0] = [df['c'].iloc[0]]
```
Then the exception is not thrown.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.112+
Version : #1 SMP Thu Jul 23 08:00:38 PDT 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 : 1.18.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 50.3.0
Cython : 0.29.21
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 0.14.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTEyMzc5OTc5
| 37,487
|
TST/REF: loc/iloc/at/iat tests go in tests.indexing
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| 2
| 2020-10-29T15:54:34Z
| 2020-10-30T14:44:14Z
| 2020-10-30T13:28:08Z
|
MEMBER
| null |
not tests.(series|frame).indexing
`(Series|DataFrame).__(getitem|setitem)__` tests go in the tests.(series|frame).indexing
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QST: assert_frame_equal with disabled column type check fails unexpectedly
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[] | null | 1
| 2020-10-29T16:19:05Z
| 2021-08-14T04:26:35Z
| null |
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 have two dataframes with different column types that I want to compare. I thought the pd.testing.assert_frame_equal function with check_column_type would be the best solution:
import pandas as pd
df1 = pd.DataFrame({"1": [1, 2 ,3], "2": [1, 5, 9]})
df2 = pd.DataFrame({1: [1, 2 ,3], 2: [1, 5, 9]})
pd.testing.assert_frame_equal(df1, df2, check_column_type=False)
But I get:
DataFrame.columns classes are not equivalent
[left]: Index(['1', '2'], dtype='object')
[right]: Int64Index([1, 2], dtype='int64')
Am I missing anything?
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BUG: mypy doesn't understand the ~ operand for Series
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| 2020-10-29T16:45:08Z
| 2021-02-28T13:04:44Z
| 2021-02-28T13:04:44Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# save it like test.py
import pandas as pd
s = pd.Series([False])
print(~s)
```
then running `mypy` on this code I get the following errors:
```bash
$ mypy test.py
test.py:3: error: Too many arguments for "Series"
tesrt.py:4: error: Unsupported operand type for ~ ("Series")
```
#### Problem description
Since the code is correct, mypy is flagging things wrongly.
#### Expected Output
mypy should accept that `~` operand for Series (as well as the arguments).
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 5532ae8bfb5e6a0203172f36c0738e9c345956a6
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 : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.0.dev0+964.g5532ae8bf
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
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
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
Also
```
mypy 0.790
mypy-extensions 0.4.3
```
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MDU6SXNzdWU3MzI1MDIzOTg=
| 37,490
|
BUG: File saved with to_stata and version=119 cannot be opened with STATA 15.1 SE
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| 3
| 2020-10-29T17:13:45Z
| 2020-11-10T03:38:45Z
| 2020-11-10T03:38:45Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version 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({"A":[1]})
df.to_stata("a.dta", version=119)
```
#### Problem description
I have Stata/SE 15.1.
I run the above code, then try to open `a.dta` with my version of Stata.
But Stata doesn't open the file and instead gives the following error:
> dataset too large
> This .dta file format was created by Stata/MP and has more variables than your Stata can handle.
Of course the file is not too large, since it contains only 1 observation and 1 variable.
It looks like pandas is saving the file in the "MP" version.
I propose to save it in the SE version, or at least specify which sub-version of Stata the file is saved in in the documentation.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.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 : Italian_Italy.1252
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 : 1.5.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDU6SXNzdWU3MzI1MTY1MTc=
| 37,491
|
BUG: Inconsistent data type conversion with .sum
|
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[] | null | 10
| 2020-10-29T17:33:34Z
| 2020-10-30T10:49:51Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
print(s.sum().dtype) # int64
print(s.astype("float16").sum().dtype) # float16
print(s.astype("float32").sum().dtype) # float32
print(s.astype("int16").sum().dtype) # int64 ???
print(s.astype("uint8").sum().dtype) # int64 ???
```
#### Problem description
Summing a `float16` Series/DataFrame returns `float16` values.
Summing a `float32` Series/DataFrame returns `float32` values.
However, summing integer Series/DataFrames always returns `int64` values, regardless of their original type ( `int16`, `uint8` etc.).
#### Expected Output
Summing `int32` values should return `int32` values, just like summing `float32` values returns `float32` values.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.16299
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Italian_Italy.1252
pandas : 1.1.3
numpy : 1.19.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
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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| 37,492
|
CLN: unify numpy.random-related imports
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| 5
| 2020-10-29T18:41:55Z
| 2020-10-30T14:08:07Z
| 2020-10-30T13:22:51Z
|
CONTRIBUTOR
| null |
- [x] closes #37053 ~~(keep this issue open until both #37492 and #37117 are completed)~~
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
# update 2020-10-30
This is the replacement for #37103, since I messed up the git timeline in the previous PR(the conflicts are too complex to fix). The separate CI is in #37117.
The whole project is reformatted except `pandas/_testing.py` for there are quite a few `tm.randn` used in testing files.
below is the script to do the whole clean-up
```
import os
import re
class NumpyRandomRelatedScript:
def __init__(self, base_dir: str = "/dir/of/pandas/pandas") -> None:
self.base_dir = base_dir
self.py_files = []
self.backup_files = []
self.p1 = re.compile("import numpy as np")
self.p2 = re.compile("from numpy.random import[ ,a-zA-Z]*[\s]")
self.p3 = re.compile("from numpy import random[\s]")
def search_py_files(self, pandas_file_dir: str) -> None:
if os.path.isfile(pandas_file_dir):
if pandas_file_dir[-3:] == ".py":
if pandas_file_dir != os.path.join(self.base_dir, "_testing.py"):
self.py_files.append(pandas_file_dir)
elif os.path.isdir(pandas_file_dir):
for d in os.listdir(pandas_file_dir):
self.search_py_files(os.path.join(pandas_file_dir, d))
def do_the_clean_up(self, file_dir: str) -> None:
with open(file_dir, "r") as file:
data = file.read()
m1, m2, m3 = (
re.search(self.p1, data),
re.search(self.p2, data),
re.search(self.p3, data),
)
if not (m2 or m3):
# print("There's no need to change, please recheck!")
return
backup_dir = file_dir + ".issue37053_backup"
self.backup_files.append(backup_dir)
print("Backup: " + backup_dir)
with open(backup_dir, "w+") as file:
file.write(data)
with open(file_dir, "w+") as file:
if m2:
if not m1:
data = re.sub(self.p2, "import numpy as np\n", data)
m1 = True
else:
data = re.sub(self.p2, "", data)
methods = (
m2.group(0)
.replace("from numpy.random import", "")
.replace(" ", "")
.replace("\n", "")
.split(",")
)
if isinstance(methods, str):
methods = [methods]
for method in methods:
data = re.sub(
r"[\s]{meth}[^a-z]".format(meth=method),
" np.random.{meth}(".format(meth=method),
data,
)
data = re.sub(
r"[\s]-{meth}[^a-z]".format(meth=method),
" -np.random.{meth}(".format(meth=method),
data,
)
data = re.sub(
r"[\(]{meth}[^a-z]".format(meth=method),
"(np.random.{meth}(".format(meth=method),
data,
)
if m3:
if not m1:
data = re.sub(self.p3, "import numpy as np\n", data)
else:
data = re.sub(self.p3, "", data)
data = re.sub(r"[\s]random.{1}", " np.random.", data)
data = re.sub(r"[\(]random.{1}", "(np.random.", data)
file.write(data)
print("Clean: " + file_dir)
def remove_backup(self) -> None:
for file in self.backup_files:
print("Remove: " + file)
os.remove(file)
if __name__ == "__main__":
script = NumpyRandomRelatedScript()
script.search_py_files(script.base_dir)
for f in script.py_files:
script.do_the_clean_up(f)
script.remove_backup()
```
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MDU6SXNzdWU3MzI1OTA0NDk=
| 37,493
|
ENH: support masked arrays in groupby cython algos
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| 1
| 2020-10-29T19:23:02Z
| 2021-09-22T15:27:50Z
| null |
MEMBER
| null |
Similarly as for normal reductions (eg https://github.com/pandas-dev/pandas/pull/30982), we should investigate having masked array-specific support in the groupby algorithms.
Currently, when starting from a nullable extension array, they get converted to a numpy array (eg integers with missing values will typically get cast to float with nan) before passing to the cython algorithm.
Having support for passing a mask to the cython algos can improve the groupby support for nullable dtypes.
* [x] `any` / `all` (correctly pass mask + add Kleene logic) -> https://github.com/pandas-dev/pandas/pull/40819
* [x] `cummin` / `cummax` -> https://github.com/pandas-dev/pandas/pull/40651
* [ ] `cumsum` / `cumprod`
* [ ] `sum` / `prod` / `var` / `mean`
* [x] `min` / `max` -> https://github.com/pandas-dev/pandas/pull/42567
* [ ] `median`
* [ ] `ohlc`
* [ ] `quantile` (correctly pass mask)
* [ ] `last` / `nth`
* [ ] `rank`
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ENH: improve the resulting dtype for groupby operations on nullable dtypes
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| 2020-10-29T19:29:51Z
| 2021-08-14T04:28:06Z
| null |
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/37433, and partly related to https://github.com/pandas-dev/pandas/issues/37493
Currently, after groupby operations we try to cast back to the original dtype when possible (at least in case of extension arrays). But this is not always correct, and also not done consistently. Some examples using the test case from the mentioned PR using a nullable Int64 column as input:
```
In [1]: df = DataFrame(
...: {
...: "A": ["A", "B"] * 5,
...: "B": pd.array([1, 2, 3, 4, 5, 6, 7, 8, 9, pd.NA], dtype="Int64"),
...: }
...: )
In [2]: df.groupby("A")["B"].sum()
Out[2]:
A
A 25
B 20
Name: B, dtype: Int64
In [3]: df.groupby("A")["B"].std()
Out[3]:
A
A 3.162278
B 2.581989
Name: B, dtype: float64
In [4]: df.groupby("A")["B"].mean()
Out[4]:
A
A 5
B 5
Name: B, dtype: Int64
In [5]: df.groupby("A")["B"].count()
Out[5]:
A
A 5
B 4
Name: B, dtype: int64
```
So some observations:
* For `sum()`, we correctly have Int64 for the result
* For `std()`, we could use the nullable Float64 instead of float64 dtype
* For `mean()`, we incorrectly cast back to Int64 dtype, as the result of mean should always be floating (in this case the casting just happened to work because the means were rounded numbers)
* For `count()`, we did not create a nullable Int64 dtype for the result, while this could be done in the input is nullable
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BUG: Series[uintarray] failing to raise KeyError
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| 0
| 2020-10-29T20:24:18Z
| 2020-10-30T22:49:43Z
| 2020-10-30T22:45:57Z
|
MEMBER
| null |
- [x] closes #37218
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2020-10-29T21:19:41Z
| 2020-10-30T09:36:58Z
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MEMBER
| null |
Closes #36373
See discussion at https://github.com/pandas-dev/pandas/pull/36498#issuecomment-708578774 for reverting the original fix (https://github.com/pandas-dev/pandas/pull/30501 for #30484)
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TST: tz_localize/tz_convert unclear test assertions
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MEMBER
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kill off tests.series.test_timezones.
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REGR: fix isin for large series with nan and mixed object dtype (causing regression in read_csv)
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| 2
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MEMBER
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Closes #37094
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Backport PR #37496 on branch 1.1.x (DOC: release date for 1.1.4)
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NONE
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Backport PR #37496: DOC: release date for 1.1.4
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BUG: Groupby any/all raises TypeError for pd.NA
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| 4
| 2020-10-29T21:58:48Z
| 2021-06-21T16:59:23Z
| 2021-06-21T16:59:23Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 1, 2, 2, 3, 3, 4], "b": [1, pd.NA, 2, 3, 4, 5, 6]})
grouped = df.groupby("a")
print(grouped.all())
print(grouped.any())
```
#### Problem description
Both raise a ``TypeError``
Traceback:
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_4.py", line 10, in <module>
print(grouped.all())
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 1424, in all
return self._bool_agg("all", skipna)
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 1375, in _bool_agg
return self._get_cythonized_result(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 2631, in _get_cythonized_result
raise TypeError(error_msg)
TypeError: boolean value of NA is ambiguous
```
Maybe using mask like in ``print(df.all(axis=1))``?
#### Expected Output
Should work like with ``np.nan`` and return
```
b
a
1 True
2 True
3 True
4 True
b
a
1 True
2 True
3 True
4 True
```
#### Output of ``pd.show_versions()``
<details>
master
</details>
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REGR: revert behaviour of getitem with assigning with a Series
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| 2
| 2020-10-29T22:00:13Z
| 2020-10-30T08:42:59Z
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|
MEMBER
| null |
Closes #37427
@jbrockmendel this is reverting part of the clean-up you did in https://github.com/pandas-dev/pandas/pull/33643. I think the behavioural change was unintentional in that PR. We might still want to do it for 1.2, but then with a deprecation first.
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| 0
| 2020-10-29T22:03:15Z
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NONE
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Backport PR #37473: CI: 32 bit maybe_indices_to_slice
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| 0
| 2020-10-29T22:34:09Z
| 2020-10-30T14:49:07Z
| 2020-10-30T13:24:00Z
|
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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REF: Make function for dtype casting in pre-processing of groupby
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MEMBER
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#### Is your feature request related to a problem?
[this should provide a description of what the problem is, e.g. "I wish I could use pandas to do [...]"]
#### Describe the solution you'd like
As mentioned in #37145 the pre processing part in https://github.com/pandas-dev/pandas/blob/6abe5106a63431239081af7c15a1569628cf7628/pandas/core/groupby/groupby.py#L2174 is very generic and could be moved to a function
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ENH: implement Kleene versions of GroupBy any/all kernels for nullable dtypes
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xref https://github.com/pandas-dev/pandas/issues/37493#issuecomment-718974981
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REGR: inplace Series op not actually operating inplace
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| 2020-10-30T15:00:52Z
| 2020-10-30T08:59:45Z
|
MEMBER
| null |
- [x] closes #36373
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
alternative to #37497, ensures that Series inplace ops are actually inplace whenever possible. The whatsnew is copied verbatim from #37497, the new test is copied with an additional assertion about `series._values`
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MDExOlB1bGxSZXF1ZXN0NTEyNzE1NDMy
| 37,509
|
CLN: assorted cleanups
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MDU6SXNzdWU3MzI4MzUzMjU=
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BUG: index label messed up when reset_level then unstack then sort_index
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| 0
| 2020-10-30T02:38:29Z
| 2020-12-31T19:11:28Z
| 2020-12-31T19:11:28Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
df1 = pd.DataFrame({'id1': [1, 2, 3, 4],
'id2': [3,4,1,2],
'id3': [1,1,1,1],
'x': [1,2,3,4]})
df1.set_index(['id1', 'id2', 'id3'], inplace=True)
new_levels = ['n1', 'n2', 'n3', None]
df1.index.set_levels(levels=new_levels, level='id1', inplace=True)
df1.index.set_levels(levels=new_levels, level='id2', inplace=True)
df1.unstack('id3')[('x',1)].sort_index()
```
#### Problem description
I would expect the index after the reset_level operations should be just normal index, but when doing sort_index, it seems index level ‘id1’ of row 1 is incorrectly changed to ‘n1’ from NaN and the same for level ‘id2’ of row 2.
The result is:
id1 id2
n1 n2 4
n3 1
n2 n1 2
n3 n1 3
Name: (x, 1), dtype: int64
#### Expected Output
id1 id2
n1 n3 1
n2 NaN 2
n3 n1 3
NaN n2 4
Name: (x, 1), dtype: int64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.1
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTEyNzE5MTc3
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CLN: remove _vendored/typing_extensions
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| 2020-10-30T02:38:57Z
| 2020-10-30T14:56:51Z
| 2020-10-30T13:20:34Z
|
MEMBER
| null |
- [x] closes #37119
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As discussed in #37119, the vendored version does not serve its intended purpose. Moreover, #37137 shows a way to get by without it.
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BUG: Series.replace does not preserve dtype of original Series
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MDU6SXNzdWU3MzMwMjQyNTc=
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API: alignment behaviour of Series as assignment value in indexing
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| 2020-10-30T09:34:18Z
| 2021-08-14T04:30:46Z
| null |
MEMBER
| null |
Follow up on https://github.com/pandas-dev/pandas/issues/37427
Currently, there is an inconsistency between `__getitem__` and `loc` related to how the RHS value in an assignment gets re-aligned or not.
In this example, the values in Series get just assigned as is, ignoring the index of the series (only requiring that the length matches the number of values in the indexer):
```python
s = pd.Series(range(5))
idx = np.array([1, 4])
>>> s[idx] = Series([10, 11])
>>> s
0 0
1 10
2 2
3 3
4 11
dtype: int64
```
However, with `loc` we have a different behaviour: the Series gets basically aligned with `s` first, and then this realigned series is also indexed with ``idx``, and those values get set:
```python
s = pd.Series(range(5))
idx = np.array([1, 4])
>>> s.loc[idx] = Series([10, 11])
>>> s
0 0.0
1 11.0
2 2.0
3 3.0
4 NaN
dtype: float64
```
you could write what basically happens more explicitly as:
```python
s.loc[idx] = Series([10, 11]).reindex(s.index)[idx]
```
To have the same behaviour as `__getitem__`, you need to assign with an array instead of series:
```python
s = pd.Series(range(5))
idx = np.array([1, 4])
>>> s.loc[idx] = Series([10, 11]).values
>>> s
0 0
1 10
2 2
3 3
4 11
dtype: int64
```
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Backport PR #37499 on branch 1.1.x (REGR: fix isin for large series with nan and mixed object dtype (causing regression in read_csv))
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| 2020-10-30T11:53:34Z
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NONE
| null |
Backport PR #37499: REGR: fix isin for large series with nan and mixed object dtype (causing regression in read_csv)
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ENH: Validate merge is not made on NA values in the merge keys
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[] | null | 3
| 2020-10-30T11:16:31Z
| 2021-02-11T03:12:25Z
| 2021-02-11T03:12:25Z
|
NONE
| null |
#### Is your feature request related to a problem?
I wish pandas merge function would prodive a way to verify that it does not merge on NA values, which, most of the times, are not what we want. Right now, I'm always doing a pre-check that my right dataframe does not have any NA values in keys, but when you are doing 10 merges one after the other, this is cumbersome and bug-prone.
#### Describe the solution you'd like
This would be an additional parameter (e.g. validate_no_na_merge) in pd.merge() function, defaulting to False, which, when True, would just assert that there are no NA values in the keys of the right dataframe. Thus, checking that there won't be any merge on NA keys.
Note this leaves the possibility that keys in the _left_ dataframe contain NA values and will be kept as such in the final merged dataframe. Here we just wish to verify that the _merge_ itself is not made on NA values.
#### API breaking implications
If the parameter defaults to False, I do not see any breaks.
#### Describe alternatives you've considered
Right now, I'm always doing a pre-check such as :
`assert not df_right.merge_key.isnull().any()`
`df = pd.merge(df_left, df_right, on='merge_key', ...)`
#### Additional context
None
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