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MDExOlB1bGxSZXF1ZXN0NTc2NzQzMDA5
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TYP: create a Frequency alias in _typing
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| 2021-02-21T17:56:15Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Spinning off a piece of #38416
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BUG: "unique" and pd.Series.unique produce different results with aggregation
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| 2021-02-19T22:11:19Z
| 2021-08-15T18:44:25Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 1, 2], "b": [2, 1, 1, 2]})
g = df.groupby("a")
for aggfunc in ["unique", pd.unique, pd.Series.unique]:
try:
print(g.agg({"b": aggfunc}))
except ValueError as e:
print(f"{aggfunc!r} failed with {e!r}")
```
Running this script produces
```
b
a
1 [2, 1]
2 [1, 2]
<function unique at 0x7f6885cc7310> failed with ValueError('Must produce aggregated value')
<function Series.unique at 0x7f688369f8b0> failed with ValueError('Must produce aggregated value')
```
#### Problem description
When using "unique" str as the `aggfunc` parameter, internally it is [interpreted](https://github.com/pandas-dev/pandas/blob/v1.2.2/pandas/core/groupby/generic.py#L241) as `pd.Series.unique`. I expect identical results when passing "unique" and `pd.Series.unique`, or `pd.unique` but they are not.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f904213ffcc36d3ae29c37ae28fbb1844a0769ab
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.11-arch1-1
Version : #1 SMP PREEMPT Wed, 27 Jan 2021 13:53:16 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+772.gf904213ffc
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU4MTI0Mjg1OTM=
| 39,921
|
ENH: get_rename_function: add support for __getitem__ override
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[] | null | 5
| 2021-02-19T23:25:35Z
| 2021-08-15T18:46:24Z
| 2021-08-15T18:46:18Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to be able to use a subclass of UserDict with custom `__getitem__` implementation as a mapper for my dataframe columns. My use case is that I want to have a mapper with default remapping rules when the key is not present.
Unfortunately, as the implementation checks for membership of the key before actually calling `__getitem__`, I am only able to do the behaviour by wrapping the UserDict with a lambda.
Current implementation of get_rename_function in pandas:
```
def get_rename_function(mapper):
"""
Returns a function that will map names/labels, dependent if mapper
is a dict, Series or just a function.
"""
if isinstance(mapper, (abc.Mapping, ABCSeries)):
def f(x):
if x in mapper:
return mapper[x]
else:
return x
else:
f = mapper
return f
```
My proposition is the following:
```
def get_rename_function(mapper):
"""
Returns a function that will map names/labels, dependent if mapper
is a dict, Series or just a function.
"""
if isinstance(mapper, (abc.Mapping, ABCSeries)):
def f(k):
return mapper.get(k, k)
else:
f = mapper
return f
```
which functionally should result in the same behaviour.
The advantage for me is that `__getitem__` is called and I can process k to return my default mapping.
I don't know however if this would have a significant performance impact. If so, alternatively I'd propose the following:
```
def get_rename_function(mapper):
"""
Returns a function that will map names/labels, dependent if mapper
is a dict, Series or just a function.
"""
if not callable(mapper) and isinstance(mapper, (abc.Mapping, ABCSeries)):
def f(x):
if x in mapper:
return mapper[x]
else:
return x
else:
f = mapper
return f
```
Then I could implement the `__call__` method. The downside however is that this could be a breaking change for some people that pass a subclass of a Mapping as well.
What do you think?
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BENCH: collect isin asvs
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MEMBER
| null |
Pushes these as far towards parametrization as I can get them. We end up adding a few cases, dropping none.
Decides on a policy to benchmark Series.isin and leave the others alone (see `algos/__init__.py`)
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MDExOlB1bGxSZXF1ZXN0NTc2ODU3NDg2
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REF: De-duplicate ExtensionIndex._validate_fill_value
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| 0
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| 2021-02-21T17:29:14Z
| 2021-02-21T17:26:47Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDU6SXNzdWU4MTI1MDkwODU=
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Assigning a row using `DataFrame.loc` sometimes fails to preserve type of data
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[] | null | 2
| 2021-02-20T04:11:15Z
| 2021-02-20T14:30:22Z
| 2021-02-20T14:30:22Z
|
NONE
| null |
When assigning a singleton row to a DataFrame with integer type using an `Index` and column, the stored row's `dtype` is sometimes converted to `float64`.
Minimum reproducing example:
```
import pandas as pd
import numpy as np
print(pd.__version__)
print()
df1 = pd.DataFrame({'bar': [0, 1]}) # Initializing with at least one row seems necessary to reproduce
df1.loc[df1.index[:], 'foo'] = np.array([0, 1], dtype=np.int16)
print(df1['foo'])
print()
df2 = pd.DataFrame({'bar': [0]})
df2.loc[df2.index[:], 'foo'] = np.array([0], dtype=np.int16)
print(df2['foo'])
print()
```
Output:
```
1.2.2
0 0
1 1
Name: foo, dtype: int16
0 0.0
Name: foo, dtype: float64
```
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Updated read_html to add option
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[] | null | 5
| 2021-02-20T04:41:01Z
| 2021-06-08T18:50:20Z
| 2021-06-08T18:50:19Z
|
NONE
| null |
Adds optional boolean parameter "remove_whitespace" to skip the remove_whitespace functionality. Defaults to true to support backwards compatibility. See https://github.com/pandas-dev/pandas/issues/24766
- [ x ] closes #24766
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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|
BUG: fillna(0) does not fill NaNs for Float64Arrays
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[] | null | 11
| 2021-02-20T05:14:07Z
| 2021-09-17T09:39:31Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
f = pandas.DataFrame(data = [[0, 0]], columns = ['A', 'B']).astype('Int64')
f['C'] = f['A'] / f['B']
f.fillna(0)
```
Output:
```
A B C
0 0 0 NaN
```
#### Problem description
fillna does not work as expected. It do not fill na
#### Expected Output
```
A B C
0 0 0 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.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 : 1.2.2
numpy : 1.18.3
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.16.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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BUG: Cannot sample on DataFrameGroupBy with weights when index is specified
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| 0
| 2021-02-20T07:49:37Z
| 2021-02-24T17:58:05Z
| 2021-02-24T17:58:05Z
|
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({'a': [3, 5, 2, 7, 1, 2, 4, 6, 2, 4],
'b': [8, 3, 4, 1, 8, 2, 2, 2, 2, 3],
'c': [1, 8, 8, 5, 3, 5, 0, 0, 5, 4],
'd': [9, 7, 6, 3, 6, 3, 2, 1, 5, 8]}, index=list(range(10, 20)))
df1.groupby('c').sample(1, weights=df1['d'].to_numpy())
```
#### Problem description
Sampling ought to work but exception is reported with error stack below:
```
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-4-1fc31a504740>", line 1, in <module>
df1.groupby('c').sample(1, weights=df1['d'].to_numpy())
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 3024, in sample
ws = [weights[idx] for idx in self.indices.values()]
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 3024, in <listcomp>
ws = [weights[idx] for idx in self.indices.values()]
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/series.py", line 875, in __getitem__
return self._get_with(key)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/series.py", line 910, in _get_with
return self.loc[key]
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 895, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1113, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1053, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1266, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1308, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Int64Index([6, 7], dtype='int64')] are in the [index]"
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 1.2.2
numpy : 1.19.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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MDU6SXNzdWU4MTI1NTAzMTQ=
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BUG: Sampling over selected groupbys does not reflect the selection
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| 1
| 2021-02-20T08:20:45Z
| 2021-02-27T19:10:19Z
| 2021-02-27T19:10:19Z
|
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({'a': [3, 5, 2, 7, 1, 2, 4, 6, 2, 4],
'b': [8, 3, 4, 1, 8, 2, 2, 2, 2, 3],
'c': [1, 8, 8, 5, 3, 5, 0, 0, 5, 4],
'd': [9, 7, 6, 3, 6, 3, 2, 1, 5, 8]}, index=list(range(10, 20)))
>>> # selecting one single column works
>>> df1.groupby('c').b.sample(1)
7 2
0 8
4 8
9 3
3 1
2 4
Name: b, dtype: int64
>>> # selecting multiple columns does not work
>>> df1.groupby('c')[['a', 'd']].sample(1)
a b c d
7 6 2 0 1
0 3 8 1 9
4 1 8 3 6
9 4 3 4 8
8 2 2 5 5
2 2 4 8 6
```
#### Problem description
When ``['a', 'd']`` is selected, the result should only contain column ``a`` and ``d``. However, this selection has no effect on the result produced by `groupby-sample`.
#### Expected Output
```python
>>> df1.groupby('c')[['a', 'd']].sample(1)
a d
7 6 1
0 3 9
4 1 6
9 4 8
8 2 5
2 2 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 1.2.2
numpy : 1.19.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
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ENH: Want a new feature in merge_asof function, which includes the all the rows of the right data frame with duplicate values of the coulumn on which it is matched , in the merged dataframe
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[] | null | 1
| 2021-02-20T09:22:56Z
| 2021-08-15T18:47:31Z
| null |
NONE
| null |
#### I want a new feature in merge_asof function of pandas?
I wish I could keep all the rows of the right data frame after merging if that row has duplicates values in the "on" coulumns which I am matching
#### I have given an example as a code in the code block
I have medical data, which has two data frames, one is the radiology scan or any other medical report and the other one is the OPD visit of the patient where the doctor makes the diagnosis or writes comments. The OPD data frame has multiple visits from one patient in a calendar year but has only one radiology_scan or medical report. The visit year might or might not be same, hence I am using merge_asof function to match the radiology visit with the nearest OPD visit. If the patient had multiple OPD visits in the same year as the scan year in radiology sheet, I want to preserve all the OPD visits of that year for that patient in the merged sheet. In merge_asof I am only able to get the first OPD visit form the patient , even if there are multiple in that year which matches the scan year or is near to it .
#### Can you add any parameter in the function so that we can use the above feature
#### I have used merge function
Merge function keeps all the data of the visit year if there are multiple visits in the matched year, but I cannot asof in it
```python
# import pandas as pd
radiology_scan = pd.DataFrame({"patient_id":['a001','a002','a003'],
"Visit_year": [2010, 2012, 2016], "scan_report": [1, 55,2]})
OPD_data = pd.DataFrame({"patient_id":['a001','a001','a002','a003','a004','a005'],
"Visit_year": [2010 , 2010 , 2013, 2017, 2017,2018],
"doctor_comments": ['diagnosis normal, xyz symptoms',
'diagnosis normal, abc symptoms',
'diagnosis abnormal, pqr symptoms',
'diagnosis abnormal, apq symptoms',
'diagnosis normal, xzy symptoms',
'diagnosis abnormal, yzx symptoms' ]})
# here the patient a001 has two visits in opd data frame, when i merge it I want both the visits data, to be reflected
# in the merged dataframe
x = pd.merge_asof(radiology_scan, OPD_data, on = ["Visit_year"], by = ["patient_id"] , direction="nearest")
# when using merge_asof only the first visit of the a001's OPD is seen in the merged data frame
y = pd.merge(radiology_scan, OPD_data, on =["Visit_year","patient_id" ], how = 'left' )
# merge function gives both visits of the matched visit year , but i cannot merge the nearest visits of other patients whose
# visit years from both the data frames don't match ( as in merge_asof )
```
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MDExOlB1bGxSZXF1ZXN0NTc2OTI5ODc3
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ENH: add a gradient map to background gradient
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| 6
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| 2021-04-09T05:28:01Z
| 2021-04-08T22:16:44Z
|
CONTRIBUTOR
| null |
closes #22727
closes #28901
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
A very minor extension to the code and adding one argument gives the function a lot more scope:
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BUG: Index alignment behaviour
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| 2021-02-22T22:59:23Z
| 2021-02-22T22:59:23Z
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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
```python
np.random.seed(1)
df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), columns=['A', 'B'])
>>> df
A B
0 37 12
1 72 9
2 75 5
3 79 64
4 16 1
5 76 71
6 6 25
7 50 20
8 18 84
9 11 28
mask = df['A'] >= 40
>>> df[mask]
A B
1 72 9
2 75 5
3 79 64
5 76 71
7 50 20
>>> df[mask].sort_values('A')
A B
7 50 20
1 72 9
2 75 5
5 76 71
3 79 64
```
### CASE 1: Assignment with `.loc`
No problem here this is expected behaviour as pandas align data on indices.
```python
df.loc[mask] = df[mask].sort_values('A')
>>> df[mask]
A B
1 72 9
2 75 5
3 79 64
5 76 71
7 50 20
```
### CASE 2: Assignment without `.loc`
**The problem** is here. In pandas version `1.2.x` assignment without using `.loc` no longer respects `index` alignment behaviour which was previously respected in version `1.1.x`.
```python
df[mask] = df[mask].sort_values('A')
>>> df[mask]
A B
1 50 20
2 72 9
3 75 5
5 76 71
7 79 64
```
#### Problem description
Lets say we want to sort the values of the slice of the dataframe and assign the sorted values back to the original dataframe in-place.
- In `CASE 1` when using assignment with `.loc` **no effect of sorting the slice is observed in original dataframe which is *expected as pandas align data on indices***.
- In `CASE 2` when using assignment without `.loc` **the assignment operation no longer respects the alignment of indices** which is unexpected behaviour in pandas version `1.2.x`
Prior to pandas version `1.2.x` lets say in pandas version `1.1.5` the assignment operation even without using the `loc` respected the index alignment behaviour.
#### Expected Output
In `CASE 2` the expected output should be:
```python
>>> df[mask]
A B
1 72 9
2 75 5
3 79 64
5 76 71
7 50 20
```
As the assignment operation should respect the pandas alignment of indices behaviour.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-44-generic
Version : #50-Ubuntu SMP Tue Feb 9 06:29:41 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 1.2.2
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 44.0.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.5
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : None
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.22
tables : None
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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DOC: updated wrong type hint for pandas.Series.droplevel
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| 1
| 2021-02-20T13:29:25Z
| 2021-02-20T18:16:27Z
| 2021-02-20T18:16:15Z
|
CONTRIBUTOR
| null |
- [x] closes #39907
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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Grammatically update the readme file
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| 2021-02-20T13:47:14Z
| 2021-02-20T14:09:00Z
| 2021-02-20T14:09:00Z
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NONE
| null |
Small grammatical update the readme file
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BUG: DataFrame.agg and apply with 'size' returns a scalar
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| 0
| 2021-02-20T14:05:07Z
| 2021-02-24T16:32:19Z
| 2021-02-24T16:32:19Z
|
MEMBER
| null |
Compare
```
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
print(df.apply('size'))
print(df.apply(['size']))
print('---')
print(df.agg('size'))
print(df.agg(['size']))
```
gives
```
6
a b
size 3 3
---
6
a b
size 3 3
```
agg/apply should operate on rows/columns.
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MDExOlB1bGxSZXF1ZXN0NTc2OTQ4OTIw
| 39,935
|
BUG: DataFrame.agg and apply with 'size' returns a scalar
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| 7
| 2021-02-20T14:18:49Z
| 2021-06-16T16:07:41Z
| 2021-02-24T16:32:19Z
|
MEMBER
| null |
- [x] closes #39934
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
|
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ENH: Create script to set up a development environment using docker
|
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[] | null | 17
| 2021-02-20T14:37:09Z
| 2021-09-08T08:18:14Z
| 2021-09-08T02:41:40Z
|
CONTRIBUTOR
| null |
This script will help the contributors with the process of setting up a docker container. So instead of manually passing the Github username in the Dockerfile and then running a couple of commands, you can run only this:
```
./docker.sh
```
...and enter the Github username when prompted.
I think this can be good, right? Especially for the new users.
|
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|
ENH: Add function to create DateOffset objects from offset aliases
|
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| 2021-02-20T18:24:56Z
|
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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ENH: Ability to use "normalize" function (df['col'].value_counts(normalize=True) when you do df.groupby(cols).size()
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| 2021-02-20T16:20:36Z
| 2021-03-07T21:14:30Z
| 2021-03-07T21:14:30Z
|
NONE
| null |
`groupby.size()` should have the ability to "normalize" the results and return them as a percentage.
```
df['col'].value_counts(normalize=True)
A = 0.25
B = 0.25
C = 0.25
D = 0.25
```
To accomplish this result with a `groupby.size()` you have to do the following -
```
df2 = df.groupby(['subset_product', 'subset_close']).size().reset_index(name='prod_count')
a = df2.groupby('subset_product')['prod_count'].transform('sum')
df2['prod_count'] = df2['prod_count'].div(a)
```
Is there a way to add a feature to include this as a parameter in `.size()`
|
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REF: share _maybe_coerce_values
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| 2021-02-22T17:57:04Z
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Gets us within spittting distance of combining DTBlock/TDBlock (and not far from DT64TZBlock too)
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- [x] closes #26411
- [x] closes #13483
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- [x] whatsnew entry
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DOC: Updated convert_dtype of Series.apply
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| 11
| 2021-02-20T20:29:10Z
| 2021-06-12T10:22:48Z
| 2021-04-25T17:54:03Z
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CONTRIBUTOR
| null |
- [x] closes #39580
- [ ] tests added / passed
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- [ ] whatsnew entry
This updates the docs for parameter convert_dtype in pandas.Series.apply and adds dtypes for which the original dtype is kept.
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BUG: format multiple CSS selectors correctly
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| 2021-02-20T20:53:06Z
| 2021-02-27T20:17:14Z
| 2021-02-27T19:44:59Z
|
CONTRIBUTOR
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- [x] closes #34061
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Fixes a bug that doesn't format multiple CSS elements in selectors properly.
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BUG: unary operators on IntegerArray return shared mask creating inconsistencies when assigning null and non-null values
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| 3
| 2021-02-20T21:05:16Z
| 2021-02-23T11:11:35Z
| 2021-02-23T11:11:35Z
|
MEMBER
| 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
>>> s = pd.Series([1, 2, 3], dtype="Int64")
>>> s
0 1
1 2
2 3
dtype: Int64
>>>
>>> s2 = -s
>>>
>>> s2
0 -1
1 -2
2 -3
dtype: Int64
>>>
>>> s[0] = None # assigning None
>>>
>>> s
0 <NA>
1 2
2 3
dtype: Int64
>>>
>>> s2
0 <NA> # <--- expected -1
1 -2
2 -3
dtype: Int64
>>>
>>> s[1] = 10 # assigning a value is ok
>>> s
0 <NA>
1 10
2 3
dtype: Int64
>>>
>>> s2
0 <NA>
1 -2
2 -3
dtype: Int64
>>>
>>> s2[2] = None
>>> s2
0 <NA>
1 -2
2 <NA>
dtype: Int64
>>>
>>> s
0 <NA>
1 10
2 <NA> # expected 3
dtype: Int64
>>>
>>> s.values._data is s2.values._data
False
>>>
>>> s.values._mask is s2.values._mask
True
```
#### Problem description
shared mask leads to an inconsistency when assigning a non-null value compared to assigning a null value
also for `__invert__` in BaseMaskedArray so will also affect other nullable arrays
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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MDExOlB1bGxSZXF1ZXN0NTc3MDE1MDU1
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Fix regression for setitem not aligning rhs with boolean indexer
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| 4
| 2021-02-20T22:22:10Z
| 2021-02-22T23:06:13Z
| 2021-02-22T22:59:24Z
|
MEMBER
| null |
- [x] closes #39931
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
reindexing solves the issue here before dispatching to iloc
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Change in astype leads to no longer casting to numpy string dtypes
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| 2021-06-16T13:43:15Z
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|
MEMBER
| null |
- [x] closes #39566
- [x] tests added / passed
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- [x] whatsnew entry
@jreback as discussed we no longer cast to numpy string dtypes after this
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refactor styler tests
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| 1
| 2021-02-20T23:25:44Z
| 2021-02-23T06:29:45Z
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CONTRIBUTOR
| null |
splits `test_style.py` to `style/test_style.py` and `style/test_builtins.py`, with the simple classification of being related to `core` functionality or added `extensions`.
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| 2021-02-21T00:37:40Z
| 2021-02-22T15:44:50Z
| 2021-02-22T15:44:47Z
|
MEMBER
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- [ ] closes #xxxx
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- [ ] whatsnew entry
cc @simonjayhawkins @Dr-Irv there are a couple of kludges (ndarray_obj_2d, const memoryviews), interested in your thoughts on how to handle these
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REF: implement array_algos.take
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TYP: hashing
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MDU6SXNzdWU4MTI4MDI4NTk=
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|
BUG: Value become 0 while exporting (to_excel) the str data
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| 6
| 2021-02-21T08:26:12Z
| 2021-02-21T11:13:22Z
| 2021-02-21T11:13:05Z
|
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 csv
import pandas as pd
output_csv = pd.read_csv(r"D:\test\output.csv")
output_csv.to_excel(r"D:\test\output.xlsx", index=False)
```
#### Problem description
Hi All,
I found some issue while exporting "str" data from some column to excel, And I noticed this issue occurred only after pandas version 1.1.5 and above. And attached my sample of data for your reference. Kindly help to check. Thanks
[files.zip](https://github.com/pandas-dev/pandas/files/6017378/files.zip)
#### output.csv
Col1,Col2
a1,50060e8007123400
a2,50060e8007123400
a3,50060e8007123400
a4,50060e8007123402
a5,50060e8007123402
a6,50060e8007123402
a7,50060e8007123402
a8,50060e8007123402
a9,50060e8007123404
a10,50060e8007123404
#### output.xlsx -> Current output
Col1 | Col2
-- | --
a1 | 0
a2 | 0
a3 | 0
a4 | 0
a5 | 0
a6 | 0
a7 | 0
a8 | 0
a9 | 0
a10 | 0
#### Expected Output
Col1 | Col2
-- | --
a1 | 50060e8007123400
a2 | 50060e8007123400
a3 | 50060e8007123400
a4 | 50060e8007123402
a5 | 50060e8007123402
a6 | 50060e8007123402
a7 | 50060e8007123402
a8 | 50060e8007123402
a9 | 50060e8007123404
a10 | 50060e8007123404
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
setuptools : 51.1.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU4MTI4MDc2ODc=
| 39,951
|
BUG: `to_html()` contains deprecated and poorly supported HTML
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open
| false
| null |
[] | null | 2
| 2021-02-21T08:58:37Z
| 2021-02-23T22:02:12Z
| null |
CONTRIBUTOR
| null |
```
df = pd.DataFrame([[1,2],[3,4]], index=pd.MultiIndex.from_product([['a'], [1,2]]), columns=pd.MultiIndex.from_product([['b'], [3,4]]))
df.to_html().split('\n')
['<table border="1" class="dataframe">',
' <thead>',
' <tr>',
' <th></th>',
' <th></th>',
' <th colspan="2" halign="left">b</th>',
' </tr>',
' <tr>',
' <th></th>',
' <th></th>',
' <th>3</th>',
' <th>4</th>',
' </tr>',
' </thead>',
' <tbody>',
' <tr>',
' <th rowspan="2" valign="top">a</th>',
' <th>1</th>',
' <td>1</td>',
' <td>2</td>',
' </tr>',
' <tr>',
' <th>2</th>',
' <td>3</td>',
' <td>4</td>',
' </tr>',
' </tbody>',
'</table>']
```
`valign` is a deprecated HTML attribute and `halign` is reported to be poorly supported even when it was not deprecated.
CSS solutions are recommended, but inline css like 'style="text-align:center;"' is not recommended as inline styles can break the more secure website CSPs (content security policies).
See here..
https://developer.mozilla.org/en-US/docs/Web/HTML/Element/td
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MDExOlB1bGxSZXF1ZXN0NTc3MDk0OTI1
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|
PERF: Styler render improvement by limiting .join()
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| 1
| 2021-02-21T11:27:43Z
| 2021-03-30T05:05:11Z
| 2021-02-21T18:03:47Z
|
CONTRIBUTOR
| null |
Currently
`<th>` only have attributes when there is a row or column span.
`<td>` never have attributes currently.
So there is no need for repititive `join()` operations, especially inside the jinja2 template. Could be preconfigured outside.
We can get around 10-15% better render times.
```
[ 75.00%] ··· io.style.RenderApply.time_render ok
[ 75.00%] ··· ====== ============ ============
-- rows
------ -------------------------
cols 12 120
====== ============ ============
12 11.6±0.5ms 77.0±0.5ms
24 29.2±0.5ms 144±1ms
36 43.7±0.9ms 209±2ms
====== ============ ============
```
versus master
```
[100.00%] ··· io.style.RenderApply.time_render ok
[100.00%] ··· ====== ============ ==========
-- rows
------ -----------------------
cols 12 120
====== ============ ==========
12 12.9±0.4ms 89.8±4ms
24 31.4±0.9ms 166±2ms
36 46.5±2ms 250±40ms
====== ============ ==========
```
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PERF: avoid object conversion in fillna(method=pad|backfill) for masked arrays
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| 3
| 2021-02-21T12:19:42Z
| 2021-03-06T09:55:11Z
| 2021-03-05T19:00:20Z
|
MEMBER
| null |
```
[ 75.00%] ··· frame_methods.Fillna.time_frame_fillna ok
[ 75.00%] ··· ========= ======== ============ ============= ========== ============= =============
-- dtype
------------------ -----------------------------------------------------------------
inplace method float64 float32 object Int64 Float64
========= ======== ============ ============= ========== ============= =============
True pad 3.44±0.3ms 2.64±0.2ms 307±10ms 3.97±0.2ms 3.72±0.1ms
True bfill 3.23±0.2ms 2.89±0.2ms 275±8ms 2.34±0.1ms 2.45±0.09ms
False pad 3.62±0.1ms 2.06±0.01ms 181±3ms 4.23±0.07ms 4.16±0.07ms
False bfill 3.59±0.2ms 2.06±0.02ms 186±6ms 4.35±0.09ms 4.18±0.06ms
========= ======== ============ ============= ========== ============= =============
```
```
before after ratio
[2e5c28fc] [d3ae0cf1]
<master> <BaseMaskedArray.fillna>
- 2.28±0.06ms 2.06±0.02ms 0.91 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'float32')
- 15.5±0.2ms 2.45±0.09ms 0.16 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Float64')
- 16.5±0.2ms 2.34±0.1ms 0.14 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Int64')
- 85.2±0.9ms 4.18±0.06ms 0.05 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Float64')
- 86.6±0.6ms 4.16±0.07ms 0.05 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Float64')
- 90.9±0.8ms 4.35±0.09ms 0.05 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Int64')
- 92.4±0.4ms 4.23±0.07ms 0.05 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Int64')
- 85.3±1ms 3.72±0.1ms 0.04 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Float64')
- 92.5±2ms 3.97±0.2ms 0.04 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Int64')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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REF: Move the rest of Series.agg into apply
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| 1
| 2021-02-21T12:34:19Z
| 2021-02-21T17:37:28Z
| 2021-02-21T17:32:11Z
|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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MDExOlB1bGxSZXF1ZXN0NTc3MTEwMjA0
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REF: Move the rest of DataFrame.agg into apply
|
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MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run
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MDU6SXNzdWU4MTI4NTQyMjQ=
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|
DOC: Serialization recommendation is deprecated
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| 12
| 2021-02-21T13:19:10Z
| 2021-09-29T10:33:32Z
| 2021-06-11T11:18:47Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/user_guide/io.html#io-msgpack
#### Documentation problem
Since the deprecation of msgpack for on-the-wire transmission, it is recommended to use pyarrow serialization/deserialization. However, since pyarrow 2.0, this [has been deprecated](https://arrow.apache.org/docs/python/ipc.html#arbitrary-object-serialization) for arbitrary objects. A deprecation message is emitted when using the documented code snippet.
```
$ python
Python 3.8.5 (default, Jul 28 2020, 12:59:40)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pyarrow as pa
>>> pa.__version__
'3.0.0'
>>> pa.default_serialization_context()
<stdin>:1: DeprecationWarning: 'pyarrow.default_serialization_context' is deprecated as of 2.0.0 and will be removed in a future version. Use pickle or the pyarrow IPC functionality instead.
<pyarrow.lib.SerializationContext object at 0x7f23dfa36940>
>>>
```
#### Suggested fix for documentation
Would pickle be next in line for a recommended on-the-wire format?
|
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MDExOlB1bGxSZXF1ZXN0NTc3MTE1NDk1
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REF: Move transform into apply
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| 1
| 2021-02-21T13:53:51Z
| 2021-02-21T17:38:02Z
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|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Cleanups and consolidations to follow.
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DOC: remove unnecessary index.html from the URL
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|
CONTRIBUTOR
| null |
remove unnecessary index.html from the URL
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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TYP: maybe_cast_to_datetime
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MEMBER
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Underlying motivation is to make Block._maybe_coerce_values unnecessary, which will improve `__init__` perf
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CLN: Remove some duplicated tests and parametrize
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MEMBER
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
There were a few duplicates from the ix removal
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REF: ensure soft_convert_objects returns ArrayLike
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MEMBER
| null |
moves towards making _maybe_coerce_values unnecessary (perf!)
Also removes is_datetime and is_float attrs from Block, unrelated but ended up in the same branch.
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MEMBER
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Part of making Block._maybe_coerce_values unnecessary
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ENH: NDArrayBackedExtensionArray swapaxes, delete-with-axis
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Sits on top of #39880
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- [ ] closes #xxxx
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DOC : Updated the comparison with spreadsheets with GroupBy
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| 2021-06-29T08:14:50Z
| null |
NONE
| null |
# Problem description
For testing data pipelines using pandas I usually use `assert_frame_equal` to compare expected and resulting dataframes. However, in some circumstances (e.g. test dataframes with more than 20 rows/columns and timestamps) the resulting assertion message may not provide enough information to easily identify the difference between expected and resulting dataframe.
Consider the following example with timestamp columns:
```python
import pandas as pd
df_expected = pd.DataFrame(
{
"timestamp_1": pd.date_range(start="2020-01-01 10:00:01", periods=10, freq="d"),
"timestamp_2": pd.date_range(start="2020-05-05 10:00:01", periods=10, freq="d"),
}
)
df_resulting = df_result.copy()
df_resulting.iloc[7, 1] = pd.Timestamp("2020-01-07 15:00:00")
pd.testing.assert_frame_equal(df_resulting, df_expected)
```
The resulting assertion message is the follwing:
```python
AssertionError: numpy array are different
numpy array values are different (10.0 %)
[index]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[left]: [1588672801000000000, 1588759201000000000, 1588845601000000000, 1588932001000000000, 1589018401000000000, 1589104801000000000, 1589191201000000000, 1578409200000000000, 1589364001000000000, 1589450401000000000]
[right]: [1588672801000000000, 1588759201000000000, 1588845601000000000, 1588932001000000000, 1589018401000000000, 1589104801000000000, 1589191201000000000, 1589277601000000000, 1589364001000000000, 1589450401000000000]
```
It is already hard to spot the actual difference even though we have only 2 columns with 10 rows. I don't know the column name and the affected indices.
# Proposal
The assertion message could include the name of the column and differing indices, like:
```python
AssertionError: numpy array are different
numpy array values are different (10.0 %)
[column]: timestamp_2
[affected_indices]: [7]
[index]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[left]: [1588672801000000000, 1588759201000000000, 1588845601000000000, 1588932001000000000, 1589018401000000000, 1589104801000000000, 1589191201000000000, 1578409200000000000, 1589364001000000000, 1589450401000000000]
[right]: [1588672801000000000, 1588759201000000000, 1588845601000000000, 1588932001000000000, 1589018401000000000, 1589104801000000000, 1589191201000000000, 1589277601000000000, 1589364001000000000, 1589450401000000000]
```
I can find the differences by using some additional boilerplate code however it would be more convenient to have such assertion information right away.
### API breaking implications
There should be no breaking API changes. However, `assert_extension_array_equal` and `assert_numpy_array_equal` may need an additional keyword argument in order to pass the column name.
### Describe alternatives you've considered
Currently, I use the following helper function to see the actual differences:
```python
import pandas as pd
import numpy as np
def identify_differences(df_left: pd.DataFrame, df_right: pd.DataFrame) -> pd.DataFrame:
"""Provide indices, column names and differing values for left and
right dataframe. Assumes that dataframes have same shape, indicies
and columns.
Parameters
----------
df_left: pd.DataFrame
Left dataframe used for comparison.
df_right: pd.DataFrame
Right dataframe used for comparison.
Returns
-------
df_differences: pd.DataFrame
DataFrame containing indices, column names and differing values
of left and right dataframe.
"""
mask = df_left.ne(df_right)
indices, columns = np.where(mask)
differences = [
{
"index": df.index[idx],
"column": df.columns[col],
"left": df_left.iloc[idx, col],
"right": df_right.iloc[idx, col],
}
for idx, col in zip(indices, columns)
]
return pd.DataFrame(differences)
```
This works nicely but I'd rather have the information in the assertion message already.
---
If agreed, I could provide a PR myself. Thanks for looking at it anyway.
|
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Backport PR #39966 on branch 1.2.x (DOC: fix link in 1.2.3 release notes)
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NONE
| null |
Backport PR #39966: DOC: fix link in 1.2.3 release notes
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DOC: fix a warning using obsolete package
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| 2021-02-22T21:54:35Z
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|
CONTRIBUTOR
| null |
currently there is a warning:
"/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/IPython/html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
warn("The `IPython.html` package has been deprecated since IPython 4.0. ""
this change fixes the warning.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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[BUG] - Can't build pandas with msvc compiler (on Windows)
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| 2021-02-22T15:36:30Z
| 2021-02-24T07:41:08Z
| 2021-02-24T07:41:08Z
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NONE
| null |
Building pandas on Windows gives the following errors:
pandas/__libs/src/parser/tokenizer.c(464): error C2146: syntax error: missing ')' before identifier 'PRIu64'
It seems like PRIu64 is used in src/parser/tokenizer.c, but the header defining PRIu64 (inttypes.h) is not included. The library includes <stdint.h> through src/headers/stdint.h, but not inttypes.h which defines PRIu64, and also includes stdint.h
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REGR: Fix assignment bug for unary operators
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| 6
| 2021-02-22T16:11:16Z
| 2021-02-27T12:20:07Z
| 2021-02-23T11:07:54Z
|
MEMBER
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/39943
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
If there was somehow a way to copy the mask only on write so we aren't always copying that may be better. @simonjayhawkins I put this in the `IntegerArray` tests even though we're technically checking a `Series`, let me know if you'd prefer I rewrite or move elsewhere.
|
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| 813,693,582
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MDExOlB1bGxSZXF1ZXN0NTc3Nzk5NTEw
| 39,972
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PERF: halve render time in Styler with itertuples
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| 3
| 2021-02-22T17:12:44Z
| 2021-03-30T05:04:59Z
| 2021-02-23T13:26:50Z
|
CONTRIBUTOR
| null |
```
before after ratio
[dfb92d0f] [e5c2790b]
<master> <perf_itertups>
- 149±1ms 74.4±2ms 0.50 io.style.RenderApply.time_render(24, 120)
- 31.9±1ms 15.9±0.8ms 0.50 io.style.RenderApply.time_render(24, 12)
- 86.3±20ms 42.3±1ms 0.49 io.style.RenderApply.time_render(12, 120)
- 12.2±0.2ms 5.93±0.2ms 0.49 io.style.RenderApply.time_render(12, 12)
- 44.8±0.5ms 21.7±0.3ms 0.48 io.style.RenderApply.time_render(36, 12)
- 227±2ms 99.9±1ms 0.44 io.style.RenderApply.time_render(36, 120)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
FYI `Styler` now faster than `to_html`, even without any function stripping...
```
df = pd.DataFrame(np.random.rand(10000,10))
%timeit df.to_html()
1.9 s ± 63.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit df.style.render()
1.71 s ± 14.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
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MDExOlB1bGxSZXF1ZXN0NTc3ODE4MDg5
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DOC: fix a mis-formatted external link
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| 3
| 2021-02-22T17:40:51Z
| 2021-02-23T01:09:04Z
| 2021-02-22T23:54:31Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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|
update pyperclip link
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| 3
| 2021-02-22T19:46:44Z
| 2021-02-23T05:31:59Z
| 2021-02-22T23:51:51Z
|
CONTRIBUTOR
| null |
looks like the link is outdated. updated it.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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| 813,842,464
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MDExOlB1bGxSZXF1ZXN0NTc3OTIyMTM3
| 39,975
|
BUG: Incomplete Styler copy methods fix (#39708)
|
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| 2021-03-05T08:42:48Z
| 2021-03-05T00:52:34Z
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CONTRIBUTOR
| null |
- [x] closes #39708
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I haven't made so many pull requests before, so I hope this is going into the right direction.
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MDExOlB1bGxSZXF1ZXN0NTc3OTM4MzIz
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TST: collect tests by method
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| 2021-02-22T21:03:48Z
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| null |
- [ ] closes #xxxx
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- [ ] whatsnew entry
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|
BUG: numpy.datetime64 unit counts ignored
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| 2021-02-22T21:13:37Z
| 2021-06-06T20:53:41Z
| 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.
---
```python
import numpy as np
import pandas as pd
pd.Timestamp(np.datetime64('2000-01-01', '10ns')) == pd.Timestamp('2000-01-01')
```
Should be ```True```, is instead ```False```. The problem is that the unit count of 10 is ignored. Compare:
```python
pd.Timestamp(np.datetime64('2000-01-01', '10ns')).asm8.item()
pd.Timestamp(np.datetime64('2000-01-01', '1ns')).asm8.item()
```
The bug occurs in two places, both in `_libs/tslibs/conversion.pyx`. Both `get_datetime64_nanos` and `ensure_datetime64ns` forget to check the count. I don't know a Cython API to get the count, though it is accessible from `numpy.datetime_data`.
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MDExOlB1bGxSZXF1ZXN0NTc3OTQ5OTMw
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DOC: fix template substitution in pad/bfill docstrings
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| 2021-02-22T21:23:47Z
| 2021-02-24T15:08:24Z
| 2021-02-24T13:36:09Z
|
CONTRIBUTOR
| null |
- [ ] closes #39640
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
#39640 Added doc tag above ffill() to substitute klass
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Backport PR #39944 on branch 1.2.x (Fix regression for setitem not aligning rhs with boolean indexer)
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NONE
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Backport PR #39944: Fix regression for setitem not aligning rhs with boolean indexer
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TST: collect indexing tests by method
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NONE
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Backport PR #39974: update pyperclip link
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NONE
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Backport PR #39973: DOC: fix a mis-formatted external link
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Deprecate: `level` parameter for aggregations in DataFrame and Series
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| 12
| 2021-02-23T00:24:37Z
| 2021-09-10T02:19:32Z
| 2021-04-13T11:53:28Z
|
CONTRIBUTOR
| null |
For `sum`, `prod`, `count`, etc. there is a parameter `level` which is internally rewritten into `groupby`.
I propose that the `level` parameter for these aggregations be deprecated and instead recommend to users that they use the `groupby` syntax.
The functions that would be affected are:
* any
* all
* count
* sum
* prod
* max
* min
* mean
* median
* skew
* kurt
* sem
* var
* std
* mad
I believe this list is comprehensive.
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MDU6SXNzdWU4MTQwNDY0MzE=
| 39,984
|
BUG: Unable to create a MultiIndex with `nan` values in nullable `Float` dtypes
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[] | null | 0
| 2021-02-23T02:44:21Z
| 2021-07-01T21:14:39Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> df = pd.DataFrame({'a':pd.Series([1, 2, None], dtype='Int64'), 'b':pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None]))})
>>> df
a b
0 1 0.2
1 2 NaN
2 <NA> <NA>
>>> pd.MultiIndex.from_frame(df)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 662, in from_frame
return cls.from_arrays(columns, sortorder=sortorder, names=names)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 462, in from_arrays
codes, levels = factorize_from_iterables(arrays)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 2632, in factorize_from_iterables
return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 2632, in <genexpr>
return map(list, zip(*(factorize_from_iterable(it) for it in iterables)))
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 2604, in factorize_from_iterable
cat = Categorical(values, ordered=False)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 359, in __init__
dtype = CategoricalDtype(categories, dtype.ordered)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/dtypes/dtypes.py", line 160, in __init__
self._finalize(categories, ordered, fastpath=False)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/dtypes/dtypes.py", line 314, in _finalize
categories = self.validate_categories(categories, fastpath=fastpath)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/dtypes/dtypes.py", line 508, in validate_categories
raise ValueError("Categorical categories cannot be null")
ValueError: Categorical categories cannot be null
```
#### Problem description
Should `nan` be allowed in the first place while creating a nullable floating array? If yes, then we have an issue while creating a mulit-index from the dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
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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MDU6SXNzdWU4MTQwODEwMTE=
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BUG: to_json fails writing to GCS with compression
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| 9
| 2021-02-23T04:07:53Z
| 2021-02-26T00:12:38Z
| 2021-02-24T18:05:39Z
|
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
df = pd.DataFrame({'numbers': list(range(1, 10))})
df.to_json('gcs://test-bucket/test.json.gz')
```
#### Problem description
Error writing compressed stream using gcs. Removing compression works fine.
```
import pandas as pd...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/workspaces/charybdis/test_pg.py in
254
255 df = pd.DataFrame({'numbers': list(range(1, 10))})
----> 256 df.to_json('gcs://river-categorizer-data-us-central1/test.jsonl.gz')
/workspaces/charybdis/.venv/lib/python3.8/site-packages/pandas/core/generic.py in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options)
2463 indent = indent or 0
2464
-> 2465 return json.to_json(
2466 path_or_buf=path_or_buf,
2467 obj=self,
/workspaces/charybdis/.venv/lib/python3.8/site-packages/pandas/io/json/_json.py in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options)
100 if path_or_buf is not None:
101 # apply compression and byte/text conversion
--> 102 with get_handle(
103 path_or_buf, "wt", compression=compression, storage_options=storage_options
104 ) as handles:
/workspaces/charybdis/.venv/lib/python3.8/site-packages/pandas/io/common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
590 )
591 else:
--> 592 handle = gzip.GzipFile(
593 fileobj=handle, # type: ignore[arg-type]
594 mode=ioargs.mode,
/usr/local/lib/python3.8/gzip.py in __init__(self, filename, mode, compresslevel, fileobj, mtime)
202
203 if self.mode == WRITE:
--> 204 self._write_gzip_header(compresslevel)
205
206 @property
/usr/local/lib/python3.8/gzip.py in _write_gzip_header(self, compresslevel)
230
231 def _write_gzip_header(self, compresslevel):
--> 232 self.fileobj.write(b'\037\213') # magic header
233 self.fileobj.write(b'\010') # compression method
```
#### Expected Output
No error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.121-linuxkit
Version : #1 SMP Tue Dec 1 17:50:32 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
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sort_index not sorting when multi-index made by different categorical types
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| 3
| 2021-02-23T06:45:55Z
| 2021-03-15T00:32:17Z
| 2021-03-15T00:32:13Z
|
CONTRIBUTOR
| null |
- [ ] closes #24271
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
|
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MDU6SXNzdWU4MTQxODczMTQ=
| 39,987
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ENH: `applymap` get kwargs?
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| 1
| 2021-02-23T07:15:06Z
| 2021-03-23T16:11:16Z
| 2021-03-23T16:11:16Z
|
CONTRIBUTOR
| null |
`apply` has `kwargs` whilst `applymap` does not. Is there a specific reason for this?
it's straight forward to do
```
apply(f, **kwarrgs)
```
or
```
g = partial(f **kwargs)
applymap(g)
```
its not documented how this can be achieved with `partial` which the more casual user might miss?
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|
BUG: to_offset doesn't use the compiled regex
|
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| 0
| 2021-02-23T09:52:04Z
| 2021-03-01T13:54:59Z
| 2021-03-01T13:54:59Z
|
CONTRIBUTOR
| null |
- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version 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.
#### Problem description
When checking lines: https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/tslibs/offsets.pyx#L3582
and https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/tslibs/offsets.pyx#L3456
we can see that there is:
```python
opattern = re.compile(
r"([+\-]?\d*|[+\-]?\d*\.\d*)\s*([A-Za-z]+([\-][\dA-Za-z\-]+)?)"
)
```
and
```python
split = re.split(opattern, freq)
```
Which shows there's a compiled regex but it's not used.
#### Expected Output
Instead, it should be:
```python
split = opattern.split(freq)
```
If you ever like this kind of thing...
```bash
>>> timeit.timeit("opattern.split(freq)", setup='''import re;opattern = re.compile(r"([+\-]?\d*|[+\-]?\d*\.\d*)\s*([A-Za-z]+([\-][\dA-Za-z\-]+)?)");freq="1Min"''', number=1000000)
0.6493891229999917
>>> timeit.timeit("re.split(opattern, freq)", setup='''import re;opattern = re.compile(r"([+\-]?\d*|[+\-]?\d*\.\d*)\s*([A-Za-z]+([\-][\dA-Za-z\-]+)?)");freq="1Min"''', number=1000000)
1.9217748190000066
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
Version : Darwin Kernel Version 18.6.0: Thu Apr 25 23:16:27 PDT 2019; root:xnu-4903.261.4~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.1
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTc4MzI4OTE3
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CLN: rename do_integrity_check to verify_integrity in internals
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| 1
| 2021-02-23T10:21:14Z
| 2021-02-24T07:47:19Z
| 2021-02-24T00:37:16Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/39841/
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Backport PR #39971 on branch 1.2.x (REGR: Fix assignment bug for unary operators)
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| 0
| 2021-02-23T11:08:52Z
| 2021-02-23T12:30:27Z
| 2021-02-23T12:30:27Z
|
NONE
| null |
Backport PR #39971: REGR: Fix assignment bug for unary operators
|
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[ArrayManager] DataFrame constructors
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| 6
| 2021-02-23T11:53:03Z
| 2021-03-03T08:22:19Z
| 2021-03-03T08:22:13Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/39146
In this PR, I am trying to ensure what when we construct a DataFrame, we directly construct the proper manager (depending on the option settings) instead of converting at the end of the constructor (as is done now). To that end, I am passing through a keyword to indicate the type of the manager to be created to the different internal construction methods like `arrays_to_mgr` etc.
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STYLE Increase coverage of inconsistent-namespace-usage
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| 23
| 2021-02-23T12:05:06Z
| 2021-03-11T13:47:14Z
| 2021-03-11T13:47:14Z
|
MEMBER
| null |
If you already have experience contributing, then please leave this issue to newcomers - thanks :+1:
---
In `.pre-commit-config.yaml`, the check `inconsistent-namespace-usage` is limited to `pandas/tests/frame/`. Let's expand it to the rest of `pandas/tests/`.
The task here is:
0. make sure you're familiar with the [contributing guide](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html)
1. choose a folder from the list below (e.g. `series`) and add it to the check in `.pre-commit-config.yaml`. E.g., if you chose `series`, that would be:
```yaml
- id: inconsistent-namespace-usage
name: 'Check for inconsistent use of pandas namespace in tests'
entry: python scripts/check_for_inconsistent_pandas_namespace.py
language: python
types: [python]
files: ^pandas/tests/(frame|series)/
```
2. temporarily add `tokenize-rt` as an additional dependency and `--replace` as an arg (these will be removed later):
```yaml
- id: inconsistent-namespace-usage
name: 'Check for inconsistent use of pandas namespace in tests'
entry: python scripts/check_for_inconsistent_pandas_namespace.py
language: python
types: [python]
files: ^pandas/tests/(frame|series)/
additional_dependencies: [tokenize-rt]
args: [--replace]
```
3. run this check on all files with pre-commit, i.e.:
```console
$ pre-commit run inconsistent-namespace-usage --all-files
[INFO] Initializing environment for local:tokenize-rt.
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
Check for inconsistent use of pandas namespace in tests.......................Failed
- hook id: inconsistent-namespace-usage
- files were modified by this hook
```
4. run it again - this time, it should pass
```console
$ pre-commit run inconsistent-namespace-usage --all-files
Check for inconsistent use of pandas namespace in tests.......................Passed
```
Check that the other hooks still pass - if you enable pre-commit (`pre-commit install`), then this will happen automatically when you try to commit your changes (see step 6)
5. revert the changes to `.pre-commit-config.yaml` (we'll update it once these changes are all so as to avoid a barrage of merge conflicts)
6. commit your changes, and open a pull request. Check over your changes, make sure they look sensible, let me know if not
---
Here are the folders in `pandas/tests` which this check needs expanding to:
- [x] series
- [x] scalar
- [x] resample
- [x] util
- [x] tseries
- [x] dtypes
- [x] arrays
- [x] plotting
- [x] tools
- [x] indexing
- [x] strings
- [x] reductions
- [x] frame
- [x] arithmetic
- [x] config
- [x] api
- [x] generic
- [x] io
- [x] base
- [x] window
- [x] internals
- [x] reshape
- [x] computation
- [x] apply
- [x] groupby
- [x] tslibs
- [x] libs
- [x] indexes
- [x] extension
No need to ask for permission to work on this, just leave a comment letting people know which folder(s) from the list above you're working on
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MDU6SXNzdWU4MTQ0Nzg0Mzg=
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|
REF: Avoid special-casing in core.apply
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| 3
| 2021-02-23T13:45:07Z
| 2021-09-25T01:15:03Z
| null |
MEMBER
| null |
Ref https://github.com/pandas-dev/pandas/pull/39935#issuecomment-783654077
Currently in apply, there are many separate places where post-processing logic is applied. Once this is logic has been better consolidated, we can avoid special-casing certain ops (e.g. "size" for DataFrame).
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fix benchmark failure with numpy 1.20+
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| 2
| 2021-02-23T14:59:53Z
| 2021-02-27T10:40:23Z
| 2021-02-26T23:11:43Z
|
MEMBER
| null |
cherry-pick from #36092 in case we don't backport it as this change needs to be backported, follow-up to #39795
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BENCH: skip slowest tzlocal asvs
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| 2021-02-23T15:23:03Z
| 2021-02-27T19:16:04Z
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MEMBER
| null |
Running with `-b tslibs` in master takes 141 minutes. This cuts it down to 36 minutes
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Translation of official documentation into Portuguese
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| 2
| 2021-02-23T15:43:44Z
| 2021-10-21T12:46:46Z
| null |
NONE
| null |
#### Location of the documentation
[https://pandas.pydata.org/pandas-docs/version/1.2.1/]
#### Documentation problem
[There is no Portuguese translation. I would like to translate the official documentation and post it on my blog. And the documentation license is not specified.]
#### Suggested fix for documentation
[The Brazilian panda community is growing. And it would grow faster if it had good documentation centralized in Portuguese.]
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REF: move Block construction in groupby aggregation to internals
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Possible precursor for https://github.com/pandas-dev/pandas/pull/39885
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Remove numpy pin from environment.yml on 1.2.x
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| 3
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MEMBER
| null |
xref #39513, expected to fail until #39994 backported
This PR is directly against 1.2.x and ignores site packages for mypy checks.
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MDExOlB1bGxSZXF1ZXN0NTc4NjA0NTA2
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| 1
| 2021-02-23T17:00:26Z
| 2021-02-23T17:11:37Z
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|
CONTRIBUTOR
| null |
https://github.com/trifacta/vega -> https://github.com/vega/vega
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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CLN: follow-ups
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NTc4NjIwNDI2
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STYLE: Inconsistent namespace - Indexes (#39992)
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| 2021-02-26T16:42:44Z
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [X] whatsnew entry
Don't think tests apply here. By itself, does not close 39992.
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BUG: rolling with time window does not always pick edge correctly
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| 1
| 2021-02-23T18:27:18Z
| 2021-02-24T05:47:33Z
| 2021-02-24T05:26:14Z
|
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
data = pd.Series(range(1, 4), index=pd.date_range(start="2020-01-01", end="2020-01-03", freq="1d")).iloc[::-1]
data.rolling("1d", closed="left").sum()
```
#### Problem description
I'm expecting the result to be
```
2020-01-03 NaN
2020-01-02 3.0
2020-01-01 2.0
Freq: -1D, dtype: float64
```
but instead I get
```
2020-01-03 NaN
2020-01-02 NaN
2020-01-01 NaN
Freq: -1D, dtype: float64
```
This problem doesn't occur if `window` is `int` instead of `offset`, or if index is in ascending order.
For example:
`data.rolling(1, closed="left").sum()` yields
```
2020-01-03 NaN
2020-01-02 3.0
2020-01-01 2.0
Freq: -1D, dtype: float64
```
and
```
data = pd.Series(range(1, 4), index=pd.date_range(start="2020-01-01", end="2020-01-03", freq="1d"))
data.rolling("1d", closed="left").sum()
```
yields
```
2020-01-01 NaN
2020-01-02 1.0
2020-01-03 2.0
Freq: D, dtype: float64
```
#### Expected Output
```
2020-01-03 NaN
2020-01-02 3.0
2020-01-01 2.0
Freq: -1D, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
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 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.1
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : 0.29.21
pytest : 6.2.2
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.15.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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CLN: Remove classes from tests.apply
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| 0
| 2021-02-23T19:35:20Z
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|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
No changes to the tests, other than to remove the unused class structure.
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| 4
| 2021-02-23T21:58:57Z
| 2021-02-25T02:54:30Z
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|
MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Currently there are no tests for dict-like argument with agg when a DataFrame is missing a column (edit: in the apply tests, there are tests in groupby, resample, and window). This PR changes the error slightly from
> f"Column '{k}' does not exist!"
to
> f"Column(s) {cols} do not exist"
in order to make it consistent (and I think more helpful, in the case of multiple columns) with the error message from transform.
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POC: dynamically choose which asvs to run
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| 2021-02-23T22:04:08Z
| 2021-11-20T23:22:54Z
| 2021-03-01T02:02:51Z
|
MEMBER
| null |
The main idea is in trim_asv_paths. Ideally it would be expanded to allow finer-tuning (e.g. #39917 would allow some more)
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|
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| 1
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| 2021-03-04T21:57:21Z
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MEMBER
| null |
- [x] closes #40002
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MDExOlB1bGxSZXF1ZXN0NTc4ODAxNDQx
| 40,007
|
PERF: make DTA/TDA/PA _ndarray the attribute, _data the property
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| 2021-02-23T22:33:37Z
| 2021-02-24T18:34:16Z
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MEMBER
| null |
the difference isnt huge, but we do access _ndarray in a lot of places so it adds up
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MDExOlB1bGxSZXF1ZXN0NTc4ODA5MzY0
| 40,008
|
BUG: overflow in astype(td64ns)
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| 5
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| 2021-07-01T17:30:29Z
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NTc4ODEwMTEx
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|
REF: share check_value_size
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MDExOlB1bGxSZXF1ZXN0NTc4ODUxNTg0
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REGR: compressed to_json with URL-like paths and binary objects
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| 2021-02-24T00:13:12Z
| 2021-06-05T20:50:49Z
| 2021-02-24T18:05:39Z
|
CONTRIBUTOR
| null |
- [ ] closes #39985
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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TST: collect indexing tests by method
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- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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MDExOlB1bGxSZXF1ZXN0NTc4OTgzNzk1
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sanitize_array raise_cast_failure default to True
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MDU6SXNzdWU4MTUzODY5MjU=
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BUG: Issues with return type hint and/or definition of `pandas.core.generic.NDFrame.__hash__`
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| 0
| 2021-02-24T11:25:35Z
| 2021-06-29T12:27:42Z
| 2021-06-29T12:27:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [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
pd.DataFrame().__hash__()
```
#### Problem description
It is already known that calling `NDFrame.__hash__` always raises an exception, and thus `NDFrame` objects are not hashable. My issue is with the return type hint, which is currently `int`:
https://github.com/pandas-dev/pandas/blob/84a9a6511b99bb0e9fe6f570cae7e6888f6f06da/pandas/core/generic.py#L1819-L1823
I believe that the correct return type hint should be `typing.NoReturn`, according to the example given at https://docs.python.org/3/library/typing.html#typing.NoReturn.
From a static type checker point-of-view (e.g. `mypy`), it may be even better to override the attribute as `__hash__ = None`, as this what native Python expects for unhashable objects (see https://github.com/python/cpython/blob/1f433406bd46fbd00b88223ad64daea6bc9eaadc/Lib/_collections_abc.py#L76-L102). Any non-`None` implementation currently produces non-intuitive behaviour; for example, the following passes `mypy` static type checking:
```python
import typing
from pandas.core import frame
def accept_hashable(obj: typing.Hashable):
print(obj)
accept_hashable(frame.DataFrame())
...
Success: no issues found in 1 source file
```
To get behaviour identical to `list` and `dict` objects, it looks like making the change to `NDFrame` and `Series`* to `__hash__ = None` will do; after this change, we can get e.g. in `mypy`:
```python
accept_hashable(frame.DataFrame())
...
error: Argument 1 to "accept_hashable" has incompatible type "DataFrame"; expected "Hashable" [arg-type]
note: Following member(s) of "DataFrame" have conflicts:
note: __hash__: expected "Callable[[], int]", got "None"
```
\**Note: For some reason, `Series` redefines `__hash__` as the same as `NDFrame.__hash__`, even though `Series` inherits from `NDFrame`.*
https://github.com/pandas-dev/pandas/blob/84a9a6511b99bb0e9fe6f570cae7e6888f6f06da/pandas/core/series.py#L268
#### Expected Output
(Not applicable)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-44-generic
Version : #50~20.04.1-Ubuntu SMP Wed Feb 10 21:07:30 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2020.5
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 8.0.0.dev
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU4MTU0MTUzNjk=
| 40,014
|
BUG: "groupby" failed for dataframe with object-type index
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| 6
| 2021-02-24T12:05:28Z
| 2021-04-15T04:16:49Z
| 2021-04-15T04:16:49Z
|
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
I have a dataframe where the index is object type, and I found the error by accident. The following code is a simplified reproduction of the error.
```python
df = pd.DataFrame(
{ "c0": ["x", "x", "x"],
"c1": ["x", "x", "y"],
"p": [0,1,2] } )
df.index = df.index.astype("O")
df.groupby(['c0','c1']).p.agg(lambda x:all(x>0))
```
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-227-54707938a409> in <module>
5
6 df.index = df.index.astype("O")
----> 7 df.groupby(['c0','c1']).p.agg(lambda x:all(x>0))
/opt/conda/lib/python3.8/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
254
255 if self.grouper.nkeys > 1:
--> 256 return self._python_agg_general(func, *args, **kwargs)
257
258 try:
/opt/conda/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in _python_agg_general(self, func, *args, **kwargs)
1171 try:
1172 # if this function is invalid for this dtype, we will ignore it.
-> 1173 result, counts = self.grouper.agg_series(obj, f)
1174 except TypeError:
1175 continue
/opt/conda/lib/python3.8/site-packages/pandas/core/groupby/ops.py in agg_series(self, obj, func)
704
705 try:
--> 706 return self._aggregate_series_fast(obj, func)
707 except ValueError as err:
708 if "Must produce aggregated value" in str(err):
/opt/conda/lib/python3.8/site-packages/pandas/core/groupby/ops.py in _aggregate_series_fast(self, obj, func)
730 group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
731 grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
--> 732 result, counts = grouper.get_result()
733 return result, counts
734
pandas/_libs/reduction.pyx in pandas._libs.reduction.SeriesGrouper.get_result()
pandas/_libs/reduction.pyx in pandas._libs.reduction._BaseGrouper._apply_to_group()
/opt/conda/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in <lambda>(x)
1158 def _python_agg_general(self, func, *args, **kwargs):
1159 func = self._is_builtin_func(func)
-> 1160 f = lambda x: func(x, *args, **kwargs)
1161
1162 # iterate through "columns" ex exclusions to populate output dict
<ipython-input-227-54707938a409> in <lambda>(x)
5
6 df.index = df.index.astype("O")
----> 7 df.groupby(['c0','c1']).p.agg(lambda x:all(x>0))
/opt/conda/lib/python3.8/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
/opt/conda/lib/python3.8/site-packages/pandas/core/arraylike.py in __gt__(self, other)
43 @unpack_zerodim_and_defer("__gt__")
44 def __gt__(self, other):
---> 45 return self._cmp_method(other, operator.gt)
46
47 @unpack_zerodim_and_defer("__ge__")
/opt/conda/lib/python3.8/site-packages/pandas/core/series.py in _cmp_method(self, other, op)
4975 res_values = ops.comparison_op(lvalues, rvalues, op)
4976
-> 4977 return self._construct_result(res_values, name=res_name)
4978
4979 def _logical_method(self, other, op):
/opt/conda/lib/python3.8/site-packages/pandas/core/series.py in _construct_result(self, result, name)
2759 # We do not pass dtype to ensure that the Series constructor
2760 # does inference in the case where `result` has object-dtype.
-> 2761 out = self._constructor(result, index=self.index)
2762 out = out.__finalize__(self)
2763
/opt/conda/lib/python3.8/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
346 try:
347 if len(index) != len(data):
--> 348 raise ValueError(
349 f"Length of passed values is {len(data)}, "
350 f"index implies {len(index)}."
ValueError: Length of passed values is 1, index implies 2.
```
#### Problem description
If the index is not an object type, you will not get this error, so I guess there is something wrong with object type index.
#### Expected Output
```python
c0 c1
x x False
y True
Name: p, dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.121-linuxkit
Version : #1 SMP Tue Dec 1 17:50:32 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : 0.29.21
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.2
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.2.2
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NTc5MjY1OTE1
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BUG: Cannot sample on DataFrameGroupBy with weights when index is specified
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CONTRIBUTOR
| null |
- [x] closes #39927
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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|
DOC: Clarify rank documentation
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Example of top/bottom behaviour vs ascending:
```python
x = np.array([np.nan, 3.1, 1.4, 2.1, np.nan, 5, 0.5])
pd.Series(x).rank(ascending=False, na_option='bottom', method='min') ->
0 6.0
1 2.0
2 4.0
3 3.0
4 6.0
5 1.0
6 5.0
dtype: float64
pd.Series(x).rank(ascending=True, na_option='bottom', method='min') ->
0 6.0
1 4.0
2 2.0
3 3.0
4 6.0
5 5.0
6 1.0
dtype: float64
pd.Series(x).rank(ascending=True, na_option='top', method='min') ->
0 1.0
1 6.0
2 4.0
3 5.0
4 1.0
5 7.0
6 3.0
dtype: float64
pd.Series(x).rank(ascending=False, na_option='top', method='min') ->
0 1.0
1 6.0
2 4.0
3 5.0
4 1.0
5 7.0
6 3.0
dtype: float64
```
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MDExOlB1bGxSZXF1ZXN0NTc5MzA3NTQx
| 40,017
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Backport PR #39978 on branch 1.2.x (DOC: fix template substitution in pad/bfill docstrings)
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| 0
| 2021-02-24T13:36:54Z
| 2021-02-24T16:39:19Z
| 2021-02-24T16:39:19Z
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NONE
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Backport PR #39978: DOC: fix template substitution in pad/bfill docstrings
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BUG: DataFrame/Series.transform with list and non-list dict values
|
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| 3
| 2021-02-24T14:53:11Z
| 2021-02-27T18:18:33Z
| 2021-02-27T18:18:33Z
|
MEMBER
| null |
```
df = pd.DataFrame({'a': [1, 2], 'b': [1, 4], 'c': [1, 4]})
print(df.transform({'b': ['sqrt', 'abs'], 'c': 'sqrt'}))
print(df['a'].transform({'b': ['sqrt', 'abs'], 'c': 'sqrt'}))
```
produces
```
b c
sqrt abs c
0 1.0 1 1.0
1 2.0 4 2.0
b c
sqrt abs a
0 1.000000 1 1.000000
1 1.414214 2 1.414214
```
In each case the last column label on the 2nd level should be "sqrt" and not "a" or "c".
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