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} | 0 | "2021-07-13T20:11:23Z" | "2021-07-14T01:18:16Z" | "2021-07-13T22:54:13Z" | 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
Puts us within spitting distance of making this something EAs can override | {
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} | 4 | "2021-07-13T20:12:45Z" | "2021-07-19T11:48:58Z" | "2021-07-19T11:48:58Z" | MEMBER | null | From the manylinux README
```
The goal of the manylinux project is to provide a convenient way to distribute binary Python extensions as wheels on Linux. This effort has produced PEP 513 (manylinux1), PEP 571 (manylinux2010), PEP 599 (manylinux2014) and PEP 600 (manylinux_x_y).
PEP 513 defined manylinux1_x86_64 and manylinux1_i686 platform tags and the wheels were built on Centos5. Centos5 reached End of Life (EOL) on March 31st, 2017.
PEP 571 defined manylinux2010_x86_64 and manylinux2010_i686 platform tags and the wheels were built on Centos6. Centos6 reached End of Life (EOL) on November 30th, 2020.
PEP 599 defines the following platform tags:
manylinux2014_x86_64
manylinux2014_i686
manylinux2014_aarch64
manylinux2014_armv7l
manylinux2014_ppc64
manylinux2014_ppc64le
manylinux2014_s390x
Wheels are built on CentOS 7 which will reach End of Life (EOL) on June 30th, 2024.
```
Since Centos5 and 6 have reached EOL already, we should bump manylinux to manylinux2014. FWIW, numpy has already bumped manylinux to manylinux2014.
cc @simonjayhawkins | {
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Thanks realead for fixing the tests.
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} | 2 | "2021-07-14T04:56:18Z" | "2021-07-14T23:21:31Z" | "2021-07-14T23:21:27Z" | CONTRIBUTOR | null | - [x] closes #21247
- [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
Whats new:
Add a test to `pandas/tests/indexes/multi/test_reindex.py`
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} | 4 | "2021-07-14T07:14:59Z" | "2021-07-26T14:26:17Z" | "2021-07-26T12:43:02Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
series = df.pop("b")
series[[True, False, False]] = 9
```
#### Problem description
Throws a `KeyError`:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
1061 try:
-> 1062 self._set_with_engine(key, value)
1063 except (KeyError, ValueError):
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)
1094 # fails with AttributeError for IntervalIndex
-> 1095 loc = self.index._engine.get_loc(key)
1096 # error: Argument 1 to "validate_numeric_casting" has incompatible type
~/.envs/sigma/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/.envs/sigma/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
TypeError: '[True, False, False]' is an invalid key
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3360 try:
-> 3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
~/.envs/sigma/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/.envs/sigma/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'b'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
/tmp/ipykernel_29158/1487425742.py in <module>
3 df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
4 series = df.pop("b")
----> 5 series[[True, False, False]] = 9
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
1080 key = np.asarray(key, dtype=bool)
1081 try:
-> 1082 self._where(~key, value, inplace=True)
1083 except InvalidIndexError:
1084 self.iloc[key] = value
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/generic.py in _where(self, cond, other, inplace, axis, level, errors)
8859 new_data = self._mgr.putmask(mask=cond, new=other, align=align)
8860 result = self._constructor(new_data)
-> 8861 return self._update_inplace(result)
8862
8863 else:
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/generic.py in _update_inplace(self, result, verify_is_copy)
4228 self._clear_item_cache()
4229 self._mgr = result._mgr
-> 4230 self._maybe_update_cacher(verify_is_copy=verify_is_copy)
4231
4232 @final
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/series.py in _maybe_update_cacher(self, clear, verify_is_copy)
1232 if len(self) == len(ref):
1233 # otherwise, either self or ref has swapped in new arrays
-> 1234 ref._maybe_cache_changed(cacher[0], self)
1235 else:
1236 # GH#33675 we have swapped in a new array, so parent
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/frame.py in _maybe_cache_changed(self, item, value)
3896 The object has called back to us saying maybe it has changed.
3897 """
-> 3898 loc = self._info_axis.get_loc(item)
3899 arraylike = value._values
3900 self._mgr.iset(loc, arraylike)
~/.envs/sigma/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
-> 3363 raise KeyError(key) from err
3364
3365 if is_scalar(key) and isna(key) and not self.hasnans:
KeyError: 'b'
```
Note that when changing the line `series = df.pop("b")` to `series = df.pop("b").copy()` it works as expected.
#### Expected Output
No exception, first value in `series` set to `9`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.117-58.216.amzn2.x86_64
Version : #1 SMP Tue May 11 20:50:07 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_AU.UTF-8
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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It is depreciated and needs replacement with ax.get_subplotspec().is_first_col().
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More like an improvement.
#### Describe the solution you'd like
I just started playing around with pandas styling, and I noticed there is no way to print only the head and/or tail of a stylized DataFrame. It would be awesome if the styling could be applied to the whole df but then we could print only selected rows, like head and/or tail.
#### API breaking implications
I guess there could be a parameter passed to methods such as `bar`, or a new method to be appended to the end of a call.
#### Describe alternatives you've considered
I can apply the style to a slice of a DataFrame but then the styling function is applied to this slice only. What if I want to highlight_max to the whole df but print head/tail only? | {
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] | closed | false | null | [] | null | 17 | "2021-07-14T16:44:28Z" | "2021-07-19T11:54:28Z" | "2021-07-19T11:54:28Z" | 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.
---
```python
import pandas as pd
s = pd.Series([1,2,3], name="foo")
reveal_type(s.name)
nam: str = s.name
```
mypy then reports:
```
seriesnam.py:5: note: Revealed type is "typing.Hashable*"
seriesnam.py:7: error: Incompatible types in assignment (expression has type "Hashable", variable has type "str") [assignment]
```
It's natural to assume that the names of a Series are `str`
So why do we have it typed as `Hashable` ?
Seems like @simonjayhawkins changed this in https://github.com/pandas-dev/pandas/pull/39107
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xlrd.biffh.XLRDError: Excel xlsx file; not supported
>>>
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] | open | false | null | [] | null | 3 | "2021-07-14T18:55:55Z" | "2021-08-21T04:25:08Z" | null | NONE | null | - [Yes] I have checked that this issue has not already been reported.
- [Yes] I have confirmed this bug exists on the latest version of pandas.
Tested with pandas 1.3.0. Verified the expected output is correct in the previous version, 1.2.5.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
left = ({'name': 'Foo', 'datetime': None, 'some column': 'some value'},)
left = pd.DataFrame(left).set_index(['name', 'datetime'])
right = ({'name': 'Foo', 'datetime': '2021-07-14', 'other column': 'other value'},)
right = pd.DataFrame(right).set_index(['name', 'datetime'])
merged = left.merge(right, how='outer', left_index=True, right_index=True)
print(merged)
```
#### Problem description
When merging left and right together using an OUTER join, I would expect to see two rows in the result. The problem appears to be with the `datetime` index column with `NaN` values. If I provide a date in left, I see the expected output. Without it, any row from the right is dropped, i.e. behaving like a left join.
Actual Output
```
some column other column
name datetime
Foo NaN some value NaN
```
#### Expected Output
```
some column other column
name datetime
Foo NaN some value NaN
2021-07-14 NaN other value
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 41.2.0
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.4
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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} | 5 | "2021-07-14T19:08:53Z" | "2021-08-08T12:40:36Z" | "2021-08-07T23:14:31Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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.style.set_sticky(axis="columns")
```
#### Problem description
When "sticking" columns in a DataFrame as shown above it does not work correctly if the DataFrame has an Multi-index. If the DataFrame has only a single index field then it works correctly. When a Multi-Index or hierarchical index is used, the index columns stick, but the other column headers disappear.
#### Expected Output
That all columns headings, both for index and non-index columns should be visible when scrolling down the DataFame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
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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} | 3 | "2021-07-14T19:38:00Z" | "2021-07-16T09:46:42Z" | "2021-07-14T23:12:58Z" | MEMBER | null | - [ ] xref #39696
- [ ] 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
Not turning on CI/Checks since that one is non-trivial since it requires configuring the doc build to not upload on 1.3.x.
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} | 3 | "2021-07-14T20:27:46Z" | "2021-07-16T01:53:40Z" | "2021-07-14T23:12:09Z" | MEMBER | null | Reverts pandas-dev/pandas#42413
Test passing on 3.9 numpydev again?
Numpydev is red again b/c of this I think.
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} | 5 | "2021-07-14T22:30:00Z" | "2021-11-20T21:11:25Z" | "2021-11-20T21:11:25Z" | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.json_normalize.html (currently responds with a `404 Not Found`)
#### Documentation problem
While the source code does contain API reference documentation for `pandas.json_normalize` (previously `pandas.io.json.json_normalize`) as seen in [`/pandas/io/json/_normalize.py`](https://github.com/pandas-dev/pandas/blob/master/pandas/io/json/_normalize.py#L240-L381), it isn't available in the documentation for the current version of pandas hosted on `pandas.pydata.org`.
#### Suggested fix for documentation
At least one older version of `pandas` _does_ have documentation that includes a `json_normalize` API reference under its old name ([`pandas.io.json.json_normalize` in v0.25.0](https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.io.json.json_normalize.html)), so it seems feasible that it was removed by accident when its old name was [deprecated in v1.0.0](https://github.com/pandas-dev/pandas/blob/master/pandas/io/json/_normalize.py#L524-L526). Attempting to visit [`pandas.io.json.json_normalize` in the current version of the documentation](https://pandas.pydata.org/docs/dev/reference/api/pandas.io.json.json_normalize.html) redirects to the new name (`pandas.json_normalize`), which responds with a 404.
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} | 3 | "2021-07-14T22:58:54Z" | "2021-07-19T15:36:44Z" | "2021-07-15T04:18:39Z" | MEMBER | null | - [x] closes #42092
- [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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} | 7 | "2021-07-15T00:47:26Z" | "2021-10-04T12:10:50Z" | "2021-10-04T12:10:46Z" | CONTRIBUTOR | null | The documentation on the parameters that actually are of type `Suffixes`:
> *suffixeslist-like, default is (“_x”, “_y”)*
> A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. _**Pass a value of None**_ instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.
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```
In [3]: ser = pd.Series([pd.NA], dtype="Int64")
In [4]: ser.isin([pd.NA])
Out[4]:
0 True
dtype: boolean
In [5]: ser.isin([np.nan])
Out[5]:
0 False
dtype: boolean
```
`StringArray.isin` also follows this behavior, so it is not `MaskedArray` specific.
There was discussion about this being problematic since `_from_sequence` will treat other missing values just the same as `pd.NA` (https://github.com/pandas-dev/pandas/pull/42473#discussion_r667991012). In that case, the output in the second case should also be `True`.
As a final option, both outputs could be `pd.NA`. In #38379 there was discussion of propagating `pd.NA` instead of `True/False` depending on the presence of missing values in the `values` argument. A nice description of that debate is here https://github.com/pandas-dev/pandas/pull/38379#issuecomment-757510449. | {
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```
In[13]:
df = pd.DataFrame([1,2,3], columns=['num'])
df.describe().loc['count']
Out[13]:
num 3.0
Name: count, dtype: float64
```
I think count variable should be integer type because it is "count".
#### Describe the solution you'd like
Before returning the count value, change its type to int or int64.
#### Additional Question
Is there any intentional reason for doing the variable of count so far? | {
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} | 0 | "2021-07-15T04:19:07Z" | "2021-07-15T12:25:41Z" | "2021-07-15T12:25:41Z" | NONE | null | Backport PR #42541: REGR: concat(ints, bools) casting to object #42092 | {
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} | 13 | "2021-07-15T14:47:32Z" | "2021-10-16T19:25:34Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
This requires both pandas and grpcio-tools.
```python
import subprocess
import sys
import pandas as pd
proto = """
syntax="proto3";
message Data {
repeated float values = 1;
}
"""
with open('data.proto', 'w') as f:
f.write(proto)
subprocess.run([
sys.executable, '-m', 'grpc_tools.protoc', '-I.',
'--python_out=.', '--grpc_python_out=.', 'data.proto'
])
from data_pb2 import Data
proto_data = Data(values=range(3))
print(proto_data.values)
print(type(proto_data.values))
df = pd.DataFrame(index=range(3), data={'a': proto_data.values})
print(df)
```
#### Problem description
On 1.3.0 and the master branch this code prints:
```
[0.0, 1.0, 2.0]
<class 'google.protobuf.pyext._message.RepeatedScalarContainer'>
a
0 [0.0, 1.0, 2.0]
1 [0.0, 1.0, 2.0]
2 [0.0, 1.0, 2.0]
```
The issue seems to arise from #41592. In 1.2.x this object was handled by `_try_cast` in [this else clause](https://github.com/pandas-dev/pandas/blob/1.2.x/pandas/core/construction.py#L495-L496). After that change, it's handled by `construct_1d_arraylike_from_scalar` because [`is_list_like(data)` returns False](https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py#L511-L515). Note that [RepeatedScalarContainer](https://github.com/protocolbuffers/protobuf/blob/master/python/google/protobuf/pyext/repeated_scalar_container.cc#L767-L781) implements `PyTypeObject.tp_as_sequence` but not `PyTypeObject.tp_iter`, so `list(proto_data.values)` works fine, but the [`hasattr(obj, "__iter__")` check in `is_list_like`](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/lib.pyx#L1092-L1102) is False.
Based on all this, I suspect that this same issue will occur on any object which implements `PyTypeObject.tp_as_sequence` but not `PyTypeObject.tp_iter`, however this protobuf object the only example I have right now so I can't test further.
I'm not familiar enough with Cython to provide a full fix, but is there some way to examine the struct fields of `obj` in `is_list_like`? If so, that function could be amended to check `hasattr(obj, "__iter__") or hasattr(obj, "tp_as_sequence")`. If not I think the logic of `sanitize_array` needs to be amended.
#### Expected Output
On 1.2.x this prints:
```
[0.0, 1.0, 2.0]
<class 'google.protobuf.pyext._message.RepeatedScalarContainer'>
a
0 0.0
1 1.0
2 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.6.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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.3.4
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 : 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 : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Currently, the constructor for `Series` has the `name` argument as a `str` or `None` . But `Series.name` can return a `Hashable`, e.g., as a result of using `DataFrame.xs()` or `.iloc` on a row of a `DataFrame`.
So this is inconsistent. I think people expect the names of Series to be strings.
#### Describe the solution you'd like
Change the implementation of `Series.name` to return a string or `None`
#### API breaking implications
This is cleaning up the API, IMHO.
#### Describe alternatives you've considered
N/A
#### Additional context
ref: discussion in #42534
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} | 12 | "2021-07-15T17:37:44Z" | "2021-11-19T11:00:23Z" | "2021-11-19T09:15:16Z" | 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
exi = pd.read_parquet('/home/yoh/Documents/code/data/existing.parquet', engine='fastparquet')
new = pd.read_parquet('/home/yoh/Documents/code/data/new.parquet', engine='fastparquet')
to_record = pd.concat([exi, new])
```
Please, find files enclosed. I could not succeed to re-create manually faulty dataframes.
[faulty_dataframes.zip](https://github.com/pandas-dev/pandas/files/6825097/faulty_dataframes.zip)
(size of each is 5 rows x 8 columns)
#### Problem description
Before installation of `pandas 1.3.0`, I was using `pandas 1.2.5` and `fastparquet 0.6.4.dev0` and this extract of data was not causing problem.
After I installed `pandas 1.3.0` the `concat` command is now issuing following error:
```python
to_record = pd.concat([exi, new])
Traceback (most recent call last):
File "<ipython-input-2-9967cb321e9e>", line 4, in <module>
to_record = pd.concat([exi, new])
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 307, in concat
return op.get_result()
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 532, in get_result
new_data = concatenate_managers(
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py", line 222, in concatenate_managers
values = _concatenate_join_units(join_units, concat_axis, copy=copy)
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py", line 486, in _concatenate_join_units
to_concat = [
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py", line 487, in <listcomp>
ju.get_reindexed_values(empty_dtype=empty_dtype, upcasted_na=upcasted_na)
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py", line 403, in get_reindexed_values
values = self.block.get_values()
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 1360, in get_values
return np.asarray(values).reshape(self.shape)
ValueError: cannot reshape array of size 5 into shape (1,0)
```
I could notice that using `pyarrow` to read the files back allows having dataframes not causing any error.
I also tried to concat various extract of the dataframes by selecting column one by one, or even several at once, and it does not raise the error. For instance, following ` concat` do not raise trouble:
```python
to_record = pd.concat([exi[['timestamp','period','side']], new[['side','timestamp','period']]])
to_record = pd.concat([exi[['period','id']], new[['id','period']]])
to_record = pd.concat([exi['tracking'], new['tracking']])
# etc...
```
I am at a loss to reduce the trouble to the root cause.
Please, would anyone has some advice?
#### Expected Output
No error :)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-59-generic
Version : #66~20.04.1-Ubuntu SMP Thu Jun 17 11:14:10 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.0
fastparquet : 0.6.4.dev0
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.19
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42553 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42553/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42553/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42553/events | https://github.com/pandas-dev/pandas/pull/42553 | 945,613,283 | MDExOlB1bGxSZXF1ZXN0NjkwOTE3NzMw | 42,553 | Revert "REGR: concat(ints, bools) casting to object #42092" | {
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This fails ArrayManager.
cc @jbrockmendel | {
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} | 2 | "2021-07-15T22:44:41Z" | "2021-07-22T18:37:22Z" | "2021-07-16T13:26:04Z" | CONTRIBUTOR | null | I have added Ibis to pandas ecosystem under the section of Out_of_core. This solves issue #20483
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- [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
## Pandas <1.2.0
import numpy as np
import pandas as pd
data = pd.DataFrame(
{
"admin": [0.0, 0.15, 0.15],
"rate": [
np.array([[0.5, 0.25, 0.25], [1.2, 0.6, 0.3]]),
np.array([[0.0, 0.75, 0.25], [5.5, 0.4, 0.7]]),
np.array([[1.0, 0, 0], [1.0, 0, 0]]),
],
"distrib": [
np.array([0.0, 1.0, 0.0]),
np.array([0.0, 0.0, 1.0]),
np.array([1.0, 0, 0]),
],
}
)
data["base"] = data["admin"] * data["rate"]
data["final"] = data["base"] * data["distrib"] * 2
print(data["final"])
```
Outputs:
```
0 [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
1 [[0.0, 0.0, 0.075], [0.0, 0.0, 0.21]]
2 [[0.3, 0.0, 0.0], [0.3, 0.0, 0.0]]
Name: final, dtype: object
```
#### Problem description
This is kind-of a bug, kind-of a regression, kind-of a feature request.
Prior to version `v1.2.0` we could have Numpy N-d arrays within cells of the DataFrame. We could apply math to them, math columns together, etc. It worked *fairly* well—with a few small hiccups. But it was workable.
With `v1.2.0` Pandas got much more strict on having only scalar values in Series. I understand why—that’s the primary purpose of Pandas DataFrames. But it completely breaks our use case.
Our use case is computing costs for leases within large commercial properties. The costs are distributed into cost pools, matrixes of interior/exterior expenses (as an example). Our cost pools are `3x3` NP arrays. We need to take those cost pools and multiply them by admin fees & redistribute them based on lease conditions etc.
| lease_id | admin_fee | rate_cost_pool |
| --- | --- | --- |
| 1 | 0.15 | [ [4.5,0.5,1.5], [4.5,0.5,1.5], [4.5,0.5,1.5] ] |
| 2 | 0.10 | [ [0.0,0.5,1.5], [0.343,0.5,1.5], [4.5,0.5,2.87] ] |
*Example: multiply each `rate_cost_pool` by their `admin_fee`, etc.*
**With Pandas v1.2.0 this stopped working.** Getting “can only perform ops with numeric values“ `TypeErrors`, etc.
I thought maybe making a new Dtype would allow me to somehow force Pandas to think my NDArrays are “scalar” but couldn’t figure that out. I got close by using `Tensor` from this library:
https://github.com/CODAIT/text-extensions-for-pandas
#### Expected Output
I hope there’s a way to get this working with newer Pandas versions. It’s not sustainable to keep our Pandas version out-of-date.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.11.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 57.0.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.21
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-07-15T23:46:27Z" | "2021-07-16T16:04:23Z" | "2021-07-16T10:38:14Z" | MEMBER | null | - [ ] closes #39696
- [ ] 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
cc @simonjayhawkins
How this was tested
I made a fake 1.3.x branch which is actually just a copy of master.
I applied these changes, and GHA triggered on my fork. I also verifyed that Installing the ssh key and uploading docs were skipped.
See the run here.
https://github.com/lithomas1/pandas/runs/3081634451?check_suite_focus=true
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} | 4 | "2021-07-16T00:18:56Z" | "2021-08-02T15:16:51Z" | "2021-08-02T15:16:15Z" | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
**Note**: Not in a released pandas, only master
---
#### Code Sample
```python
import pandas as pd
mi = pd.MultiIndex.from_product([["a","b"],[1,2],["z","y"]])
df = pd.DataFrame(index=mi,dtype=float)
mi2 = pd.MultiIndex.from_product([["a","b"],[1,2]])
s = pd.Series(index=mi2, dtype=float)
s.iloc[:]=3.14
df["new"] = s
print(df)
```
#### Problem description
In 1.3.0 MultiIndex assignment broadcast missing levels. As of #42231, this no longer works.
#### Expected Output
In master, I see
```
new
a 1 z NaN
y NaN
2 z NaN
y NaN
b 1 z NaN
y NaN
2 z NaN
y NaN
```
In 1.3.0 or 1.2.5, I see
```
new
a 1 z 3.14
y 3.14
2 z 3.14
y 3.14
b 1 z 3.14
y 3.14
2 z 3.14
y 3.14
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f3a6753d2d8abd20cd522920d4dc08555c701bce
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.4.0.dev0+246.gf3a6753d2d
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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- [ ] 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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Prior to [GH40230](https://github.com/pandas-dev/pandas/issues/40230) it was possible to write to existing sheet using code like below. I would like to be able to do it again.
I use it to create excel files with overly compicated and/or regularly changing header requirements.
```python
def write_to_excel(df: pd.DataFrame, mode: str = 'a', form: Path, sheetname: Union[int, str] = 0, **kwargs)
with pd.ExcelWriter(path, mode=mode) as writer:
if mode == 'a':
for sheet, sheet_name in zip(writer.book.worksheets, writer.book.sheetnames)
writer.sheets.setdefault(sheet_name, sheet)
if isinstance(sheetname, int):
sheetname = writer.book.sheetnames[sheetname]
else:
sheetname = sheetname if isinstance(sheetname, str) else 'Sheet1'
df.to_excel(writer, sheet_name=sheetname, engine='openpyxl', **kwargs)
```
#### Describe the solution you'd like
Add `append` option for `if_sheet_exists` parameter
#### API breaking implications
New option for parameter shouldn't break API.
#### Describe alternatives you've considered
This was possible prior to previously mentioned enhancement. Only solution I can think of now is to initiate `ExcelWriter` with `if_sheet_exists='new'` and to copy everything from previously existing sheet before writing dataframe and to rename and delete sheets accordingly later.
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] | open | false | null | [] | null | 6 | "2021-07-16T10:06:51Z" | "2021-10-18T05:27:43Z" | null | NONE | null | Added autofilter parameter to Excel output for both xlsxwriter and openpyxl engines
- [x] closes #15307
- [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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] | open | false | null | [] | null | 0 | "2021-07-16T13:00:00Z" | "2021-08-21T04:28:48Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish DataFrame.read_excel could skip automatically the rows that are hidden in excel. I want to read data from an excel sheet but I want to select within excel the data that will go to the dataFrame. I am using excel filter to select data of interest so some lines are hidden. I can use [this workaround](https://stackoverflow.com/questions/67318473/reading-the-excel-file-into-pandas-data-frame-without-hidden-rows-or-ignore-the) but it implies reading twice the excel sheet which in my case is quite big.
#### Describe the solution you'd like
`DataFrame.read_excel` should get a new parameter `skip_hidden` that is default Fasle but when set to True will `DataFrame.read_excel` will skip the hidden rows
#### API breaking implications
Additionnal parameter to `DataFrame.read_excel` with default value so I guess no API breaking risk.
#### Describe alternatives you've considered
Going for a workaround with `openpyxl` which implies opening twice the excel sheet which is expensive. [stackoverflow example](https://stackoverflow.com/questions/67318473/reading-the-excel-file-into-pandas-data-frame-without-hidden-rows-or-ignore-the)
#### Additional context
looking at `pandas/io/excel/_openpyxl.py` function `get_sheet_data`, considering new parameter to `read_excel` named `skip_hidden`.
modification looks quite simple:
```python
def get_sheet_data(self, sheet, convert_float: bool, skip_hidden: bool) -> list[list[Scalar]]:
if self.book.read_only:
sheet.reset_dimensions()
rowDim=sheet.row_dimensions # added
data: list[list[Scalar]] = []
last_row_with_data = -1
for row_number, row in enumerate(sheet.rows):
converted_row = [self._convert_cell(cell, convert_float) for cell in row]
while converted_row and converted_row[-1] == "":
# trim trailing empty elements
converted_row.pop()
if converted_row:
last_row_with_data = row_number
if not (skip_hidden and rowDim[row_number].hidden) : # added
data.append(converted_row)
# Trim trailing empty rows
data = data[: last_row_with_data + 1]
if len(data) > 0:
# extend rows to max width
max_width = max(len(data_row) for data_row in data)
if min(len(data_row) for data_row in data) < max_width:
empty_cell: list[Scalar] = [""]
data = [
data_row + (max_width - len(data_row)) * empty_cell
for data_row in data
]
return data
```
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} | 5 | "2021-07-16T13:47:29Z" | "2021-09-14T16:35:25Z" | "2021-09-14T16:35:22Z" | CONTRIBUTOR | null | Add code that allows nullable arrays to be written
closes #40855
- [X] closes #40855
- [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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} | 1 | "2021-07-16T15:00:14Z" | "2021-07-16T15:19:28Z" | "2021-07-16T15:19:28Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (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
from tqdm import tqdm
tqdm.pandas()
df = pd.DataFrame({"a": [0,1,2], "b": [0,1,2]})
df.progress_apply(lambda row: row["b"] + 3, axis = 1)
```
#### Problem description
The snippet above fails with
`AttributeError: 'DataFrame' object has no attribute '_is_builtin_func'`
not sure if this is a compatibility bug with tqdm.
#### Expected Output
```
100%|██████████| 3/3 [00:00<00:00, 627.73it/s]
0 3
1 4
2 5
dtype: int64
```
(works with `pandas==1.2.6`)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.20
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
asvs show no change for non-nullable dtypes | {
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- [x] tests added / passed
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To see the bug, see the changed `expected` in test_mi_intervalindex_slicing_with_scalar | {
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} | 2 | "2021-07-16T16:42:17Z" | "2021-07-22T22:54:16Z" | "2021-07-22T22:54:16Z" | 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
# Your code here
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(25).reshape(5,5))
def valid_indexes(in_df: pd.DataFrame):
return in_df.loc[:3]
valid = valid_indexes(df)
valid[2] = 3
```
#### Problem description
Before (pandas 1.2.*), the resulting warning showed my own code:
```
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
valid[2] = 3
```
Now, it only shows library code:
```
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self._set_item(key, value)
```
This is significantly harder to diagnose.
#### Expected Output
The line of code I wrote to produce the error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.15
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 4 | "2021-07-16T17:20:21Z" | "2021-07-17T00:15:38Z" | "2021-07-16T22:01:44Z" | 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
I accidentally made a typo in #42556.
Hopefully works this time. The docs weren't getting uploaded, b/c of this typo. | {
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- [ ] 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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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
```python
>>> import numpy as np
>>> import pandas as pd
>>> mat = np.array([[1,2], [2,3]])
>>> mat
array([[1, 2],
[2, 3]])
>>> df = pd.DataFrame(mat)
>>> df.iloc[0,1] = 4
>>> mat
array([[1, 4],
[2, 3]])
```
Why the value in the original `mat` is also changed? I am not sure if this is a bug or Pandas' way of doing things. I found it to be very confusing. | {
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} | 3 | "2021-07-16T20:58:02Z" | "2021-07-22T17:55:16Z" | "2021-07-22T17:55:06Z" | CONTRIBUTOR | null | Minor change to DataFrame.plot.hist documentation: "Dice" is traditionally plural (one die, two dice). Nowadays it can also be singular, but "die" is clearer. "Dices" is simply not a thing.
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} | 4 | "2021-07-16T21:39:40Z" | "2021-07-25T14:42:48Z" | "2021-07-25T14:20:50Z" | MEMBER | null | Reverts pandas-dev/pandas#42231
This introduced a bug discussed in #42231. The long-term fix is in MultiIndex.get_indexer, but that's going to take me a little while to get done. | {
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} | 3 | "2021-07-16T21:47:26Z" | "2021-07-19T16:27:29Z" | "2021-07-19T15:35:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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} | 6 | "2021-07-16T21:59:34Z" | "2021-07-20T12:17:38Z" | "2021-07-20T12:17:05Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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This should close #38645 in conjunction with dask/dask#7888, will hold off on closing the issue until @jsignell confirms. | {
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} | 7 | "2021-07-16T22:07:40Z" | "2021-07-23T01:07:37Z" | "2021-07-23T01:02:27Z" | MEMBER | null | - [x] closes #42477
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@mzeitlin11 did you want to suggest an improved warning message for .insert? | {
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] | open | false | null | [] | null | 2 | "2021-07-17T00:42:49Z" | "2021-09-07T22:01:37Z" | null | MEMBER | null | Currently, I think we are downloading all the packages every time CI is run. This takes a long time(~5-10 min. on azure) and makes CI flakier, since sometimes we can't connect to the Anaconda servers, and sometimes we timeout on Azure. We should cache the packages downloaded and only check for updates everytime the CI is run. I think we're doing some form of this on GHA, but we should also do it on azure.
cc @fangchenli
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] | open | false | null | [] | null | 7 | "2021-07-17T04:43:46Z" | "2021-08-21T19:02:19Z" | null | NONE | null | [Asked at SO](https://stackoverflow.com/questions/68403642/saving-pandas-dataframes-in-formats-including-data-descriptions-or-other-self-do/): I need to share well described data and want to do this in a modern way that avoids managing bureaucratic documentation no one will read. Fields require some description or note (eg. "values don't include ABC because XYZ") which I'd like to associate to columns that'll be saved with `pd.to_<whatever>()`.
[Looks like JSON supports annotations](http://json-schema.org/understanding-json-schema/reference/generic.html#annotations) and I'd love to have the option of using them with pandas, but [couldn't figure out how to](https://stackoverflow.com/questions/68403642/saving-pandas-dataframes-in-formats-including-data-descriptions-or-other-self-do/).
Could we please develop functionality to add descriptions in a convenient way (eg. `df[col].description = 'string'`) and have that save in output schemas? And maybe have that be selectable and show with `df.info(verbose=True)` or similar?
I know documentation is boring but maintaining bureaucratic paperwork is even worse. Also, data documentation is a requirement common to big orgs and schools / unis, and I reckon providing innovative functionality to make boring tasks more enjoyable is an efficient way of getting more people to ~~stop using excel~~ use pandas and newer technology in general, making the world a better place.
Unfortunately I don't understand pandas enough to see how this might be a stupid idea. Is this possible? | {
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- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version 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
for k in range(9,17):
dstr = '{}-07-2021 15:57:40'.format(k) # assumed format ''%d-%m-%Y %H:%M:%S' eg. in ch/DE
print(' dstr: {} pd.to_datetime(): {}'.format(dstr,pd.to_datetime(dstr)))
Output:
dstr: 9-07-2021 15:57:40 pd.to_datetime(): 2021-**09**-07 15:57:40
dstr: 10-07-2021 15:57:40 pd.to_datetime(): 2021-**10**-07 15:57:40
dstr: 11-07-2021 15:57:40 pd.to_datetime(): 2021-**11**-07 15:57:40
dstr: 12-07-2021 15:57:40 pd.to_datetime(): 2021-**12**-07 15:57:40
dstr: 13-07-2021 15:57:40 pd.to_datetime(): 2021-07-13 15:57:40
dstr: 14-07-2021 15:57:40 pd.to_datetime(): 2021-07-14 15:57:40
dstr: 15-07-2021 15:57:40 pd.to_datetime(): 2021-07-15 15:57:40
dstr: 16-07-2021 15:57:40 pd.to_datetime(): 2021-07-16 15:57:40
```
#### Problem description
It would be ok if pd.to_datetime() assumed a default for parameter format, for example either US-format, or ISO-format '%Y/%m/%d %H:%M:%S'.
Anyhow as the code sample show, to_datetime() changes it's assumptions based on each input string. This is not acceptable and can be very harmful. There is no good in such dynamic parsing strategy.
I guess pd.to_datetime() assume a dateformat of '%m-%d-%Y %H:%M:%S', once this assumption is violated, the decoding strategy changes to
'%d-%m-%Y %H:%M:%S' instead of throwing an exception. Such instability is no good. pd.to_datetime() should work according a given date format. First violation according that date format should throw an exception
#### Expected Output
Any time pd.to_datetime() should work under control of a format string, a datetime string format. This could either be a default or a parameter passed format string.
If no format string is passed as a parameter to_datetime() uses a flexible strategy. As Pandas main scope is to work with DataFrame and Series object, timeseries are a prominent application. Such flexible date format interpretation can here be very destructive. Such behaviour of to_datetime() should not need to be expected and is NOT failsafe.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : German_Switzerland.1252
pandas : 1.2.5
numpy : 1.20.2
<details>
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
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] | open | false | null | [] | null | 5 | "2021-07-17T14:36:12Z" | "2021-08-21T19:02:58Z" | 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.
---
**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
import numpy as np
# Example 1: x is ascending, it works with both methods (we expect the NaN value to be filled with "50")
df = pd.DataFrame(
{
'x': [-79, -79, 0, 61, 2783],
'y': [-200, -50, np.nan, -50, 1000],
}
).set_index(['x'])
print('Example 1')
print(df.head())
df1 = df.interpolate('index')
df2 = df.interpolate('slinear')
print(df1.head())
print(df2.head())
# Example 2: x is descending, it does not work with any method (seems like the point at position 1 is not taken into account)
df = pd.DataFrame(
{
'x': [79, 79, 0, -61, -2783],
'y': [-200, -50, np.nan, -50, 1000],
}
).set_index(['x'])
print('Example 2')
print(df.head())
df1 = df.interpolate('index')
df2 = df.interpolate('slinear')
print(df1.head())
print(df2.head())
# Example 3: same kind of df as Example 2 (descending x values), but with 20 elements. It works with 'index', not with 'slinear'
df = pd.DataFrame(
{
'x': [79, 79, 0, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -2783],
'y': [-200, -50, np.nan, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, 1000]
}
).set_index(['x'])
print('Example 3')
print(df.head())
df1 = df.interpolate('index')
df2 = df.interpolate('slinear')
print(df1.head())
print(df2.head())
# Example 4: removing just the elements at positions -2 and -3 in both x and y. it fails with both methods
df = pd.DataFrame(
{
'x': [79, 79, 0, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -61, -2783],
'y': [-200, -50, np.nan, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, 1000]
}
).set_index(['x'])
print('Example 4')
print(df.head())
df1 = df.interpolate('index')
df2 = df.interpolate('slinear')
print(df1.head())
print(df2.head())
```
#### Output:
```
Example 1
y
x
-79 -200.0
-79 -50.0
0 NaN
61 -50.0
2783 1000.0
y
x
-79 -200.0
-79 -50.0
0 -50.0
61 -50.0
2783 1000.0
y
x
-79 -200.0
-79 -50.0
0 -50.0
61 -50.0
2783 1000.0
Example 2
y
x
79 -200.0
79 -50.0
0 NaN
-61 -50.0
-2783 1000.0
y
x
79 -200.000000
79 -50.000000
0 -115.357143
-61 -50.000000
-2783 1000.000000
y
x
79 -200.000000
79 -50.000000
0 -115.357143
-61 -50.000000
-2783 1000.000000
Example 3
y
x
79 -200.0
79 -50.0
0 NaN
-61 -50.0
-61 -50.0
y
x
79 -200.0
79 -50.0
0 -50.0
-61 -50.0
-61 -50.0
y
x
79 -200.000000
79 -50.000000
0 -115.357143
-61 -50.000000
-61 -50.000000
Example 4
y
x
79 -200.0
79 -50.0
0 NaN
-61 -50.0
-61 -50.0
y
x
79 -200.000000
79 -50.000000
0 -115.357143
-61 -50.000000
-61 -50.000000
y
x
79 -200.000000
79 -50.000000
0 -115.357143
-61 -50.000000
-61 -50.000000
```
#### Problem description
I am trying to use pandas to interpolate "curves" that can have "steps" (eg. 2 consecutive points with same x-coordinate, but different y-coordinate, or the other way around). There can be an arbitrary number of NaN values between 2 points.
I think this is quite a common use case, and I think i would be cool to have pandas able to perform this kind of "point-to-point" interpolation.
I have been using either "index" or "slinear" methods to do so. While it seems to work in most cases, there seem to be inconsistencies around:
- the fact that x-axis is ordered in ascending (it works - see Example 1 -) or descending order (it fails - see Example 2 -, more precisely it seems to not consider duplicate values of y)
- In some cases, for descendingly sorted x-axis 'slinear' vs 'index' do not yield the same result (it works with 'index' while failing with 'slinear' - see Example 3/4 -) but I really would not figure out what is the root cause, appart length of dataframe. weird..
#### Expected Output
I would like all NA values to be linearly interpolated locally, based on the 'previous' and 'next' points (Side note, for extrapolation, 'nearest'-like approach seems to be the most sensible outcome).
#### Output of ``pd.show_versions()``
<details>
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : None.None
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] 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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- [x] tests added / passed
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- [x] whatsnew entry
Note, this would make the next release 1.4 for sure.
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See these logs from database build for example.
```
___________________________ test_cross_engine_fp_pa ____________________________
[gw1] linux -- Python 3.8.10 /usr/share/miniconda/envs/pandas-dev/bin/python
request = <FixtureRequest for <Function test_cross_engine_fp_pa>>
df_cross_compat = a b d e f
0 a 1 4.0 True 2013-01-01
1 b 2 5.0 False 2013-01-02
2 c 3 6.0 True 2013-01-03
pa = 'pyarrow', fp = 'fastparquet'
def test_cross_engine_fp_pa(request, df_cross_compat, pa, fp):
# cross-compat with differing reading/writing engines
df = df_cross_compat
with tm.ensure_clean() as path:
df.to_parquet(path, engine=fp, compression=None)
with catch_warnings(record=True):
result = read_parquet(path, engine=pa)
> tm.assert_frame_equal(result, df)
E AssertionError: Attributes of DataFrame.iloc[:, 4] (column name="f") are different
E
E Attribute "dtype" are different
E [left]: datetime64[ns, UTC]
E [right]: datetime64[ns]
pandas/tests/io/test_parquet.py:337: AssertionError
______________________ TestParquetFastParquet.test_basic _______________________
[gw1] linux -- Python 3.8.10 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_parquet.TestParquetFastParquet object at 0x7fd39d1cc070>
fp = 'fastparquet'
df_full = string string_with_nan ... datetime_tz timedelta
0 a a ... 2013-01-01 00:00:00-05...01-02 00:00:00-05:00 2 days
2 c c ... 2013-01-03 00:00:00-05:00 3 days
[3 rows x 14 columns]
def test_basic(self, fp, df_full):
df = df_full
dti = pd.date_range("20130101", periods=3, tz="US/Eastern")
dti = dti._with_freq(None) # freq doesn't round-trip
df["datetime_tz"] = dti
df["timedelta"] = pd.timedelta_range("1 day", periods=3)
> check_round_trip(df, fp)
pandas/tests/io/test_parquet.py:915:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/io/test_parquet.py:220: in check_round_trip
compare(repeat)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
repeat = 2
def compare(repeat):
for _ in range(repeat):
df.to_parquet(path, **write_kwargs)
with catch_warnings(record=True):
actual = read_parquet(path, **read_kwargs)
> tm.assert_frame_equal(
expected,
actual,
check_names=check_names,
check_like=check_like,
check_dtype=check_dtype,
)
E AssertionError: Attributes of DataFrame.iloc[:, 6] (column name="uint") are different
E
E Attribute "dtype" are different
E [left]: uint8
E [right]: UInt8
pandas/tests/io/test_parquet.py:210: AssertionError
__________________ TestParquetFastParquet.test_bool_with_none __________________
[gw1] linux -- Python 3.8.10 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_parquet.TestParquetFastParquet object at 0x7fd39fd34af0>
fp = 'fastparquet'
def test_bool_with_none(self, fp):
df = pd.DataFrame({"a": [True, None, False]})
expected = pd.DataFrame({"a": [1.0, np.nan, 0.0]}, dtype="float16")
> check_round_trip(df, fp, expected=expected)
pandas/tests/io/test_parquet.py:928:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/io/test_parquet.py:220: in check_round_trip
compare(repeat)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
repeat = 2
def compare(repeat):
for _ in range(repeat):
df.to_parquet(path, **write_kwargs)
with catch_warnings(record=True):
actual = read_parquet(path, **read_kwargs)
> tm.assert_frame_equal(
expected,
actual,
check_names=check_names,
check_like=check_like,
check_dtype=check_dtype,
)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="a") are different
E
E Attribute "dtype" are different
E [left]: float16
E [right]: boolean
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- [x] Added a test for #25658.
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- [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] Added test for issue #26568
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} | 1 | "2021-07-18T13:43:21Z" | "2021-07-23T08:53:08Z" | "2021-07-22T21:50:03Z" | CONTRIBUTOR | null | - [x] closes #26568
- [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] Added test for issue #26568
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] | closed | false | null | [] | null | 11 | "2021-07-18T14:39:28Z" | "2021-07-19T00:05:02Z" | "2021-07-18T16:35:01Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
The method called quantile, is misnamed. What is it calculating is the fractile. A fractile is a number, where fractile(x) = percentile(100x). A quantile is a set of fractiles.
#### Describe the solution you'd like
Fractile becomes an alias of quantile in next non-major release then quantile is removed in next major release, or redefined. An appropriate redefinition would take an integer n, and return an array of n values where the kth value is given by fractile(k/(n+1)) as per https://en.m.wikipedia.org/wiki/Quantile
#### API breaking implications
Removal or redefinition of quantile method will of course not be backwards compatible.
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- [ ] 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
- [ ] This is a test to see if this passes the CI tests. I will close the pull request after the CI have completed.
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- [ ] tests added / passed
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- [ ] whatsnew entry
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- [x] Closes #16345
- [x] Closes #29129
- [x] tests added / passed
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} | 18 | "2021-07-18T21:22:04Z" | "2021-10-13T12:05:33Z" | null | CONTRIBUTOR | null | ## Motivation
In order for Dask to perform large shuffles (set_index, join on a non-index column, ...) on a column it needs to be able to compute quantiles.
To do this it is useful to compute min/max values.
## What actually breaks
When I try to do this on columns of type `string[pyarrow]` I get the following exception
```python
import pandas as pd
s = pd.Series(["a", "b", "c"]).astype("string[pyarrow]")
s.min()
```
```python-traceback
~/miniconda/lib/python3.8/site-packages/pandas/core/generic.py in min(self, axis, skipna, level, numeric_only, **kwargs)
10825 )
10826 def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
> 10827 return NDFrame.min(self, axis, skipna, level, numeric_only, **kwargs)
10828
10829 setattr(cls, "min", min)
~/miniconda/lib/python3.8/site-packages/pandas/core/generic.py in min(self, axis, skipna, level, numeric_only, **kwargs)
10348
10349 def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
> 10350 return self._stat_function(
10351 "min", nanops.nanmin, axis, skipna, level, numeric_only, **kwargs
10352 )
~/miniconda/lib/python3.8/site-packages/pandas/core/generic.py in _stat_function(self, name, func, axis, skipna, level, numeric_only, **kwargs)
10343 name, axis=axis, level=level, skipna=skipna, numeric_only=numeric_only
10344 )
> 10345 return self._reduce(
10346 func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
10347 )
~/miniconda/lib/python3.8/site-packages/pandas/core/series.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
4380 if isinstance(delegate, ExtensionArray):
4381 # dispatch to ExtensionArray interface
-> 4382 return delegate._reduce(name, skipna=skipna, **kwds)
4383
4384 else:
~/miniconda/lib/python3.8/site-packages/pandas/core/arrays/string_arrow.py in _reduce(self, name, skipna, **kwargs)
377 def _reduce(self, name: str, skipna: bool = True, **kwargs):
378 if name in ["min", "max"]:
--> 379 return getattr(self, name)(skipna=skipna)
380
381 raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
AttributeError: 'ArrowStringArray' object has no attribute 'min'
```
## Solution
I am hopeful that Arrow maybe already has an min/max implementation and they just haven't been hooked up yet. | {
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} | 0 | "2021-07-18T22:38:09Z" | "2021-07-22T18:46:34Z" | "2021-07-22T18:46:34Z" | MEMBER | null | - [X] xref #22459
Fixing 4 broken doctests, so we can validate the whole `pandas/core` directory. And also, significantly reduce the list of directories/files in the call to validate the doctests. | {
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} | 2 | "2021-07-18T23:49:02Z" | "2021-07-19T14:47:21Z" | "2021-07-19T14:47:20Z" | CONTRIBUTOR | 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.
---
#### Code Sample, a copy-pastable example
```python
>>> pd.Series(np.nan) == pd.Series(np.nan)
0 False
>>> pd.Series(np.nan) == pd.Series(np.nan).astype("Int64")
0 <NA>
>>> pd.Series(np.nan).astype("Int64") == pd.Series(np.nan)
0 <NA>
>>> np.nan == np.nan
False
>>> pd.Series(np.nan).astype("Int64").values[0] == pd.Series(np.nan).astype("Int64").values[0]
<NA>
```
#### Problem description
Pandas recently introduced a [Nullable Int64 data type](https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html). I missed the conversation, but I feel like its behavior should be consistent with numpy float nan's here?
pandas 1.2.4
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Fri Mar 5 01:14:14 PST 2021; root:xnu-7195.101.1~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.7.5
pip : 21.1.3
setuptools : 53.0.0
Cython : 0.29.19
pytest : 5.0.1
hypothesis : None
sphinx : 1.6.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 2021.06.1
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.2.0
xlwt : None
numba : 0.49.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42600/events | https://github.com/pandas-dev/pandas/issues/42600 | 947,152,394 | MDU6SXNzdWU5NDcxNTIzOTQ= | 42,600 | ENH: Serialize view of ArrowStringArray | {
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] | open | false | null | [] | null | 4 | "2021-07-19T00:32:53Z" | "2021-07-22T17:51:47Z" | null | CONTRIBUTOR | null | Currently Pandas serializes views of ArrowStringArrays by serailizing the whole thing, rather than a subset. Here is an example:
```python
In [1]: import pandas as pd
In [2]: s = pd.Series([c * 1000 for c in "abcdefghijklmnopqrstuvwxyz"])
In [3]: s
Out[3]:
0 aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
1 bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb...
2 cccccccccccccccccccccccccccccccccccccccccccccc...
3 dddddddddddddddddddddddddddddddddddddddddddddd...
4 eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee...
5 ffffffffffffffffffffffffffffffffffffffffffffff...
6 gggggggggggggggggggggggggggggggggggggggggggggg...
7 hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh...
8 iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii...
9 jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj...
10 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk...
11 llllllllllllllllllllllllllllllllllllllllllllll...
12 mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm...
13 nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn...
14 oooooooooooooooooooooooooooooooooooooooooooooo...
15 pppppppppppppppppppppppppppppppppppppppppppppp...
16 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq...
17 rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr...
18 ssssssssssssssssssssssssssssssssssssssssssssss...
19 tttttttttttttttttttttttttttttttttttttttttttttt...
20 uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu...
21 vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv...
22 wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww...
23 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx...
24 yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy...
25 zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz...
dtype: object
In [4]: import pickle
In [5]: len(pickle.dumps(s))
Out[5]: 26758
In [6]: len(pickle.dumps(s.astype("string[pyarrow]")))
Out[6]: 26891
In [7]: len(pickle.dumps(s.head(5)))
Out[7]: 5632
In [8]: len(pickle.dumps(s.astype("string[pyarrow]").head(5)))
Out[8]: 26891
```
This negatively affects dask dataframe operations that cut up pandas dataframes into small pieces, moves them around to different computers, and then pieces them back together again. | {
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] | closed | false | null | [] | null | 2 | "2021-07-19T04:35:36Z" | "2021-07-19T18:15:01Z" | "2021-07-19T17:09:39Z" | NONE | null | For this dataframe
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},index=[0.1,0.2,0.5,0.7,0.9])
df
B
0.1 0.0
0.2 1.0
0.5 2.0
0.7 NaN
0.9 4.0
I want to perform an offset based rolling like:
df.rolling(0.3).sum()
But I got this error:
> ValueError: window must be an integer
Could anyone help me? my real case involves about 1B rows, and I needed to do groupby().rolling() in the end. The above is just an extreme simplification of my problem. Please help me. Thanks!
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<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.11-py2.py3-none-any.whl (98 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.6.0-py2.py3-none-any.whl (5.3 MB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
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Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
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Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
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Collecting platformdirs<3,>=2
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Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.11 nodeenv-1.6.0 platformdirs-2.0.2 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.6.0
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
updating 21.6b0 -> 21.7b0.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.21.0 -> v2.21.2.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42603/events | https://github.com/pandas-dev/pandas/issues/42603 | 947,341,661 | MDU6SXNzdWU5NDczNDE2NjE= | 42,603 | BUG: Pandas loc can result in ambiguous result not caught by an exception | {
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] | open | false | null | [] | null | 3 | "2021-07-19T07:11:27Z" | "2021-08-21T19:04:24Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
Tested on Pandas 1.3.0.
```python
import pandas as pd
import numpy as np
import pytest
sample_name = ["foo", "foo", "foo"]
sample_number = [3, 3, 3]
y_pred = np.array([[0.1, 0.1, 0.1], [0.9, 0.9, 0.9]]).reshape(-1, 2)
# a df with columns '0' and '1'
df = pd.DataFrame(y_pred, index=[sample_name, sample_number])
# Returns a df, as expected
c1 = df.loc["foo", 3]
print(c1)
assert hasattr(c1, "columns")
# Key error, as expected, because there's no entry 2
with pytest.raises(KeyError):
_c2 = df.loc["foo", 2]
# Now it's a Series of (foo, column 0) instead of a DataFrame!
c3 = df.loc["foo", 0]
print(c3)
assert hasattr(c3, "columns")
```
#### Problem description
When converting arrays to DataFrames, the columns will automatically be enumerated. Obviously this problem is somewhat circumventable by not doing all these implicit conversions. However, it doesn't warn you of this, and the case I describe above can return a Series *or* a DataFrame, depending on which index you query for, which, at least for me, created a decent amount of confusion.
#### Expected Output
I'd imagine it should raise an ambiguity error, similar to https://github.com/pandas-dev/pandas/issues/21080, like
```python
ValueError: '0' is both an index and a column label.
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] | open | false | null | [] | null | 0 | "2021-07-19T12:00:14Z" | "2021-08-21T19:04:40Z" | null | CONTRIBUTOR | null | Per comment in PR #41985 :
https://github.com/pandas-dev/pandas/pull/41985/files/c14bca375fc1f88b0ebc3fdf9d94bab568d1066a#r672213029
PR updated the docs, which reflected that `pandas.core.algorithms.duplicated()` does accept a `Series` as an argument:
```python
>>> import pandas as pd
>>> from pandas.core.algorithms import duplicated
>>> s=pd.Series([1,2,3,4,2,3,1])
>>> duplicated(s)
array([False, False, False, False, True, True, True])
```
So the input type should be updated.
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https://api.github.com/repos/pandas-dev/pandas/issues/42605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42605/events | https://github.com/pandas-dev/pandas/pull/42605 | 947,588,430 | MDExOlB1bGxSZXF1ZXN0NjkyNTUwNDk0 | 42,605 | DOC: use the commonly accepted capitalization of LaTeX | {
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- [ ] 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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} | 6 | "2021-07-19T12:13:07Z" | "2021-08-30T13:12:39Z" | "2021-08-30T13:12:39Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I had a data pipeline that was terribly slow. Turns out the issue was that all the time was spent in pd.to_datetime calls with unix sec integers as input, because I was passing big-endian ints. With normal ints it's about 1000x faster.
Can anyone reproduce this performance issue?
is it possible to improve on this "gotcha", e.g. by forcing a typecast on input, or even some other way that doesn't require a copy and extra memory?
```
In [10]: %timeit index = pd.to_datetime(np.arange(1_000_000), unit="s", utc=True)
7.83 ms ± 22 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [23]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint32"), unit="s", utc=True)
CPU times: user 4.03 s, sys: 12.5 ms, total: 4.05 s
Wall time: 4.04 s
In [24]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint64"), unit="s", utc=True)
CPU times: user 8.42 s, sys: 12.9 ms, total: 8.44 s
Wall time: 8.45 s
In [25]: %time index = pd.to_datetime(np.arange(1_000_000).astype("int64"), unit="s", utc=True)
CPU times: user 13.1 ms, sys: 4.05 ms, total: 17.2 ms
Wall time: 15.9 ms
In [26]: %time index = pd.to_datetime(np.arange(1_000_000).astype("int32"), unit="s", utc=True)
CPU times: user 13.4 ms, sys: 4.54 ms, total: 18 ms
Wall time: 16.5 ms
```
#### Output of ``pd.show_versions()``
<details>
In [11]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
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scipy : 1.6.3
sqlalchemy : 1.4.18
tables : None
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.2.0
xlwt : None
numba : 0.53.1
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] | open | false | null | [] | null | 0 | "2021-07-19T14:41:16Z" | "2021-08-21T19:06:02Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
# Test 1 - Group by with value counts returns a series
id_column = ["idA","idA","idA","idA","idA","idA","idB","idB","idB","idB","idB","idB","idB"]
test_bool_value = [True,False,False,False,False,False,True,True,False,False,False,True,True]
test_data = {"id":id_column, "bool_value":test_bool_value}
df = pd.DataFrame(data=test_data)
df_output = df.groupby("id").apply(lambda df: df["bool_value"].value_counts())
print(type(df_output))
# Test 2 - Group by with value counts returns a dataframe (same input df shape with different values)
id_column = ["idA","idA","idA","idA","idA","idA","idB","idB","idB","idB","idB","idB","idB"]
test_bool_value = [True,True,True,True,False,False,True,True,False,False,False,True,True]
test_data = {"id":id_column, "bool_value":test_bool_value}
df = pd.DataFrame(data=test_data)
df_output = df.groupby("id").apply(lambda df: df["bool_value"].value_counts())
print(type(df_output))
```
#### Problem description
When using value_counts() on a column within a groupby - in this instance to count how many True/False values exist within each id grouping - the output type varies between a Series and a DataFrame depending on the specific arrangement of True/False values with no changes to the shape of the incoming DataFrame.
#### Expected Output
Either both should return `<class 'pandas.core.frame.DataFrame'>` or `<class 'pandas.core.series.Series'>` - seeing as the `value_counts()` function returns a Series I would expect the default output from the groupby operation to be a DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.89+
Version : #1 SMP Sat Feb 13 19:45:14 PST 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
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] | open | false | null | [] | null | 3 | "2021-07-19T15:29:14Z" | "2021-08-21T19:08:57Z" | null | NONE | null | As of today, the result of filtering after grouping is a `DataFrame` :
```
type(df.groupby("foobar").filter(lambda x: len(x)>1)) == pandas.core.frame.DataFrame
```
However in many cases it also makes sense to filter **but keep a DataFrameGroupBy object**.
Surprisingly, I didn’t find such a feature request here, although this topic was raised several times on StackOverflow :
https://stackoverflow.com/questions/36047951/how-to-preserve-pandas-groupby-object-after-filter-operation
https://stackoverflow.com/questions/35832475/how-can-i-filter-a-pandas-groupby-object-and-obtain-a-groupby-object-back
https://stackoverflow.com/questions/36389919/chaining-grouping-filtration-and-aggregation
The last link proposes some interesting workarounds, but they are not convenient or simply fail in more complex cases, when the grouping and filtering is performed on multiple columns. Furthermore, manually re-applying the grouping is neither elegant nor efficient, but most of all it is error-prone in such cases.
Having a specific filter function (or an optional argument to the existing one) to return a filtered `DataFrameGroupBy` object would come very handy. | {
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} | 1 | "2021-07-19T16:43:50Z" | "2021-07-20T14:34:33Z" | "2021-07-20T12:19:03Z" | MEMBER | null | ```
In [1]: from asv_bench.benchmarks.dtypes import *
In [2]: cls = SelectDtypes
In [3]: self = cls()
In [4]: dtype = "Int32"
In [5]: self.setup(dtype)
In [6]: %timeit self.time_select_dtype_string_exclude(dtype)
1.79 ms ± 50.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
47.7 µs ± 461 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
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- [ ] 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
Get CI to green. | {
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] | open | false | null | [] | null | 2 | "2021-07-19T19:15:02Z" | "2021-07-20T11:58:54Z" | null | CONTRIBUTOR | null | In https://github.com/pandas-dev/pandas/issues/35169 / https://github.com/pandas-dev/pandas/issues/42597 we discussed the desired behavior of PyArrow-backed StringArray when a certain method is not implemented in `pyarrow.compute`.
For string methods like `str_normalize`, which aren't currently implemented in `pyarrow.compute`, I believe we (silently) cast from `Pyarrow[string]` to an object-dtype ndarray of Python `str` objects at https://github.com/pandas-dev/pandas/blob/edd5af779a9865e32d22bcd93bf00248565335a1/pandas/core/arrays/string_.py#L527-L528. That's going to be slow and more than doubles the memory usage of the array.
These kinds of performance cliffs are difficult for users to debug. I don't think we should do that conversion on behalf of the user. If something isn't implemented yet, then I think we should raise with a message saying they should convert to `string[python]` dtype first.
If we don't want to raise, we could emit a `PerformanceWarning`, similar to what we do for SparseArray when converting to dense.
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} | 3 | "2021-07-19T19:17:28Z" | "2021-07-19T21:10:47Z" | "2021-07-19T20:23:49Z" | MEMBER | null | Should fix typing failure on master
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- [ y] I have asked my usage related question on [StackOverflow](https://stackoverflow.com/questions/68446123/how-to-perform-group-by-rolling-efficiently-based-on-datetime-index?noredirect=1#comment120965466_68446123).
---
#### Question about pandas: how to perform group by rolling efficiently based on datetime index?
let's firstly create a medium size random df:
size_now = 1e5
df = pd.DataFrame({"time":np.random.randint(1000,size=int(size_now)),
"id":np.random.randint(1000,size=int(size_now)),
"data":np.random.randint(1000,size=int(size_now))})
df.time = pd.to_datetime(df.time,unit="s")
df looks like this:
print(df)
executed in 9ms, finished 15:16:05 2021-07-19
time id data
0 1970-01-01 00:15:01 915 299
1 1970-01-01 00:08:30 31 940
2 1970-01-01 00:02:55 925 600
3 1970-01-01 00:12:48 554 935
4 1970-01-01 00:07:43 565 638
... ... ... ...
99995 1970-01-01 00:16:27 707 615
99996 1970-01-01 00:01:06 298 38
99997 1970-01-01 00:14:26 37 43
99998 1970-01-01 00:16:09 66 791
99999 1970-01-01 00:01:45 376 854
I realized rolling sum can be pretty fast:
df.set_index("time").sort_index().rolling("2500ms")["data"].sum()
time
1970-01-01 00:00:00 66.0
1970-01-01 00:00:00 809.0
1970-01-01 00:00:00 879.0
1970-01-01 00:00:00 1329.0
1970-01-01 00:00:00 1729.0
...
1970-01-01 00:16:39 124998.0
1970-01-01 00:16:39 125265.0
1970-01-01 00:16:39 126050.0
1970-01-01 00:16:39 126782.0
1970-01-01 00:16:39 127766.0
Name: data, Length: 100000, dtype: float64
It only took 0.31s to complete the task. However, what I need is group by id and perform rolling on time. So here is my goal:
df.groupby("id").rolling("2500ms",on="time")["data"].sum()
id time
0 1970-01-01 00:05:43 161.0
1970-01-01 00:12:05 288.0
1970-01-01 00:00:35 981.0
1970-01-01 00:00:22 479.0
1970-01-01 00:09:33 834.0
...
999 1970-01-01 00:15:07 28159.0
1970-01-01 00:06:39 29035.0
1970-01-01 00:06:15 29538.0
1970-01-01 00:09:07 29788.0
1970-01-01 00:00:48 30736.0
Name: data, Length: 100000, dtype: float64
But it took 2s to complete the task. I wanted to know if there is much more efficient to do this.
I thought presorting may be helpful, so I tried:
df = df.set_index(["id","time"]).sort_index()
This is very fast too. After this, we just need to cut our dataframe into multiple small frame and do rolling sum and concat them together... so I tried
df.groupby(level=0).rolling("2500ms")
but it gives me this error
> ValueError: window must be an integer
so i tried:
df.groupby(level=0).rolling("2500ms",on=df.index.levels[1]).sum()
this breaks my Jupiter notebook, giving me kernel dead error.
So I tried
df_tmp_list = []
for j in df.index.unique(level=0):
df_tmp = df.loc[j]
df_tmp = df_tmp.rolling("2500ms").sum()
df_tmp["id"] = j
df_tmp_list.append(df_tmp)
# break
pd.concat(df_tmp_list).reset_index().set_index(["id","time"])
This also took 2.6s... What would be the most efficient way for me to achieve?
df.groupby("id").rolling("2500ms",on="time")["data"].sum()
**Edit**
Thanks everyone for answering. It seems everyone runs it much faster than I do. I need 2s, others need 50ms... to be exactly clear, the complete code is:
import pandas as pd
import numpy as np
size_now = 1e5
df = pd.DataFrame({"time":np.random.randint(1000,size=int(size_now)),
"id":np.random.randint(1000,size=int(size_now)),
"data":np.random.randint(1000,size=int(size_now))})
df.time = pd.to_datetime(df.time,unit="s")
%time df.groupby("id").rolling("2500ms",on="time")["data"].sum()
my output is
CPU times: user 2.3 s, sys: 24 ms, total: 2.32 s
Wall time: 2.32 s
I am actually running on AWS-EC2 with 4 cpus and 16GB RAM.I have tried to update packages:
510 conda update pandas
511 conda update numpy
so speed didn't get improved... Any help?I'm very frustrated...
I tried on my MacBook Pro, with 2.8 GHz Quad-Core Intel Core i7 and 16 GB 1600 MHz DDR3. It took 450ms... still much slower than all of you guys.... Please help me...
```
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```
df = pd.DataFrame({'a' : [1], 'b' : [2.], 'c' : ['3']})
res = df.groupby([0]).transform(lambda x: x.max() - x.min())
print(res)
df = pd.DataFrame({'a' : [1], 'b' : [2.]})
res = df.groupby([0]).transform(lambda x: x.max() - x.min())
print(res)
```
gives
```
a b
0 0 0.0
a b
0 0.0 0.0
```
Note that in the first result, the dtype of a starts and ends as integer, whereas in the 2nd, the dtype starts as integer and ends as float.
When the object column (c) is on the frame, we call `_transform_item_by_item` which operates on each column individually, giving the expected dtype in the result. Without the object column, we take the slowpath calling apply. apply returns a Series whose index is ['a', 'b'] with values [0.0, 0.0], where the dtype of column b coerces the dtype of column a into the final result of all floats.
The first example above is deprecated (will raise in a future version), but the second example will still be valid and result in the wrong dtype. | {
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} | 5 | "2021-07-19T21:46:34Z" | "2021-07-24T03:29:22Z" | "2021-07-24T03:29:22Z" | 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
pd.DataFrame([[0, 1]], columns=('a', 'b')).groupby('a')['b'].value_counts()
```
```python-traceback
IndexError Traceback (most recent call last)
<ipython-input-53-ab16326ac16f> in <module>
----> 1 pd.DataFrame([[0,1]], columns=('a', 'b')).groupby('a')['b'].value_counts()
~/.venvs/python3/lib/python3.9/site-packages/pandas/core/groupby/generic.py in value_counts(self, normalize, sort, ascending, bins, dropna)
762 if not len(lchanges):
763 inc = lchanges
--> 764 inc[idx] = True # group boundaries are also new values
765 out = np.diff(np.nonzero(np.r_[inc, True])[0]) # value counts
766
IndexError: index 0 is out of bounds for axis 0 with size 0
```
#### Problem description
Calling `value_counts()` on a `SeriesGroupBy` throws an `IndexError` when there is only one group.
#### Expected Output
```python
a b
0 1 1
Name: b, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.23.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
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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} | 1 | "2021-07-20T02:10:07Z" | "2021-08-06T13:29:45Z" | "2021-08-06T04:44:17Z" | 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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