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BUG: rolling aggregations not checking monotonic index when grouping by
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (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
import numpy as np
df = pd.DataFrame({'a':['2020-06-01 12:00',
'2020-06-01 12:30',
'2020-06-01 13:00',
'2020-06-01 14:00',
np.nan],
'b':[1,2,3,4,5],
'c':[1,1,1,1,1]})
df.a = pd.to_datetime(df.a)
df.set_index('a',inplace=True)
df.groupby('c').b.rolling('60min').sum()
'''
Note that if there's no group by, ie:
df.b.rolling('60min').sum()
index monotonicity is checked and an error is reported due to the NaT.
''''
```
#### Problem description
When using a rolling window on a timestamp index + a group by, there is no validation about the monotonicity of the index. This can lead to unexpected behaviours, as the value of the calculation can change if the index is not monotonic.
#### Expected Output
Output:
c a
1 2020-06-01 12:00:00 1.0
2020-06-01 12:30:00 3.0
2020-06-01 13:00:00 6.0
2020-06-01 14:00:00 10.0
NaT 15.0
Expected (correct) output:
c a
1 2020-06-01 12:00:00 1.0
2020-06-01 12:30:00 3.0
2020-06-01 13:00:00 5.0
2020-06-01 14:00:00 4.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.0
pytz : 2019.1
dateutil : 2.7.5
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 4.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.1.1
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : 1.2.16
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MzI0NzIzMTk=
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BUG: Float values get corrupted with df.astype(), for values with no overflow error
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| 5
| 2020-06-06T13:44:32Z
| 2020-06-08T00:09:35Z
| 2020-06-08T00:09:34Z
|
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
# Your code here
df = pd.DataFrame({'a': [1, 2, 3, 4, 5], 'b': [1270., 570., 14130., 620., 29910.]})
df['b'] = df['b'].astype('float16')
df
```
#### Problem description
Converting a 'float64' to 'float16' should return the original values, as long as they don't cause any overflow. In the example above, two values (14130. and 29910.) get corrupted to (14128. and 29904.) respectively. Both those values can fit into a 'float16' without overflow, which can be confirmed via np.finfo(np.float16).min < 29910. < np.finfo(np.float16).max
#### Expected Output
The values (14130. and 29910.) should be preserved.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 47.1.1
Cython : 0.29.19
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.8.1
bs4 : 4.6.3
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.48.0
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DOC: docstring, closes #23475
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| 1
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MEMBER
| null |
- [x] closes #23475
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MzI1Nzk5OTA=
| 34,620
|
BUG: blosc:snappy doesn't seem to be supported with blosc=1.19.0
|
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[] | null | 1
| 2020-06-06T16:42:56Z
| 2020-08-21T17:05:14Z
| 2020-06-06T17:45:00Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
The following example works with blosc=1.18.1 but fails with the latest blosc (1.19.0)
conda install pandas pytables blosc=1.18.1 # works
conda install pandas pytables blosc=1.19.0 # leads to failure
```python
import numpy as np
import pandas as pd
pd.DataFrame(np.random.rand(10, 3)).to_hdf("test.hdf", "df", complib="blosc:snappy")
```
#### Problem description
The error message is
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/twoertwe/miniforge3/envs/test/lib/python3.8/site-packages/pandas/core/generic.py", line 2490, in to_hdf
pytables.to_hdf(
File "/home/twoertwe/miniforge3/envs/test/lib/python3.8/site-packages/pandas/io/pytables.py", line 279, in to_hdf
with HDFStore(
File "/home/twoertwe/miniforge3/envs/test/lib/python3.8/site-packages/pandas/io/pytables.py", line 521, in __init__
raise ValueError(
ValueError: complib only supports ['zlib', 'lzo', 'bzip2', 'blosc', 'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:zlib', 'blosc:zstd'] compression.
```
The change log of blosc doesn't mentioned snappy: https://github.com/Blosc/c-blosc/releases
#### Expected Output
The compression should either work or `to_hdf` should mention that `blosc:snappy` doesn't work with `blosc=1.19.0`.
#### Output of ``pd.show_versions()``
<details>
I installed the latest blosc as above but for some reason ``pd.show_versions()`` doesn't find it.
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.5.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1.post20200529
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
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MzI1ODM0MjA=
| 34,621
|
BUG: Period constructor does not take into account nanonseconds
|
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| 6
| 2020-06-06T16:48:21Z
| 2020-12-02T12:12:18Z
| 2020-12-02T12:12:18Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example:
```python
print(pd.Period("2000-12-31 23:59:59.000000999").to_timestamp().nanosecond)
print(pd.Period("2000-12-31 23:59:59.000000500").to_timestamp().nanosecond)
print(pd.Period("2000-12-31 23:59:59.000000001").to_timestamp().nanosecond)
print(pd.Period("2000-12-31 23:59:59.000000001").to_timestamp(freq='ns').nanosecond)
```
output:
```python
0
0
0
0
```
#### Problem description
Nanoseconds are set to `0` when using `to_timestamp` function.
#### Expected Output
```python
999
500
1
1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c71bfc36211b5e2d860a06d8fbef902b757bd6e4
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-33-generic
Version : #37-Ubuntu SMP Thu May 21 12:53:59 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.1.0.dev0+1767.gc71bfc362
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.4.2
hypothesis : 5.16.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI5MzY5Mjk0
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TST: boolean indexing using .iloc #20627
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| 4
| 2020-06-06T18:24:00Z
| 2021-01-02T08:31:59Z
| 2020-06-13T19:58:21Z
|
CONTRIBUTOR
| null |
- [x] closes #20627
- [1] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
A simple test was added to close this issue.
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TST/REF: arithmetic tests for BooleanArray + consolidate with integer masked tests
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| 2020-06-14T15:18:13Z
| 2020-06-14T15:18:10Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/34454
- Move a bunch of tests from `integer/test_arithmetic.py` to `masked/test_arithmetic.py` so those can be shared with boolean (and once merged I can add float as well in the FloatingArray PR)
- Added some more arithmetic tests to `boolean/test_arithmetic.py` to follow the same pattern as we have in `integer/test_arithmetic.py`.
- The commong integer/boolean tests uncovered a bug in the BooleanArray implementation when dealing with a `pd.NA` scalar as other operand.
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DOC: updated core/arrays/base.py for PR08 errors
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MDExOlB1bGxSZXF1ZXN0NDI5NDgzNTc5
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REF: re-use existing conversion functions
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BUG: s3 reads from public buckets not working
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| 9
| 2020-06-07T07:22:00Z
| 2020-07-15T19:07:52Z
| 2020-07-15T19:07:52Z
|
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
# Your code here
import pandas as pd
df = pd.read_csv("s3://nyc-tlc/trip data/yellow_tripdata_2019-01.csv")
```
<details>
<summary> Error stack trace </summary>
<pre>
Traceback (most recent call last):
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/s3.py", line 33, in get_file_and_filesystem
file = fs.open(_strip_schema(filepath_or_buffer), mode)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/fsspec/spec.py", line 775, in open
**kwargs
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 378, in _open
autocommit=autocommit, requester_pays=requester_pays)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 1097, in __init__
cache_type=cache_type)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/fsspec/spec.py", line 1065, in __init__
self.details = fs.info(path)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 530, in info
Key=key, **version_id_kw(version_id), **self.req_kw)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 200, in _call_s3
return method(**additional_kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 622, in _make_api_call
operation_model, request_dict, request_context)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 641, in _make_request
return self._endpoint.make_request(operation_model, request_dict)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 102, in make_request
return self._send_request(request_dict, operation_model)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 132, in _send_request
request = self.create_request(request_dict, operation_model)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in create_request
operation_name=operation_model.name)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 356, in emit
return self._emitter.emit(aliased_event_name, **kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 228, in emit
return self._emit(event_name, kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 211, in _emit
response = handler(**kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/signers.py", line 90, in handler
return self.sign(operation_name, request)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/signers.py", line 160, in sign
auth.add_auth(request)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/auth.py", line 357, in add_auth
raise NoCredentialsError
botocore.exceptions.NoCredentialsError: Unable to locate credentials
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/parsers.py", line 431, in _read
filepath_or_buffer, encoding, compression
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/common.py", line 212, in get_filepath_or_buffer
filepath_or_buffer, encoding=encoding, compression=compression, mode=mode
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/s3.py", line 52, in get_filepath_or_buffer
file, _fs = get_file_and_filesystem(filepath_or_buffer, mode=mode)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/pandas/io/s3.py", line 42, in get_file_and_filesystem
file = fs.open(_strip_schema(filepath_or_buffer), mode)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/fsspec/spec.py", line 775, in open
**kwargs
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 378, in _open
autocommit=autocommit, requester_pays=requester_pays)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 1097, in __init__
cache_type=cache_type)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/fsspec/spec.py", line 1065, in __init__
self.details = fs.info(path)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 530, in info
Key=key, **version_id_kw(version_id), **self.req_kw)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/s3fs/core.py", line 200, in _call_s3
return method(**additional_kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 622, in _make_api_call
operation_model, request_dict, request_context)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/client.py", line 641, in _make_request
return self._endpoint.make_request(operation_model, request_dict)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 102, in make_request
return self._send_request(request_dict, operation_model)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 132, in _send_request
request = self.create_request(request_dict, operation_model)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in create_request
operation_name=operation_model.name)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 356, in emit
return self._emitter.emit(aliased_event_name, **kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 228, in emit
return self._emit(event_name, kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/hooks.py", line 211, in _emit
response = handler(**kwargs)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/signers.py", line 90, in handler
return self.sign(operation_name, request)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/signers.py", line 160, in sign
auth.add_auth(request)
File "/home/conda/envs/pandas-test/lib/python3.7/site-packages/botocore/auth.py", line 357, in add_auth
raise NoCredentialsError
</pre>
</details>
#### Problem description
Reading directly from s3 public buckets (without manually configuring the `anon` parameter via s3fs) is broken with pandas 1.0.4 (worked with 1.0.3).
Looks like reading from public buckets requires `anon=True` while creating the filesystem. This 22cf0f5dfcfbddd5506fdaf260e485bff1b88ef1 seems to have introduced the issue, where `anon=False` is passed when the `noCredentialsError` is encountered.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-55-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
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
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI5ODgwNDMz
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TST #24444 added tests
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| 2
| 2020-06-07T08:53:04Z
| 2020-06-07T18:53:01Z
| 2020-06-07T17:19:57Z
|
CONTRIBUTOR
| null |
At last, there are no issues at all. I've made a new PR.
- [x] closes #24444
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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WIP: avoid internals for Series.interpolate
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MEMBER
| null |
This may be premature while discussion about removing the block manager is ongoing
most of the diff is type annotations and collecting the method validation. will break off into pre-cursor PRs
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QST: How to plot hist not in stacked style for different columns by df.plot.hist()?
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| 2020-06-09T20:29:26Z
|
NONE
| null |
- I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
When using pd.plot.hist(), how to plot different columns in the parallel style(1st pic below), rather than in the stacked style(2nd pic below)?
Thanks,
Alex
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
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TYP: some type annotations in core\tools\datetimes.py
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TYP: some type annotations for interpolate
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| 2020-06-08T08:32:06Z
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MEMBER
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pre-cursor to #34628
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|
Revert backport of #33632: Parquet & s3 I/O changes
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| 1
| 2020-06-07T15:43:26Z
| 2020-06-09T07:36:51Z
| 2020-06-09T07:36:51Z
|
CONTRIBUTOR
| null |
Ref: https://github.com/pandas-dev/pandas/issues/34626#issuecomment-640225867
Revert backport of https://github.com/pandas-dev/pandas/pull/33632
Backport: https://github.com/pandas-dev/pandas/pull/34173
cc @jorisvandenbossche @simonjayhawkins
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MDU6SXNzdWU2MzM1OTYyNjU=
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BUG: to_timedelta() works differently depending on type of arg for the same unit
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| 3
| 2020-06-07T17:24:18Z
| 2021-07-11T20:15:45Z
| 2021-07-11T20:15:33Z
|
NONE
| null |
- [X ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
<Timestamp object> += pd.to_timedelta(16, unit='hours') , OR
<Timestamp object> += pd.to_timedelta("16", unit='hours')
```
#### Problem description
<Timestamp object> += pd.to_timedelta(16, unit='hours') will add correctly to hours
<Timestamp object> += pd.to_timedelta("16", unit='hours') will add to milliseconds
As per documentation, arg=string should work for the specified unit.
#### Expected Output
opt_expiry_dt += pd.to_timedelta("16", unit='hours'), gives:
2020-06-12 00:00:00.000000016
opt_expiry_dt += pd.to_timedelta(16, unit='hours'), gives:
2020-06-19 16:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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BUG/API: Disallow unit if input to Timedelta and to_timedelta is/contains str
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| 2020-06-08T22:10:15Z
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|
CONTRIBUTOR
| null |
- [x] closes #12136
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU2MzM5NzE2Mzk=
| 34,635
|
BUG: test_eval.TestMathNumExprPandas.test_result_complex128 failing on OSX
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| 2020-06-08T01:41:58Z
| 2020-08-06T17:55:42Z
| 2020-08-06T17:55:42Z
|
MEMBER
| null |
intermittently
OSX 10.15.5
python 3.7.6
numpy 1.18.5
numexpr 2.7.1
```
________________________________________________________________________________ TestMathNumExprPandas.test_result_complex128 _________________________________________________________________________________
self = <pandas.tests.computation.test_eval.TestMathNumExprPandas object at 0x7fc0ec87de50>
@td.skip_if_windows
def test_result_complex128(self):
# xref https://github.com/pandas-dev/pandas/issues/12293
# this fails on Windows, apparently a floating point precision issue
# Did not test complex64 because DataFrame is converting it to
# complex128. Due to https://github.com/pandas-dev/pandas/issues/10952
> self.check_result_type(np.complex128, np.complex128)
pandas/tests/computation/test_eval.py:1832:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/computation/test_eval.py:1817: in check_result_type
tm.assert_series_equal(got, expect, check_names=False)
pandas/_libs/testing.pyx:68: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (20.0 %)
E [index]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
E [left]: [(0.771519184640353+0j), (-0.7269031850139311-0j), (0.995153744613331+0j), (-0.08814548225306257+0j), (0.9413908236833275+0j), (0.9482003101223208+0j), (-0.9535450271705115+0j), (-0.27032459595133673+0j), (0.011064344419221385+0j), (-0.8916020886333388+0j)]
E [right]: [(0.771519184640353+0j), (-0.7269031850139313+0j), (0.995153744613331+0j), (-0.08814548225306257+0j), (0.9413908236833275+0j), (0.9482003101223209+0j), (-0.9535450271705115+0j), (-0.27032459595133673+0j), (0.011064344419221385+0j), (-0.8916020886333388+0j)]
pandas/_libs/testing.pyx:183: AssertionError
```
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MDExOlB1bGxSZXF1ZXN0NDMwODM1MzQ1
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CLN: EWMA cython code and function dispatch
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MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH: specify function to determine NaN when using fillna()
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| 2020-09-04T13:47:45Z
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|
NONE
| null |
#### Is your feature request related to a problem?
When using `pd.fillna(df)` it does not fill all of the values that `pd.isna(df)` returns as `True`. Furthermore you might have some `NaN` values you want to fill, and some you don't
#### Describe the solution you'd like
include a `nan_function` to be parsed to `fillna()`. The `nan_function` should take a value (or row, or Series) as input and return True/False. Based on that `fillna` determines which values to replace.
```python
#examples
df = df.fillna(nan_function = pd.isnull, values = np.nan) #What pd.isnull returns as True is set to np.nan
df = df.fillna(nan_function = lambda x: x<0, values = np.nan) #Makes all negative numbers np.nan
df = df.fillna(nan_function = np.nan, values = 0) #Only fills np.nan with zeroes
```
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CLN: deduplicate in core.internals.blocks.interpolate
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MEMBER
| null |
broken off #34628
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MDU6SXNzdWU2MzQ0MDQ0MDU=
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|
ENH: Add max_results to `read_gbq`
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| 1
| 2020-06-08T09:03:57Z
| 2020-06-09T22:17:20Z
| 2020-06-09T22:17:20Z
|
MEMBER
| null |
For queries for which the purpose is to create/replace tables: `CREATE OR REPLACE TABLE`, if the table is huge, it will eat up a lot of memory currently using `read_gbq`.
And it turns out that since `pandas-gbq==0.12.0`, a new argument `max_results` was implemented to limit the number of rows in result dataframe for e.g. setting it to `0` for DDL queries, which will help save memory.
So it will be nice for pandas to add it for `read_gbq` as `pandas-gbq` does.
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MDU6SXNzdWU2MzQ0MzAxMDA=
| 34,640
|
CLN: remove `private_key` and `verbose` from gbq
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| 4
| 2020-06-08T09:23:21Z
| 2020-09-24T01:34:43Z
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|
MEMBER
| null |
Since `verbose` and `private_key` have been deprecated since https://github.com/pandas-dev/pandas/pull/30200, we should also remove both from function since they are not used at all? see them in `read_gbq` and `to_gbq`, e.g.
https://github.com/pandas-dev/pandas/blob/24857a2844be9ee6f604d93ea18301f587751f0a/pandas/io/gbq.py#L33
https://github.com/pandas-dev/pandas/blob/24857a2844be9ee6f604d93ea18301f587751f0a/pandas/io/gbq.py#L34
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ENH: Add max_results kwarg to read_gbq
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| 2
| 2020-06-08T11:53:34Z
| 2020-06-09T22:17:24Z
| 2020-06-09T22:17:20Z
|
CONTRIBUTOR
| null |
Adds support for the new `max_results` kwarg from pandas-gbq (added in [0.12.0](https://github.com/pydata/pandas-gbq/releases/tag/0.12.0)). Since `max_results` is a new kwarg, it is handled and tested in the same way as the `use_bqstorage_api` kwarg to maintain backwards compatibility.
❓ **Open question:** Setting `max_results=0` causes `pandas_gbq.read_gbq` to return `None` instead of a DataFrame. I've kept this behaviour the same in this PR, but maintainers may prefer to always return a (empty) DataFrame instead of `None`.
- [x] closes #34639
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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[] | null | 4
| 2020-06-08T12:23:27Z
| 2020-10-05T01:24:18Z
| null |
CONTRIBUTOR
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
I had a hard time understanding how [`df.rolling`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html) works when `df` is indexed by a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.html)
This is an example data frame:
```python
import pandas as pd
idx = pd.MultiIndex.from_product(
[pd.date_range("2020-01-01", "2020-1-10"), ["a", "b"]], names=["date", "obs"],
)
df = pd.DataFrame(index=idx)
df['c1'] = range(len(df))
print(df)
```
which outputs
```
c1
date obs
2020-01-01 a 0
b 1
2020-01-02 a 2
b 3
2020-01-03 a 4
b 5
2020-01-04 a 6
b 7
2020-01-05 a 8
b 9
2020-01-06 a 10
b 11
2020-01-07 a 12
b 13
2020-01-08 a 14
b 15
2020-01-09 a 16
b 17
2020-01-10 a 18
b 19
```
Now I want to apply a rolling window on the `date` level, keeping the `obs` level separate.
I tried with no success obvious and simple (least surprise) commands like
- `df.rolling("7d", index="date")` or
- `df.rolling("7d", on="date")`
but finally the desired result is obtained by
```
df_r = df.groupby(by="obs", group_keys=False).rolling(
"7d", on=df.index.levels[0]
).mean().sort_index()
print(df_r)
```
which gives me the correct result:
```
c1
date obs
2020-01-01 a 0.0
b 1.0
2020-01-02 a 1.0
b 2.0
2020-01-03 a 2.0
b 3.0
2020-01-04 a 3.0
b 4.0
2020-01-05 a 4.0
b 5.0
2020-01-06 a 5.0
b 6.0
2020-01-07 a 6.0
b 7.0
2020-01-08 a 8.0
b 9.0
2020-01-09 a 10.0
b 11.0
2020-01-10 a 12.0
b 13.0
```
It seams to me that this should be a quite common situation, so I was wondering if there is a simpler way to obtain the same results. By the way my solution is not very robust, because there are hidden assumptions on how the objects returned by `groupby` are indexed, which do not necessarily hold for a generic data frame.
Moreover the doc of the `on` parameter in `rolling` was almost incomprehensible to me: I'm still wondering if my usage `rolling( "7d", on=df.index.levels[0])` is the intended one or not.
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|
BUG: Writing Interval columns to Parquet fails
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| 4
| 2020-06-08T13:57:53Z
| 2020-12-16T08:22:28Z
| 2020-12-16T08:22:20Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"x": [pd.Interval(0, 1), ]})
df.to_parquet("test.parquet")
```
#### Problem description
Saving fails with:
```
---------------------------------------------------------------------------
ArrowException Traceback (most recent call last)
<ipython-input-2-f61461018f44> in <module>
1 import pandas as pd
2 df = pd.DataFrame({"x": [pd.Interval(0, 1), ]})
----> 3 df.to_parquet("~/test2.parquet")
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
212 else:
213 kwargs[new_arg_name] = new_arg_value
--> 214 return func(*args, **kwargs)
215
216 return cast(F, wrapper)
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pandas/core/frame.py in to_parquet(self, path, engine, compression, index, partition_cols, **kwargs)
2114 index=index,
2115 partition_cols=partition_cols,
-> 2116 **kwargs,
2117 )
2118
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pandas/io/parquet.py in to_parquet(df, path, engine, compression, index, partition_cols, **kwargs)
267 index=index,
268 partition_cols=partition_cols,
--> 269 **kwargs,
270 )
271
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pandas/io/parquet.py in write(self, df, path, compression, coerce_timestamps, index, partition_cols, **kwargs)
122 compression=compression,
123 coerce_timestamps=coerce_timestamps,
--> 124 **kwargs,
125 )
126 if should_close:
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pyarrow/parquet.py in write_table(table, where, row_group_size, version, use_dictionary, compression, write_statistics, use_deprecated_int96_timestamps, coerce_timestamps, allow_truncated_timestamps, data_page_size, flavor, filesystem, compression_level, use_byte_stream_split, data_page_version, **kwargs)
1620 data_page_version=data_page_version,
1621 **kwargs) as writer:
-> 1622 writer.write_table(table, row_group_size=row_group_size)
1623 except Exception:
1624 if _is_path_like(where):
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pyarrow/parquet.py in write_table(self, table, row_group_size)
590 raise ValueError(msg)
591
--> 592 self.writer.write_table(table, row_group_size=row_group_size)
593
594 def close(self):
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pyarrow/_parquet.pyx in pyarrow._parquet.ParquetWriter.write_table()
/opt/modules/anaconda/envs/env3/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowException: Unknown error: data type leaf_count != builder_leaf_count1 2
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.11-1.el7.elrepo.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
```
</details>
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MDU6SXNzdWU2MzQ2NDg3NTE=
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|
ENH: Support for list of intervals / IntervalArray in to_parquet
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open
| false
| null |
[] | null | 0
| 2020-06-08T14:07:09Z
| 2020-06-08T23:03:03Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I would like to be able to save the following dataframes:
- list of intervals:
```python
import pandas as pd
df = pd.DataFrame({"x": [[pd.Interval(0, 1), pd.Interval(3, 4)]]})
df.to_parquet("test.parquet")
```
- IntervalArray
```python
import pandas as pd
df = pd.DataFrame({"x": [pd.arrays.IntervalArray.from_tuples([(0, 1), (2, 3)]), ]})
df.to_parquet("test.parquet")
```
However, currently both cases fail with:
```
pyarrow.lib.ArrowInvalid: ('Could not convert (0, 1] with type pandas._libs.interval.Interval: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column x with type object')
pyarrow.lib.ArrowInvalid: ('Could not convert <IntervalArray>\n[(0, 1], (2, 3]]\nLength: 2, closed: right, dtype: interval[int64] with type IntervalArray: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column x with type object')
```
#### Describe the solution you'd like
[this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature]
#### API breaking implications
None
#### pd.show_versions()
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.14-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.4
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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BUG: TypeError: object of type 'int' has no len() when saving DataFrame with object dtype column
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[] | null | 1
| 2020-06-08T15:17:17Z
| 2020-06-19T15:05:51Z
| 2020-06-17T18:09:21Z
|
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": [None, None]})
df.loc[0, "a"] = float(1)
df.loc[1, "a"] = float(2)
hdf = pd.HDFStore("test.h5", write_mode="w")
hdf.put("table", df, format="table")
```
This causes following error:
```
...
File "/opt/venv/lib64/python3.6/site-packages/pandas/io/pytables.py", line 1042, in put
errors=errors,
File "/opt/venv/lib64/python3.6/site-packages/pandas/io/pytables.py", line 1709, in _write_to_group
data_columns=data_columns,
File "/opt/venv/lib64/python3.6/site-packages/pandas/io/pytables.py", line 4143, in write
data_columns=data_columns,
File "/opt/venv/lib64/python3.6/site-packages/pandas/io/pytables.py", line 3813, in _create_axes
errors=self.errors,
File "/opt/venv/lib64/python3.6/site-packages/pandas/io/pytables.py", line 4800, in _maybe_convert_for_string_atom
for i in range(len(block.shape[0])):
TypeError: object of type 'int' has no len()
```
#### Problem description
After initial creation of DataFrame the dtype is of `object` dtype. After putting float in the `a` column I would expect that the dtype of the `a` column will change to `float64` dtype, but it remains `object` dtype. The problem is that the type of `df.loc[0, "a"]` is `float` during saving the DataFrame, which causes the problem pasted above.
#### Expected Output
I would expect one of the following:
- Implicit conversion of the column to `float` dtype
- Conversion during `hdf.put()`
- Proper exception saying that I am saving mixed typed column
There's a pretty big chance that I am wrong and this is expected behaviour. If that's the case, please, can you explain me why, or point me to somewhere, so that I can read something about it?
Maybe it's linked with this issue #34274
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-147.5.1.el8_1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.16.4
pytz : 2018.7
dateutil : 2.8.1
pip : 19.3.1
setuptools : 46.4.0
Cython : 0.29.2
pytest : 5.1.2
hypothesis : None
sphinx : 1.8.4
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 2.0.0
numexpr : 2.6.8
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.1.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.2
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDMxMjIwNzUz
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DOC: updated io/sql.py for PR08 errors
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TST: groupby apply with indexing and column aggregation returns the column #7002
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MEMBER
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Adds test for #7002
- [x] closes #7002
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: de-duplicate month/year rolling in libperiod
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REF: simplify libperiod.get_yq
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June 2020 Meeting
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| 2020-06-12T20:18:24Z
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CONTRIBUTOR
| null |
The monthly dev meeting is Wednesday June 10th, at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
All are welcome to attend.
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BUG: df.groupby().apply() returns an empty df instead of a None-only Series
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| 2021-08-07T22:51:56Z
| 2021-08-07T22:51:56Z
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NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': (1, 2, 2)})
# The problem
df.groupby('a').apply(lambda x: None)
# The problem with a bit more context
df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 0 else None)
df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 1 else None)
df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 2 else None)
```
#### Problem description
The first and last calls to apply in the above code each return an empty DataFrame.
Instead, they should behave like the other two calls to apply, i.e., return a Series that contains the values returned by the call to the lambda function on each group.
It seems to me that the trigger to the bug is that the applied function returns None for all groups.
#### Expected Output
```python
>>> df.groupby('a').apply(lambda x: None) # The output is actually an empty df
a
1 None
2 None
>>> # The problem with a bit more context
>>> df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 0 else None)
a
1 foo
2 foo
dtype: object
>>> df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 1 else None)
a
1 None
2 foo
dtype: object
>>> df.groupby('a').apply(lambda group_df: 'foo' if len(group_df) > 2 else None) # The output is actually an empty df
a
1 None
2 None
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-101-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.1
</details>
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| 8
| 2020-06-08T19:39:16Z
| 2020-09-24T01:34:48Z
| 2020-09-24T01:34:44Z
|
CONTRIBUTOR
| null |
- [x ] closes #34640
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Hello, I am new to contributing to open source, so thank you in advance for any corrections and suggestions. Please let me know if I need to do anything else for this ticket!
I removed the code containing both 'private_key' and 'verbose' from the gbq.py file.
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read_csv cannot use dtype and true_values/false_values
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| 2
| 2020-06-08T20:00:40Z
| 2021-01-09T22:18:23Z
| 2021-01-09T22:18:22Z
|
NONE
| null |
Hi friends,
Not sure if this is working as intended, but it appears you cannot use both dtype and true_values or false_values kwargs when reading csv.
```python
from io import StringIO
from csv import writer
import pandas as pd
import pytest
def test_pandas_read_write():
df = pd.DataFrame({'A': ['yes', 'no'], 'B': ['yes', 'no']})
out_io = StringIO(newline='')
df.to_csv(out_io, index=False)
out_io.seek(0)
pd.read_csv(out_io)
out_io.seek(0)
kwargs = dict(dtype={'A': 'boolean', 'B': 'boolean'})
with pytest.raises(ValueError):
pd.read_csv(out_io, **kwargs)
kwargs.update({'true_values': ['yes'], 'false_values': ['no']})
out_io.seek(0)
with pytest.raises(ValueError):
pd.read_csv(out_io, **kwargs)
out_io.seek(0)
# pop dtype so true/false values work
kwargs.pop('dtype')
pd.read_csv(out_io, **kwargs)
```
Using converters kwarg to get the data how I like will fix my problem, but we may want to update the docs to let users know true/false user defined values will not work in conjugation with providing the boolean type.
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BUG: groupby.min has a side effect on groupby.apply
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| 7
| 2020-06-08T22:18:35Z
| 2020-08-07T16:56:13Z
| 2020-08-07T16:56:13Z
|
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(
{
"col1": [0, 1, 2, 3],
"col4": [17, 13, 16, 15],
"col5": [-4, -5, -6, -7],
}
)
by=["col4", "col5"]
apply_function = min
gb = df.groupby(by, as_index=True)
df1 = gb.apply(apply_function)
print(df1)
df2 = gb.min()
print(df2)
df3 = gb.apply(apply_function)
print(df3)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
In the code above two calls to `gb.apply(apply_function)` produce different output. The reason for this is that `groupby.min` is called before 2nd `apply` and makes its output different and incorrect.
#### Expected Output
Expected that both calls to `gb.apply(apply_function)` produce the same output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2019.2
dateutil : 2.7.3
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.17
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : 0.13.2
pyarrow : 0.16.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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PERF: don't consolidate at __init__
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MEMBER
| null |
xref mailing list [discussion](https://mail.python.org/pipermail/pandas-dev/2020-June/001246.html)
ATM most calls to `DataFrame(foo)` will end up going through either `core.internals.managers.create_manager_from_blocks` or `core.internals.managers.create_block_manager_from_arrays`, both of which call `mgr._consolidate_inplace()` before returning a `BlockManager` object. I think we should consider disabling this consolidation. (There is also consolidation-by-another-name within form_blocks that im still tracking down)
Behavior-wise, consider:
```
arr = np.random.randn(10)
ser = pd.Series(arr)
df = pd.DataFrame({"A": arr, "B": ser})
```
ATM we get a new 2x10 array backing `df`. The proposed change would mean that we keep 2 separate arrays, and `df["B"]` shares data with `ser`. I find the proposed behavior more intuitive.
Performance-wise, we expect faster construction but slower subsequent operations. [note to self: get some estimates/measurements here]. For long-lived DataFrames where the subsequent slowdowns add up, we can suggest users call `_consolidate()` (which i guess we would have to de-privatize)
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MDExOlB1bGxSZXF1ZXN0NDMxNDMyNDk5
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CLN: disallow passing tuples for Period freq
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| 2
| 2020-06-08T22:35:33Z
| 2020-06-09T01:57:10Z
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MEMBER
| null |
Really looking forward to killing off `get_freq_code`
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REF: avoid get_freq_code
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| 1
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MEMBER
| null |
We'll be able to rip it out entirely after this, #34658, and #34587.
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BUG: ExtensionArray.equals expects ExtensionArray.all to return a numpy.bool_
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| 0
| 2020-06-09T09:20:44Z
| 2020-06-09T15:37:45Z
| 2020-06-09T15:37:45Z
|
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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
The current implementation of `ExtensionArray.equals` expects that the implementation of `ExtensionArray.all` always returns a numpy scalar, see https://github.com/pandas-dev/pandas/blob/942beba1e8f49c931b7c501bb1eec0e383313ac7/pandas/core/arrays/base.py#L741. If you don't use `numpy` as the backing memory store, I don't it is reasonable to stick to NumPy scalars here.
Instead, the method should unpack `numpy` scalars if necessary but passthrough otherwise.
Context: This appeared while running the unit tests with `fletcher` and `pandas`' master.
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BUG: Allow plain bools in ExtensionArray.equals
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| 1
| 2020-06-09T09:28:51Z
| 2020-06-09T16:50:12Z
| 2020-06-09T15:37:45Z
|
CONTRIBUTOR
| null |
- [x] closes #34660
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU2MzUzMDg4NTM=
| 34,662
|
BUG: read_excel with dtype=np.int32 can't handle empty or nan cells
|
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[] | null | 1
| 2020-06-09T10:27:06Z
| 2020-09-04T13:49:05Z
| 2020-09-04T13:49:05Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
Though there's some similar issues, like https://github.com/pandas-dev/pandas/issues/20377
- [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
**EDIT:**
(won't let me comment)
Apparently, it's possible with `dtype='Int64'` (capital `I` and in ticks).
https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support
Unfortunately, that's not very well documented. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html makes it sound like you're supposed to give it a numpy type.
So it's more of a documentation issue, instead of a bug
**/EDIT**
```python
# save the lines below as test.xls:
ID string
1 a
2 b
3 c
d
4 e
nan f
6 g
# code:
test = pd.read_excel('test.xls')
test
ID string
0 1.0 a
1 2.0 b
2 3.0 c
3 NaN d
4 4.0 e
5 NaN f
6 6.0 g
# note that the IDs gained a decimal! They're needed as an ID to retrive some files, to that 0 is breaking things. So let's import `ID` as integer:
test = pd.read_excel('test.xls', dtype={'ID':np.int32})
ValueError Traceback (most recent call last)
...
ValueError: Cannot convert non-finite values (NA or inf) to integer
...
ValueError: Unable to convert column ID to type <class 'numpy.int32'>
# similar things happen with the nan cell.
```
#### Problem description
Importing an excel file with `nan` or ` ` in a column of integers fails, when specifying this column as `dtype=np.int32`
#### Expected Output
setting `nan` or ` ` as `np.nan` or similar or having a documented workaround in the documentation. `na_filter=False` seems promising, but doesn't do anything.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-147.8.1.el8_1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.5.4
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.1
</details>
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DEPR: behavior of `copy` argument in `df.reindex` is confusing
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[] | null | 4
| 2020-06-09T10:42:16Z
| 2021-11-10T09:41:00Z
| null |
MEMBER
| null |
This was inspired by #33888 and #34584
#### Problem description
The behavior of `copy` argument in `df.reindex` is confusing. Current docstring does it explain it sufficiently clear. It also seems to me `copy` is unnecessary.
Currently the docstring says
```
...
A new object is produced unless the new index is equivalent to the current one and ``copy=False``.
...
copy : bool, default True
Return a new object, even if the passed indexes are the same.
```
It is hard to clarify what is considered an "equivalent" index. See below for more details.
Further, I believe users rarely purposefully tries to `reindex` with an "equivalent" index. It happens only if the user does not yet know the current index or the index to conform to, in which case a consistent behavior (e.g. always return new object) is probably preferred.
```python
# On current master
>>> pd.__version__
'1.1.0.dev0+1802.g942beba1e'
>>> df = pd.DataFrame(range(3))
>>> df
0
0 0
1 1
2 2
>>> df.index
RangeIndex(start=0, stop=3, step=1)
# not equivalent
>>> df is df.reindex(range(3), copy=False)
False
# not equivalent
>>> df is df.reindex(list(range(3)), copy=False)
False
# equivalent
>>> df is df.reindex(pd.RangeIndex(start=0, stop=3, step=1), copy=False)
True
>>> df = pd.DataFrame(range(3), index=list(range(3)))
>>> df
0
0 0
1 1
2 2
>>> df.index
Int64Index([0, 1, 2], dtype='int64')
# not equivalent
>>> df is df.reindex(range(3), copy=False)
False
# even this is considered not equivalent
>>> df is df.reindex(list(range(3)), copy=False)
False
>>> df is df.reindex(pd.Int64Index([0, 1, 2]), copy=False)
True
```
You can see it is actually pretty strict to be "equivalent". I feel it does really make sense to have this `copy` parameter because `reindex` will return a new object in most cases anyway even when `copy=False`.
So the question is, can we deprecate `copy`?
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Backport PR #34458 on branch 1.0.x (CLN: Clean csv files in test data GH34427)
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MEMBER
| null |
xref #34458
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BUG: DataFrame.to_dict() converts Nullable Int types to numpy.int
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| 2021-08-13T10:36:10Z
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NONE
| null |
#### Problem description
`DataFrame.to_dict()` method do not cast Nullable Int types (`Int*Dtype`) into Python `int` type. Instead, it unwrapping into `numpy.int*` types.
Possibly related to: #27616, #25969, #21256
#### Expected Output
Native Python `int` type.
#### Reproduction
Make some data:
```python
import pandas as pd
df = pd.DataFrame({'id': range(5),
'coeff': [i * 0.1 for i in range(5)],
'is_hot': [True] * 2 + [False] * 3,
'value': [1, None, 2, 3, None]})
df
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>coeff</th>
<th>is_hot</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>0.0</td>
<td>True</td>
<td>1.0</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>0.1</td>
<td>True</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>0.2</td>
<td>False</td>
<td>2.0</td>
</tr>
<tr>
<th>3</th>
<td>3</td>
<td>0.3</td>
<td>False</td>
<td>3.0</td>
</tr>
<tr>
<th>4</th>
<td>4</td>
<td>0.4</td>
<td>False</td>
<td>NaN</td>
</tr>
</tbody>
</table>
```python
df.dtypes
id int64
coeff float64
is_hot bool
value float64
dtype: object
```
`value` have to be a nullable `int`:
```python
df['value'] = df['value'].astype(pd.Int64Dtype())
df.dtypes
id int64
coeff float64
is_hot bool
value Int64
dtype: object
```
Looks great. But convert a dataframe to `dict`:
```python
dicts = df.to_dict(orient='records')
dicts
[{'id': 0, 'coeff': 0.0, 'is_hot': True, 'value': 1},
{'id': 1, 'coeff': 0.1, 'is_hot': True, 'value': nan},
{'id': 2, 'coeff': 0.2, 'is_hot': False, 'value': 2},
{'id': 3, 'coeff': 0.30000000000000004, 'is_hot': False, 'value': 3},
{'id': 4, 'coeff': 0.4, 'is_hot': False, 'value': nan}]
```
```python
pd.DataFrame(
[[type(v) for k, v in row.items()] for row in dicts],
columns=dicts[0].keys())
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>coeff</th>
<th>is_hot</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td><class 'int'></td>
<td><class 'float'></td>
<td><class 'bool'></td>
<td><class 'numpy.int64'></td>
</tr>
<tr>
<th>1</th>
<td><class 'int'></td>
<td><class 'float'></td>
<td><class 'bool'></td>
<td><class 'float'></td>
</tr>
<tr>
<th>2</th>
<td><class 'int'></td>
<td><class 'float'></td>
<td><class 'bool'></td>
<td><class 'numpy.int64'></td>
</tr>
<tr>
<th>3</th>
<td><class 'int'></td>
<td><class 'float'></td>
<td><class 'bool'></td>
<td><class 'numpy.int64'></td>
</tr>
<tr>
<th>4</th>
<td><class 'int'></td>
<td><class 'float'></td>
<td><class 'bool'></td>
<td><class 'float'></td>
</tr>
</tbody>
</table>
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDU6SXNzdWU2MzU0NzkxOTA=
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BUG: Source build issue with with py38
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CONTRIBUTOR
| null |
We don't have a py38 entry in our `pyproject.toml`, so when building from source for 3.8 we fetch NumPy 1.15.4, which isn't compatible with py38. Fix incoming.
|
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BLD: pyproject.toml for Py38
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34666
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BUG: GH29461 Strftime
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CONTRIBUTOR
| null |
- [x] closes #29461
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Closed the other PR while I sorted some things out.
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Practical steps towards a simplified BlockManager
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[] | null | 7
| 2020-06-09T15:36:41Z
| 2021-08-07T22:53:18Z
| null |
MEMBER
| null |
In the mailing list discussion on a simplified (non-consolidating) BlockManager, @jbrockmendel brought up the relevant question of how we could get there (incrementally?): https://mail.python.org/pipermail/pandas-dev/2020-May/001223.html
Since that is a more practical discussion, moving that to an issue here.
**Longer term**, there are multiple options of how such a blockmanager could be enabled by the user (listing 2 here, but there are probably other options as well):
- Go "all in" on extension arrays. We briefly discussed before the idea of using (nullable) extension dtypes for all dtypes by default in pandas 2.0. If we strive towards that, and assuming we keep the current 1D-restriction on ExtensionBlock, then we would "automatically" get a BlockManager with 1D blocks. And we could then focus on optimizing code paths specifically for the case of having all 1D ExtensionBlocks.
- A "consolidation policy" option similarly as in the branch discussed in https://github.com/pandas-dev/pandas/issues/10556. Right now, that branch still uses 2D blocks (but separate 2D blocks of shape (1, n) per column) and not actually 1D blocks. So that might not be fully ideal. We could add 1D versions of our numeric blocks as well, but that would probably add a lot of complexity, although
temporary, to the Blocks, so maybe not an ideal path forward.
For the "all extension arrays" option, we could also use light-weight EAs to store numpy arrays (like the "PandasArrays" we have now, only then actually using it), as long as we don't yet have actual extension arrays for all dtypes.
For both, we could have a constructor keyword to enabled it, and/or a global config option to enable it.
Now, **on the shorter term**, there are probably some work items we could tackle to **reduce the API surface / usage of blocks outside of the internals**, to make things like the above more realistic to implement (and make it easier to experiment with alternative BlockManager implementations):
- Continue removing cases where pandas code outside of the internals work directly on blocks (@jbrockmendel you already did a lot of work on this recently, do you have an idea / overview of which are still remaining and the potential stumble blocks to further remove those cases?)
- Remove any "index label" related code in the internals, as the BlockManager in principle only needs to concern about integer locations. Although, @jbrockmendel, with your work in https://github.com/pandas-dev/pandas/pull/33052, https://github.com/pandas-dev/pandas/pull/33347,
https://github.com/pandas-dev/pandas/pull/33332 (removal of BlockManager.get/set/delete) this might actually already be done?
- Limit the API surface of the BlockManager. It might be good to get an idea of which methods on the BlockManager are used outside of the internals, clearly list those (and make others private), and see whether we can reduce this list.
(Maybe actually reducing might not be possible, but the exercise can still be useful to get an explicit overview of what is needed to implement a BlockManager).
It might be that there are already more concrete open issues about some of those aspects.
cc @pandas-dev/pandas-core
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BUG: DataFrame.to_feather() does not work
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| 2020-06-09T15:45:17Z
| 2020-06-15T14:53:56Z
| 2020-06-15T11:22:06Z
|
NONE
| null |
The function `DataFrame.to_feather()` creates a file that cannot be read in in R.
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': [42, 43]})
df.to_feather("out.feather")
```
#### Problem description
When exporting a dataframe as above to feather format and reading it in from R, I get
```R
> library(feather)
> read_feather("out.feather")
Error in openFeather(path) : Invalid: Not a feather file
```
This error seems to have been introduced recently, it used to work on older environments. R version: 3.6.3.
#### Expected Output
It should be read in correctly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-33-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : 0.29.19
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : 0.15.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
>>>
</details>
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BUG: Series / DataFrame reductions convert string data to numeric
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| 2020-06-09T16:17:44Z
| 2021-08-07T22:53:37Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [34]: pd.Series(['1', '2', '3']).median()
Out[34]: 2.0
In [36]: pd.DataFrame({"A": ['1', '2', '3']}).median()
Out[36]:
A 2.0
dtype: float64
```
#### Problem description
`median` should not convert the input types. We seem to explicitly convert all non-float dtypes in `nanmedian`. Do we want to do that?
#### Expected Output
TypeError or ValueError. Not sure
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PERF: normalize_i8_timestamps
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| 0
| 2020-06-09T16:47:38Z
| 2020-06-09T18:03:23Z
| 2020-06-09T17:59:27Z
|
MEMBER
| null |
xref #34507
Based on the asv mentioned in that thread:
```
In [3]: N = 10**5
In [4]: tz = "UTC"
In [5]: series = Series(date_range(start="1/1/2000", periods=N, freq="T", tz=tz))
In [6]: %timeit series.dt.normalize()
947 µs ± 4.33 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
3.26 ms ± 43.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- master
```
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BUG: read_excel: incorrect multi-index values - 2
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| 0
| 2020-06-09T18:11:00Z
| 2021-01-01T19:45:27Z
| 2021-01-01T19:45:27Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.read_excel('test.xlsx', header=[0, 1], index_col=[0, 1])
print(df.index)
```
(`engine` could be `xlrd` or `openpyxl`, doesn't matter.)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4753954/test.xlsx)
#### Problem description
```
MultiIndex([('x', 'B')],
names=['A', 'B'])
```
#### Expected Output
```
MultiIndex([('x', None)],
names=['A', 'B'])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDMxOTg1ODgz
| 34,674
|
CLN: remove get_freq_code
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MEMBER
| null |
Sits on top of #34587
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|
BUG: pandas.groupby.apply does not work with floating point indexed dataframe since 1.0.0
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| 2020-06-19T15:06:35Z
| 2020-06-19T15:06:23Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version 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 # pandas==1.0.4, but this issue exists since 1.0.0
import numpy as np # numpy==1.16.3
toy_df = pd.DataFrame(
{
'group': np.random.randint(0, 5, 10),
'value': np.random.random(10)
},
index=np.random.random(10),
)
# fails with floating point index
toy_df.groupby('group').apply(sum)
toy_df.groupby('group').apply(lambda x: x['value'].sum())
# with integer index then it works:
toy_df.index = np.random.randint(0, 5, 10)
toy_df.groupby('group').apply(lambda x: x['value'].sum())
toy_df.groupby('group').apply(sum)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
I found this peculiar issue with the new version of pandas. When I have floating point index in a pandas DataFrame, then I cannot perform any `groupby.apply` operation on it. It will always give the below error:
```python
ValueError: index must be monotonic increasing or decreasing
During handling of the above exception, another exception occurred:
...
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
KeyError: 0.0
```
When I used `ipdb` to trace inside the error, I found that the following line caused the problem:
```python
> pandas/core/groupby/ops.py(930)fast_apply()
929
--> 930 sdata = self._get_sorted_data()
931 return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
```
after this line, `sdata` still does not have a sorted index, and then the next line raises the `ValueError: index must be monotonic increasing or decreasing`.
I tested that this error exists in `pandas==1.0.4` and `pandas==1.0.0` but does not exist in `pandas==0.25.1`.
#### Expected Output
Expected output from groupby apply should be the same in the example with either floating point index or integer index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.173-137.229.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.16.3
pytz : 2020.1
dateutil : 2.8.0
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : 0.29.2
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
BUG: read_excel: incorrect multi-index values - 3
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| 2020-06-09T19:03:58Z
| 2021-03-28T01:09:15Z
| 2021-03-27T01:21:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.read_excel('test.xlsx', header=[0, 1], index_col=[0, 1])
print(df.index)
print(df.index)
```
(`engine` could be `xlrd` or `openpyxl`, doesn't matter.)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4754140/test.xlsx)
#### Problem description
```
MultiIndex([(nan, nan),
('x', 'y')],
names=['A', 'B'])
Index(['C', 'Unnamed: 3'], dtype='object')
```
#### Expected Output
I'm not sure exactly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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|
BUG: ASV Benchmarks failing to build
|
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|
CONTRIBUTOR
| null |
ASV benchmarks fail to build on master. Could this be related to #34666?
`error: command 'gcc' failed with exit status 1`
Anyone else having this issue?
|
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CLN: dont consolidate in NDFrame._is_numeric_mixed_type
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CLN: dont consolidate in indexing
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CLN: dont consolidate in groupby
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MEMBER
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full asv run looks like pure noise
```
before after ratio
[03f5066c] [f2cae354]
<master~3> <cln-consolidate-less-3>
+ 38.4±0.1μs 45.7±6μs 1.19 algorithms.SortIntegerArray.time_argsort(1000)
+ 11.5±0.2ms 13.3±0.4ms 1.16 groupby.Nth.time_series_nth('float64')
+ 7.09±0.02ms 8.02±0.04ms 1.13 io.hdf.HDFStoreDataFrame.time_query_store_table
+ 12.3±0.1μs 13.7±0.3μs 1.11 tslibs.timestamp.TimestampOps.time_replace_None('US/Eastern')
+ 3.96±0.03μs 4.36±0.04μs 1.10 period.Indexing.time_get_loc
- 1.25±0ms 1.13±0ms 0.91 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<DateOffset: days=2, months=2>)
- 1.22±0ms 1.10±0ms 0.90 arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<DateOffset: days=2, months=2>)
- 1.54±0.1μs 1.39±0.06μs 0.90 index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
- 12.9±0.5μs 11.6±0.5μs 0.90 tslibs.timedelta.TimedeltaConstructor.time_from_components
- 3.75±0.06ms 3.35±0ms 0.89 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'min')
- 1.22±0.01ms 1.08±0ms 0.89 arithmetic.ApplyIndex.time_apply_index(<DateOffset: days=2, months=2>)
- 4.47±0.2μs 3.95±0.08μs 0.88 tslibs.timedelta.TimedeltaConstructor.time_from_datetime_timedelta
- 12.2±0.06ms 10.7±0.04ms 0.88 groupby.MultiColumn.time_col_select_numpy_sum
- 3.98±0.5μs 3.45±0.07μs 0.87 index_cached_properties.IndexCache.time_shape('DatetimeIndex')
- 30.2±0.2ms 26.1±0.08ms 0.86 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 0)
- 2.25±1μs 1.78±0.06μs 0.79 index_cached_properties.IndexCache.time_shape('UInt64Index')
- 5.76±0.9μs 4.51±0.3μs 0.78 index_cached_properties.IndexCache.time_shape('IntervalIndex')
- 2.56±0.5μs 1.99±0.2μs 0.77 index_cached_properties.IndexCache.time_values('IntervalIndex')
- 3.67±0.02ms 2.66±0.1ms 0.73 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'min')
- 4.34±1μs 2.95±0.2μs 0.68 index_cached_properties.IndexCache.time_shape('CategoricalIndex')
- 2.48±2μs 1.65±0.09μs 0.66 index_cached_properties.IndexCache.time_shape('Float64Index')
- 3.04±1μs 1.96±0.1μs 0.64 index_cached_properties.IndexCache.time_values('CategoricalIndex')
- 1.88±0.6μs 1.09±0.06μs 0.58 index_cached_properties.IndexCache.time_values('Float64Index')
- 3.14±1μs 1.16±0.05μs 0.37 index_cached_properties.IndexCache.time_values('UInt64Index')
```
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BUG: groupby.sum() is inconsistent with df.sum() for large integers
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open
| false
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[] | null | 16
| 2020-06-09T21:20:06Z
| 2020-09-21T22:46:54Z
| null |
NONE
| null |
- [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version 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
for i in range(100):
n = 2 ** i
print(f"Trying n = 2 ** {i} '{n}'...")
df = pd.DataFrame([['A', 14], ['A', n]], columns=['gb', 'val'])
gb_sum = df.groupby('gb').sum().values[0][0]
df_sum = df.sum().values[1]
if gb_sum != df_sum:
print(f"df.sum().values[1] '{df_sum}' != df.groupby('gb').sum().values[0][0] '{gb_sum}")
break
del df
print(f"Runing pandas {pd.__version__}")
```
#### Problem description
groupby.sum() results currently provide different results for df.sum() results for large integers.
It is expected that they should provide the same results.
#### Expected Output
When grouping by a colum with a single value, the groupby().sum() result should always equal the df.sum() result()
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.16
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
Runing pandas None
Process finished with exit code 0
</details>
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MDU6SXNzdWU2MzU4NDc3Mjg=
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ENH: Make metadata from read_spss available
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| 4
| 2020-06-10T00:50:27Z
| 2021-08-07T22:55:12Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I would like to have the [metadata that pyreadstats provides](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html) available when reading files from SPSS.
This would be really helpful because it would provide an easy way to have variable labels (descriptions), value labels, and other important metadata available to format results/reports (by replacing the variable names manually with `.replace` function).
Those kinds of metadata are widely used in social sciences because it makes understanding results really easy. For example, SPSS changes the variable names to the variable labels in the output of analyses. Users could manually do this if the metadata was available.
#### Describe the solution you'd like
The metadata read by pyreadstats could be stored in the df's `_metadata` attribute and that would make it readily available
#### API breaking implications
I don't think there would be any implications if it's stored in the `_metadata` attribute because it was developed for this kind of use-case. I'm I right?
#### Describe alternatives you've considered
I could use the pyreadstats directly without using the `df.read_spss`. I can't think of any other options.
#### Additional context
This is related to issues [#11179](https://github.com/pandas-dev/pandas/issues/11179) and [#39](https://github.com/pandas-dev/pandas/issues/39).
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CLN: dont consolidate in reshape.concat
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| 25
| 2020-06-10T01:52:28Z
| 2020-12-17T21:48:07Z
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MEMBER
| null |
Looks like this consolidation was added here https://github.com/pandas-dev/pandas/commit/3b1c5b74e0ca5a782d4c070aac002b3e6d7e5290 in 2012, no clear reason why it is needed. About to start an asv run.
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RLS: 1.0.5
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| 31
| 2020-06-10T08:20:04Z
| 2020-06-18T13:09:20Z
| 2020-06-18T13:09:20Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/34664#issuecomment-641355926
>>> @simonjayhawkins do you have time one of those days to do a 1.0.5 release?
>> can do. do we want to include anything else, wait a few weeks before doing the release and see if anything else surfaces or just get a patch release done with the S3 changes?
> I would personally just do a patch release with the S3 changes, and not wait a few weeks with that. But maybe open a RLS issue for it to get some feedback from others?
cc @pandas-dev/pandas-core
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ENH: NamedAgg support for time windows/Expanding
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| 2020-06-10T08:41:07Z
| 2020-10-05T01:21:47Z
| 2020-10-05T01:21:47Z
|
NONE
| null |
#### Is your feature request related to a problem?
I am using aggregations over data with groups over time, to create features. I had hoped to use the "new" namedAgg functionality to efficiently calculate and rename the many feature columns.
I find that named Aggregations only works with the "default"groupby.agg, when used with a rolling window or expanding, it's not supported.
(I note that the functions documentation doesn't mention this,[ the named agg is mainly in the general documentation](https://pandas-docs.github.io/pandas-docs-travis/user_guide/groupby.html#groupby-aggregate-named), not method-level. i.e the [groupby.agg method's documentatio](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.aggregate.html)n doesn't mention or demonstrate this functionality at all).
By "named aggregations" I refer to the functionality:
`animals.groupby("kind").agg(**min_height=('height', 'min')**)`
#### Describe the solution you'd like
Expand named aggregation support (NamedAgg) to the groupby aggregation used in [expanding.aggregate](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.expanding.Expanding.aggregate.html) and [rolling.aggregate](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.rolling.Rolling.aggregate.html)
#### API breaking implications
Should not affect it. Seems 1:1.
#### Additional context
Example usage/errors:
```
df_actions.set_index("age").groupby('uuid').rolling(30).agg(unique_actions=('action', 'nunique'),
total_actions=('counter', 'sum'))
TypeError: aggregate() missing 1 required positional argument: 'func'
```
When used without the window, we get the benefit of namedAggs. (The real code has many more columns and transformations and the columns the features are calculated in are dynamic, so setting a list of column names to use is not desirable. Additionally, data is time-sorted):
```
df_actions.set_index("age").groupby('uuid').agg(unique_actions=('action', 'nunique'),
total_actions=('counter', 'sum'))
>>>
| unique_actions | total_actions
0 | 0.0
0 | 0.0
...
```
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API/BUG: Behaviour of NA in BooleanArray pow operation
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| 2020-06-10T08:58:16Z
| 2021-08-08T00:28:58Z
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MEMBER
| null |
For IntegerArray, we have some special-case behaviour for NAs in `pow` operation. For example, `1 ** NA == 1` and not NA as normal NA-propagation would be.
For BooleanArray, we don't have that, but maybe we should do the same? So that the boolean power of boolean-cast-to-int8 power operations for the same?
Right now this is different:
```python
>>> a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
>>> b = pd.array([True, False, None] * 3, dtype="boolean")
>>> a
<BooleanArray>
[True, True, True, False, False, False, <NA>, <NA>, <NA>]
Length: 9, dtype: boolean
>>> b
<BooleanArray>
[True, False, <NA>, True, False, <NA>, True, False, <NA>]
Length: 9, dtype: boolean
>>> a ** b
<IntegerArray>
[1, 1, <NA>, 0, 1, <NA>, <NA>, <NA>, <NA>]
Length: 9, dtype: Int8
>>> a.astype("Int8") ** b.astype("Int8")
<IntegerArray>
[1, 1, 1, 0, 1, <NA>, <NA>, 1, <NA>]
Length: 9, dtype: Int8
```
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| 5
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| 2021-04-02T17:15:22Z
| 2021-04-02T17:15:22Z
|
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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
from scipy.sparse import coo_matrix, eye
import pandas as pd
df = pd.DataFrame.sparse.from_spmatrix(eye(10))
df.sparse.density
# Should output 0.1
df.loc[range(5)]
# 0 1 2 3 4 5 6 7 8 9
# 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
# 1 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
# 2 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
# 3 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
# 4 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
df.loc[range(5)].sparse.density
# outputs 0.1
df.loc[range(5)].loc[range(3)]
# 0 1 2 3 4 5 6 7 8 9
# 0 1.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN
# 1 0.0 1.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN
# 2 0.0 0.0 1.0 0.0 0.0 NaN NaN NaN NaN NaN
df.loc[range(5)].loc[range(3)].sparse.density
# outputs 0.6
```
#### Problem description
It seems that sparse DataFrame extracted using loc does not behave as the original ones. This creates inconsistencies in our processing pipelines, depending on filtering and selection that has been applied, sometimes producing "Nans", severely impacting memory consumption and computational time.
#### Expected Output
The output of loc should not depend on multiple slicing, i.e.
df.loc[range(5)].loc[range(3)] = df.loc[range(3)]
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.3
setuptools : 39.0.1
Cython : 0.29.19
pytest : 5.4.3
hypothesis : 5.16.1
sphinx : 3.1.0
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : 0.4.0
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.49.1
</details>
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TYP: update setup.cfg
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TYP: check_untyped_defs pandas.core.nanops
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| 3
| 2020-06-10T13:00:05Z
| 2020-06-14T15:51:36Z
| 2020-06-14T14:56:37Z
|
MEMBER
| null |
pandas\core\nanops.py:565: error: Item "bool" of "Union[bool, Any]" has no attribute "any"
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DOC: Add note about shallow clones in contributing guide
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| 0
| 2020-06-10T13:48:43Z
| 2020-06-10T14:54:15Z
| 2020-06-10T14:54:15Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I didn't open a new issue for this, let me know if I should, I'll gladly do it (I got the impression that that's not mandatory by reading the contributing guidelines).
Feel also free to close without merging if I'm the only person who got confused by this.
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|
BUG: Unpredictable behaviour of `Series.replace` when filling with `None` values.
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[] | null | 2
| 2020-06-10T14:03:17Z
| 2020-06-10T16:18:13Z
| 2020-06-10T16:18:12Z
|
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
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [1.0, 2.0, 'a', 45.0, 0.0]})
df['B'] = df['B'].replace('a', None)
print(df)
```
#### Problem description
While replacing the values in dataframe with `None` using `.replace` method the values to be replaced get forward filled from the previous values which is *unpredictable behaviour here*.
#### Expected Output
```
A B
0 1 1
1 2 2
2 3 None
3 4 45
4 5 0
```
#### Actual Output
```
A B
0 1 1
1 2 2
2 3 2
3 4 45
4 5 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-33-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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TYP: check_untyped_defs pandas.core.resample
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| 2
| 2020-06-10T14:07:09Z
| 2020-06-13T18:49:41Z
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MEMBER
| null |
pandas\core\resample.py:969: error: Too many arguments for "__init__" of "object"
pandas\core\resample.py:1572: error: "DatetimeIndex" has no attribute "tzinfo"
pandas\core\resample.py:1573: error: "DatetimeIndex" has no attribute "tzinfo"
pandas\core\resample.py:1585: error: "PeriodIndex" has no attribute "asfreq"; maybe "freq"?
pandas\core\resample.py:1867: error: "PeriodIndex" has no attribute "asfreq"; maybe "freq"?
pandas\core\resample.py:1902: error: "PeriodIndex" has no attribute "asfreq"; maybe "freq"?
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DOC: combine with None at same position in both dataframes
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| 8
| 2020-06-10T14:37:09Z
| 2020-07-03T07:00:46Z
| null |
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.combine.html
#### Documentation problem
The fourth example states:
> However, if the same element in both dataframes is None, that None is preserved
In the code sample to illustrate this, however, `None` is **not** preserved:
```
df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0
```
#### Suggested fix for documentation
If the actual result is the intended behavior, this code sample should be removed. If `None` should indeed be preserved (but why should it?) then this should be filed as a bug.
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DOC: updated doctorings for SS06 errors
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TYP: check_untyped_defs pandas.io.json._table_schema
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| 2020-06-14T09:51:51Z
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MEMBER
| null |
pandas\io\json\_table_schema.py:112: error: Incompatible types in assignment (expression has type "Dict[str, List[Any]]", target has type "str")
pandas\io\json\_table_schema.py:264: error: Incompatible types in assignment (expression has type "str", target has type "List[Any]")
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ENH: DataFrame or Series to code
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| 2020-06-10T14:44:33Z
| 2020-06-12T01:16:02Z
| 2020-06-12T01:16:02Z
|
NONE
| null |
**Request**
Have a method to transform a pandas object to code.
**Example**
The given situation
`given_df = pd.DataFrame({"a": [1, 2],
"b": pd.to_datetime(["2019-01-01", "2019-12-31"]),
"c": ["a", "b"]})`
Call to the method :
`code_string = given_df.to_code()`
Expected output :
`pd.DataFrame({'a': [1, 2], 'b': pd.to_datetime(['2019-01-01', '2019-12-31'], format='%Y-%m-%d'), 'c': ['a', 'b']})`
**Is your feature request related to a problem?**
The usage I would have of such a method would be when I'm writing integration tests.
GIven `my_function`, I want to write a integration test to make sure that result doesn't changes.
The output of my function is a DataFrame which is a bit long a few lines, and a few columns.
class test(unitest.testcase):
def test_integration(self):
# Given
given_some_args
# When
result_df: pd.DataFrame = my_function(given_some_args)
# Then
pd.testing.assert_frame_equals(expected_df, result_df)
Writing the `expected_df` manually is time consuming. I would like to be able to get it by executed `result_df.to_code() `once and then copy past the generated code in my integration test.
**API breaking implications**
I don't think it will have impact on existing API.
**Describe alternatives you've considered**
A proposition of implementation would be `"df = pd.DataFrame( %s )" % (str(df.to_dict()))`
Although it is a simple implementation,
- it doesn't work for every types of series (for example datetimes),
- it doesn't provide executable code in case of `nan`,
- it doesn't include needed imports
**Additional context**
This need has already been raised in[ this question](https://stackoverflow.com/questions/41769882/pandas-dataframe-to-code) on stackoverflow.
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TYP: type annotations for read_sas
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QST: should pandas series isin raise an exception with a nested sequence?
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| 2020-06-10T15:18:06Z
| 2020-06-10T16:34:24Z
| 2020-06-10T16:12:26Z
|
NONE
| null |
- [x ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Using a nested structure in series isin() does not rasie an exception, instead no match is found? Should this pass silently?
```python
# Your code here, if applicable
test = pd.DataFrame({'a': [1,2,3]})
test['a'].isin([1])
0 True
1 False
2 False
test['a'].isin([[1]])
0 False
1 False
2 False
pd.__version__
'0.25.0'
```
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BUG: GH34677 Roll forward asv benchmark environment to py3.8
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CONTRIBUTOR
| null |
Partially closes #34677
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CLN: remove usages of base_and_stride
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CLN: remove libfrequencies.get_freq_group
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MEMBER
| null |
The next (hopefully last) step after this is to move Resolution to dtypes.pyx and de-duplicate it with FreqGroup.
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BUG: DataFrame.loc[] returns a Series instead of a value
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| 2020-08-21T17:05:15Z
| 2020-06-10T18:59:01Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([ [ 17 ] ], index=pd.MultiIndex.from_tuples([('x', pd.Timestamp('2020-06-10')) ]), columns=[ 'A' ])
print(type(df.loc[ ('x', '2020-06-10'), 'A' ]))
print(type(df.loc[ ('x', pd.Timestamp('2020-06-10')), 'A' ]))
```
#### Problem description
```
<class 'pandas.core.series.Series'>
<class 'numpy.int64'>
```
#### Expected Output
```
<class 'numpy.int64'>
<class 'numpy.int64'>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.32-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDMyNjA4OTQ3
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CLN: disallow tuple in to_offset
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| 1
| 2020-06-10T17:48:15Z
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MEMBER
| null |
This is technically an API change, but AFAICT this isnt documented behavior anywhere.
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BUG: CI builds failing at compilation of _sas
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| 2020-06-14T15:57:50Z
| 2020-06-14T15:57:50Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
See, for example, the build line https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=37068&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=2296ffae-5e67-52bf-66a8-6160145a0f80&l=660
(and this appears to be failing other PRs too)
#### Problem description
CI builds are failing, see link above
#### Expected Output
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MDExOlB1bGxSZXF1ZXN0NDMyNjQ0NzMx
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TST: Add test for rolling window, see GH 34605
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| 7
| 2020-06-10T19:07:27Z
| 2020-06-15T12:53:22Z
| 2020-06-15T12:53:18Z
|
CONTRIBUTOR
| null |
- [X] closes #34605
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Dear maintainers,
I'm opening this PR to address issue #34605.
~I've made the test conditional to Python 3.8 as I'm using the positional only syntax as strong check. If this is not desired, let me know and I will change the function to explicitly check for the length of `*args`.~
Edit: maybe the usage of `parametrize` is a bit exaggerated here.
Edit2: Using Python 3.8 only features does not pleases the tests. Updating.
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MDU6SXNzdWU2MzY1NTU5MTI=
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ENH: implement pd.Series.corr(method="polychoric")
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| 2020-06-10T21:17:15Z
| 2021-08-08T00:31:46Z
| 2021-08-08T00:31:46Z
|
NONE
| null |
I wish I could use pandas to calculate the polychoric correlation for ordinal variables. According to the literature, the polychronic correlation presents really good results. Compare to Spearman correlation it performs better on n > 1000 number of samples.
I couldn't find an easy way to use it in python.
In R there are at least two implementations of this correlation e.g.
https://r-forge.r-project.org/projects/polycor/
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PERF: is_date_array_normalized
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| 2020-06-10T21:57:37Z
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MEMBER
| null |
Same optimization we made in normalize_i8_timestamps the other day.
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MDU6SXNzdWU2MzY1ODMyMTI=
| 34,708
|
BUG: DataFrame.unstack on non-consolidated frame
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| 2020-06-10T22:15:53Z
| 2020-06-14T14:13:01Z
| 2020-06-14T14:13:01Z
|
MEMBER
| null |
```
df = pd.DataFrame({"x": [1, 2, np.NaN], "y": [3.0, 4, np.NaN]})
df2 = df[["x"]]
df2["y"] = df["y"]
assert len(df2._mgr.blocks) == 2
>>> df2.unstack()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/frame.py", line 7031, in unstack
return unstack(self, level, fill_value)
File "pandas/core/reshape/reshape.py", line 419, in unstack
return _unstack_frame(obj, level, fill_value=fill_value)
File "pandas/core/reshape/reshape.py", line 435, in _unstack_frame
unstacker = _Unstacker(obj.index, level=level)
File "pandas/core/reshape/reshape.py", line 93, in __init__
self.index = index.remove_unused_levels()
AttributeError: 'RangeIndex' object has no attribute 'remove_unused_levels'
```
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BUG: DataFrame.unstack with non-consolidated
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| 0
| 2020-06-11T00:32:04Z
| 2020-06-14T15:10:18Z
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|
MEMBER
| null |
- [x] closes #34708
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
ENH: Writing a DataFrame to Excel with `XlsxWriter` in constant_memory mode requires row-by-row writes
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[] | null | 3
| 2020-06-11T09:54:33Z
| 2021-02-18T19:23:11Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
When writing large DataFrames to an Excel file using `XlsxWriter`, one can use the `options={'constant_memory': True}` keyword arguments.
However, [per the documentation](https://xlsxwriter.readthedocs.io/workbook.html#constructor): **once this mode is active, data should be written in sequential row order.**
The way pandas works at the moment, is that cells are written [per series](https://github.com/pandas-dev/pandas/blob/v1.0.4/pandas/io/formats/excel.py#L675) first, so column-by-column. This effectively writes the DataFrame so that only the first column and last row are fully written (along with the column names, which are written as a single row).
#### Describe the solution you'd like
It would be great to add an axis-like argument in the `to_excel` method, controlling how the data is written to the file (by columns (series) or by rows).
#### API breaking implications
There should be no breaking implications. This new argument can have a default value that matches the current implementation.
#### Describe alternatives you've considered
Monkeypatching the ExcelFormatter as such works fine:
```python
from pandas.io.formats.excel import ExcelFormatter, ExcelCell
def write_excel_by_rows(self, coloffset: int):
if self.styler is None:
styles = None
else:
styles = self.styler._compute().ctx
if not styles:
styles = None
xlstyle = None
for rowidx in range(self.df.shape[0]):
for colidx in range(len(self.columns)):
if styles is not None:
xlstyle = self.style_converter(";".join(styles[rowidx, colidx]))
yield ExcelCell(self.rowcounter + rowidx, colidx + coloffset, self.df.iloc[rowidx, colidx], xlstyle)
ExcelFormatter._generate_body = write_excel_by_rows
```
#### Additional context
Reproducible minimal example:
```python
import pandas as pd
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5], 'c': [6, 7, 8]})
with pd.ExcelWriter('foo.xlsx', engine='xlsxwriter', options={'constant_memory': True}) as xl:
df.to_excel(xl, index=False)
pd.read_excel('foo.xlsx')
>>> a b c
>>> 0 0 NaN NaN
>>> 1 1 NaN NaN
>>> 2 2 5.0 8.0
```
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BLD: Pin cython for 37-locale build
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| 7
| 2020-06-11T13:47:58Z
| 2020-06-15T16:17:38Z
| 2020-06-11T15:26:58Z
|
CONTRIBUTOR
| null |
Seeing if this resolves the build failure.
xref https://github.com/pandas-dev/pandas/issues/34704
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MDExOlB1bGxSZXF1ZXN0NDMzMTcyNjA0
| 34,712
|
Make Cython in numpydev config Consistent
|
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| 2020-06-11T15:42:14Z
| 2020-06-11T21:41:45Z
| 2020-06-11T21:41:45Z
|
MEMBER
| null |
Follow up to #34711 and just matching the other config files. Not sure generally if we should be specifying here and in higher level configs as may be ambiguous as to what Cython version gets installed, but going with the quick patch for now
|
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MDExOlB1bGxSZXF1ZXN0NDMzMjExOTg4
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DOC: updated core/indexes/base.py for SS06 errors
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MDU6SXNzdWU2MzcyMDk3ODU=
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REF: BlockManager.apply_allow_failures
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| 2020-06-11T17:43:39Z
| 2020-09-03T02:56:33Z
| 2020-09-03T02:56:33Z
|
MEMBER
| null |
cc @mroeschke w/r/t rolling `._apply`, @WillAyd w/r/t `_cython_agg_blocks`, and anyone else w/r/t `DataFrame._reduce`with `numeric_only=None`, `apply.FrameApply` with `ignore_failures=True`.
All of these do something along the lines of:
```
results = []
exclude = []
for i, block in enumerate(mgr.blocks):
try:
res = func(block.values)
results.append(res)
except:
exclude.append(i)
out = reconstruct(results, exclude)
```
Two things should be done with this pattern:
1) Deprecate it, since it is a disproportionate maintainence burden
2) implement something like `BlockManager.apply_with_ignore_failures` that would resemble BlockManager.apply but with the try/except logic\*
\* We _could_ add that logic into BlockManager.apply, but BM.apply assumes the output has the same shape as the original frame, which I don't think holds for the cases mentioned above, so that would be a little more invasive than just adding a try/except.
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MDExOlB1bGxSZXF1ZXN0NDMzMjY3NTc0
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DOC: updated core/groupby/generic.py for SS06 errors
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DOC: updated pandas/core/series.py for SS06 errors
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| 2
| 2020-06-11T19:23:29Z
| 2020-06-12T20:58:35Z
| 2020-06-12T20:58:28Z
|
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