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MDExOlB1bGxSZXF1ZXN0NDI2MDc0NzI1
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TST #22663: Operation between DataFrame with non-numeric types and incomplete series
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CONTRIBUTOR
| null |
- [ x ] closes #22663
- [ 1 ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDI2MTkxNTYw
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REF: add Resolution to tslibs.__init__
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| 3
| 2020-06-01T20:15:58Z
| 2020-06-02T01:21:46Z
| 2020-06-02T01:13:39Z
|
MEMBER
| null |
rename _resolution -> _resolution_obj
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BUG: read_csv stopped working with s3 file system
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|
NONE
| null |
- [yes ] I have checked that this issue has not already been reported.
- [ yes ] I have confirmed this bug exists on the latest version of pandas.
- [ yes] (optional) I have confirmed this bug exists on the master branch of pandas.
Checked against pandas 1.1.0.dev0+1732.g2428cdda3
#### Problem description
read_csv in pandas1.0.4 has stopped working with s3fs.
On pandas1.0.3
```python
import pandas as pd; import s3fs
s3fs.S3FileSystem(anon=False, key=os.environ.get("STORE_USERNAME"), secret=os.environ.get("STORE_PASSWORD"))
df = pd.read_csv(filepath_or_buffer="s3://my-private-bucket/my_dataframe.csv")
print(df.shape)
```
prints the correct output, whilst using pandas1.0.4 it raises the following exception
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/nico/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/nico/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 431, in _read
filepath_or_buffer, encoding, compression
File "/home/nico/.local/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/nico/.local/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/nico/.local/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/nico/.local/lib/python3.7/site-packages/fsspec/spec.py", line 775, in open
**kwargs
File "/home/nico/.local/lib/python3.7/site-packages/s3fs/core.py", line 378, in _open
autocommit=autocommit, requester_pays=requester_pays)
File "/home/nico/.local/lib/python3.7/site-packages/s3fs/core.py", line 1097, in __init__
cache_type=cache_type)
File "/home/nico/.local/lib/python3.7/site-packages/fsspec/spec.py", line 1065, in __init__
self.details = fs.info(path)
File "/home/nico/.local/lib/python3.7/site-packages/s3fs/core.py", line 530, in info
Key=key, **version_id_kw(version_id), **self.req_kw)
File "/home/nico/.local/lib/python3.7/site-packages/s3fs/core.py", line 200, in _call_s3
return method(**additional_kwargs)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/client.py", line 622, in _make_api_call
operation_model, request_dict, request_context)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/client.py", line 641, in _make_request
return self._endpoint.make_request(operation_model, request_dict)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/endpoint.py", line 102, in make_request
return self._send_request(request_dict, operation_model)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/endpoint.py", line 132, in _send_request
request = self.create_request(request_dict, operation_model)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in create_request
operation_name=operation_model.name)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/hooks.py", line 356, in emit
return self._emitter.emit(aliased_event_name, **kwargs)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/hooks.py", line 228, in emit
return self._emit(event_name, kwargs)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/hooks.py", line 211, in _emit
response = handler(**kwargs)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/signers.py", line 90, in handler
return self.sign(operation_name, request)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/signers.py", line 160, in sign
auth.add_auth(request)
File "/home/nico/.local/lib/python3.7/site-packages/botocore/auth.py", line 357, in add_auth
raise NoCredentialsError
botocore.exceptions.NoCredentialsError: Unable to locate credentials
```
#### Output of ``pd.show_versions()``
using pandas 1.0.3
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-46-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_AR.UTF-8
LOCALE : es_AR.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : 5.4.0
hypothesis : None
sphinx : 1.6.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
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 : None
pytables : None
pytest : 5.4.0
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>
using pandas 1.0.4
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-46-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_AR.UTF-8
LOCALE : es_AR.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : 5.4.0
hypothesis : None
sphinx : 1.6.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
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pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.0
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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MDU6SXNzdWU2Mjg3NTE1Njg=
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|
BUG: Mixed DataFrame with Extension Array incorrect aggregation
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| 3
| 2020-06-01T21:51:17Z
| 2020-07-15T22:17:58Z
| 2020-07-15T22:17:58Z
|
MEMBER
| null |
Surprised to not get an aggregation for "b" with the Int64 dtype
```python
>>> df = pd.DataFrame([["a", 1]], columns=list("ab"))
>>> df.sum()
a a
b 1
dtype: object
>>> df.astype({"b": "Int64"}).sum()
a a
dtype: object
```
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ENH: Set numeric_only=True in aggregations
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| 2020-06-02T15:10:14Z
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|
MEMBER
| null |
I've noticed that I make this mistake quite often:
```python
>>> df = pd.DataFrame({"a": ["1"] * 3, "b": np.ones(3)})
>>> df.sum()
a 111
b 3
dtype: object
```
Getting `111` as a result in column a is harmless in this example, but actually quite annoying in most real life use cases where it can produce exceedingly large strings that exhaust memory or tie up the interpreter.
The `numeric_only` argument gets around this issue:
```python
>>> df.sum(numeric_only=True)
b 3.0
dtype: float64
```
Though I'm curious if this should really be the default
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MDExOlB1bGxSZXF1ZXN0NDI2MjYzNDAz
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DEPR: tz kwarg in Period.to_timestamp
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| 7
| 2020-06-01T23:20:39Z
| 2020-06-03T16:29:07Z
| 2020-06-03T11:34:03Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
inconsistent with PeriodArray/PeriodIndex methods
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CLN: stronger typing for Period.freq
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| 2020-06-02T14:39:22Z
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| 628,864,172
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MDExOlB1bGxSZXF1ZXN0NDI2MzIxNDM5
| 34,524
|
CLN: remove Resolution.get_attrname_from_abbrev
|
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| 2020-06-02T03:05:17Z
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MEMBER
| null |
rename get_str -> attrname, make it a property instead of a classmethod
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MDU6SXNzdWU2Mjg5MTM5MDI=
| 34,525
|
BUG: Replacing rows with iloc changes dtype
|
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| 2020-06-02T05:33:30Z
| 2020-08-21T17:05:44Z
| 2020-06-03T14:51:22Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
df1 = pd.DataFrame([(1, pd.Timestamp.utcfromtimestamp(1))] * 5, columns=['nb', 'ts'])
df2 = pd.DataFrame([(2, pd.Timestamp.utcfromtimestamp(2))] * 3, columns=['nb', 'ts'])
print("df2:\n", df2)
print("\ndf1 before:\n", df1)
df1.iloc[[1, 4]] = df2.iloc[[0, 2]].values
print("\ndf1 after replacing some rows by some of df2's rows:\n", df1)
```
Full output is
```
df2:
nb ts
0 2 1970-01-01 00:00:02
1 2 1970-01-01 00:00:02
2 2 1970-01-01 00:00:02
df1 before:
nb ts
0 1 1970-01-01 00:00:01
1 1 1970-01-01 00:00:01
2 1 1970-01-01 00:00:01
3 1 1970-01-01 00:00:01
4 1 1970-01-01 00:00:01
df1 after replacing some rows by some of df2's rows:
nb ts
0 1 1000000000
1 2 1970-01-01 00:00:02
2 1 1000000000
3 1 1000000000
4 2 1970-01-01 00:00:02
```
#### Problem description
The rows which are _not_ overwritten have their dtype changed (cast from Timestamp to int) although all dtypes align.
#### Expected Output
```
df1 after replacing some rows by some of df2's rows:
nb ts
0 1 1970-01-01 00:00:01
1 2 1970-01-01 00:00:02
2 1 1970-01-01 00:00:01
3 1 1970-01-01 00:00:01
4 2 1970-01-01 00:00:02
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.14.4.el7.x86_64
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 : 1.0.3
numpy : 1.14.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 39.1.0
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.6
tables : 3.5.2
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.45.1
</details>
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MDU6SXNzdWU2Mjg5OTA3OTE=
| 34,526
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BUG: Select an all-zero sparse series by indexer results in NaN
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| 2020-07-08T11:47:08Z
| 2020-07-08T11:47:08Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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 scipy as sp
import scipy.sparse
spmatrix = sp.sparse.csr_matrix((2, 2))
spmatrix[0, 0] = 1
df = pd.DataFrame.sparse.from_spmatrix(spmatrix)
# work as expected, cool
df.loc[1]
# 0 0.0
# 1 0.0
# Name: 0, dtype: Sparse[float64, 0.0]
# Indexing by index list. I expect 1,0, 0.0, not cool
df.loc[[1]]
# 0 1
# 1 0.0 NaN
# whenever indexing an all-zero series, it returns Nan.
df.loc[[1]].loc[[1]]
# 0 1
# 1 NaN NaN
# dtype is good
df.loc[[1]].dtypes
# 0 Sparse[float64, 0.0]
# 1 Sparse[float64, 0.0]
# dtype: object
# indexing by bool list also not cool
df.loc[[True, True]]
# 0 1
# 0 1.0 NaN
# 1 0.0 NaN
# Slicing is cool
df.loc[1:2]
# 0 1
# 1 0.0 0.0
```
#### Problem description
Index slice an all-zero sparse series by indexer (whether by bool array or index array) results in NaN. It should be the `fill_value` of the series.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-101-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 : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
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 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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MDU6SXNzdWU2MjkxNTY4MjA=
| 34,527
|
Getting Attribute error while merging framed
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[] | null | 2
| 2020-06-02T12:18:03Z
| 2020-08-03T21:12:09Z
| 2020-08-03T21:11:55Z
|
NONE
| null |
When merging frames,I am getting AttributeError:Dataframe object has no attribute _cols.
It shows the error in 5067 line of generic.py
Current pandas version:0.24.2
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MDU6SXNzdWU2MjkxNjI3MTg=
| 34,528
|
ENH: Implement categorical correlation
|
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[] | null | 7
| 2020-06-02T12:27:40Z
| 2020-09-04T13:33:38Z
| 2020-09-04T13:33:38Z
|
NONE
| null |
#### Is your feature request related to a problem?
Currently, whenever I want to visualize features correlation I'm using df.corr(), but when I have a dataset with categorical features I have to encode them before using df.corr() if I want to include the categorical features.
#### Describe the solution you'd like
I came across this article
[https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9](https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9)
and I wondered if I can implement the solution as a new feature or add it into corr().
#### API breaking implications
Maybe add another boolean parameter to corr which indicate that the user wants to include categorical features?
Thanks :)
|
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| 629,178,128
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MDU6SXNzdWU2MjkxNzgxMjg=
| 34,529
|
BUG: apply() fails on some value types
|
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| 9
| 2020-06-02T12:51:59Z
| 2020-06-19T22:33:21Z
| 2020-06-19T22:33:21Z
|
NONE
| null |
We have some existing code that manipulates data that is decoded into numpy arrays (by a C powered backend). This code has stopped working.
I've tried to strip it down to a reduced case
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.array([b'abcd', b'efgh']), columns=['col'])
df.apply(lambda x: x.astype('object'))
```
This fails with an error inside an internal function of apply:
```---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-88-a5fa9cabd101> in <module>
----> 1 df.apply(lambda x: x.astype('object'))
~/local/pkg/miniconda3/envs/odc/lib/python3.8/site-packages/pandas/core/frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
6876 kwds=kwds,
6877 )
-> 6878 return op.get_result()
6879
6880 def applymap(self, func) -> "DataFrame":
~/local/pkg/miniconda3/envs/odc/lib/python3.8/site-packages/pandas/core/apply.py in get_result(self)
184 return self.apply_raw()
185
--> 186 return self.apply_standard()
187
188 def apply_empty_result(self):
~/local/pkg/miniconda3/envs/odc/lib/python3.8/site-packages/pandas/core/apply.py in apply_standard(self)
293
294 try:
--> 295 result = libreduction.compute_reduction(
296 values, self.f, axis=self.axis, dummy=dummy, labels=labels
297 )
pandas/_libs/reduction.pyx in pandas._libs.reduction.compute_reduction()
pandas/_libs/reduction.pyx in pandas._libs.reduction.Reducer.__init__()
pandas/_libs/reduction.pyx in pandas._libs.reduction.Reducer._check_dummy()
ValueError: Dummy array must be same dtype
```
If we use a different dtype, then it works.
```
df = pd.DataFrame(np.array(['abcd', 'efgh']), columns=['col'])
df.apply(lambda x: x.astype('object'))
print(df)
```
which gives the expected result
```
col
0 abcd
1 efgh
```
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BUG: series with dtype "Int64" don't work correctly with replace() method
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| 2
| 2020-06-02T13:22:40Z
| 2020-06-15T19:21:20Z
| 2020-06-15T19:21:20Z
|
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. ( Version 1.0.4)
---
I get an AssertionError from the following code:
#### Code Sample
```python
Int_series = pd.Series(data=[1, 2, 3], dtype="Int64")
Int_series.replace("", "ABC", inplace=True) # throws exception
```
```pytb
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/sandbox/pandas/pandas/core/internals/blocks.py in replace(self, to_replace, value, inplace, regex, convert)
731 try:
--> 732 blocks = self.putmask(mask, value, inplace=inplace)
733 # Note: it is _not_ the case that self._can_hold_element(value)
~/sandbox/pandas/pandas/core/internals/blocks.py in putmask(self, mask, new, inplace, axis, transpose)
1608
-> 1609 new_values[mask] = new
1610 return [self.make_block(values=new_values)]
~/sandbox/pandas/pandas/core/arrays/masked.py in __setitem__(self, key, value)
93 value = [value]
---> 94 value, mask = self._coerce_to_array(value)
95
~/sandbox/pandas/pandas/core/arrays/integer.py in _coerce_to_array(self, value)
417 def _coerce_to_array(self, value) -> Tuple[np.ndarray, np.ndarray]:
--> 418 return coerce_to_array(value, dtype=self.dtype)
419
~/sandbox/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
241 elif not (is_integer_dtype(values) or is_float_dtype(values)):
--> 242 raise TypeError(f"{values.dtype} cannot be converted to an IntegerDtype")
243
TypeError: <U3 cannot be converted to an IntegerDtype
During handling of the above exception, another exception occurred:
AssertionError Traceback (most recent call last)
<ipython-input-3-36a959f691c3> in <module>
1 Int_series = pd.Series(data=[1, 2, 3], dtype="Int64")
----> 2 Int_series.replace("", "ABC", inplace=True) # throws exception
~/sandbox/pandas/pandas/core/series.py in replace(self, to_replace, value, inplace, limit, regex, method)
4449 limit=limit,
4450 regex=regex,
-> 4451 method=method,
4452 )
4453
~/sandbox/pandas/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6662 elif not is_list_like(value): # NA -> 0
6663 new_data = self._mgr.replace(
-> 6664 to_replace=to_replace, value=value, inplace=inplace, regex=regex
6665 )
6666 else:
~/sandbox/pandas/pandas/core/internals/managers.py in replace(self, value, **kwargs)
592 def replace(self, value, **kwargs) -> "BlockManager":
593 assert np.ndim(value) == 0, value
--> 594 return self.apply("replace", value=value, **kwargs)
595
596 def replace_list(
~/sandbox/pandas/pandas/core/internals/managers.py in apply(self, f, align_keys, **kwargs)
400 applied = b.apply(f, **kwargs)
401 else:
--> 402 applied = getattr(b, f)(**kwargs)
403 result_blocks = _extend_blocks(applied, result_blocks)
404
~/sandbox/pandas/pandas/core/internals/blocks.py in replace(self, to_replace, value, inplace, regex, convert)
743 raise
744
--> 745 assert not self._can_hold_element(value), value
746
747 # try again with a compatible block
AssertionError: ABC
```
#### Problem description
The code does not work on Pandas Version 1.0.4. It throws an exception. However, it works on Pandas Version 1.0.1, and also with dtype="int64.
#### Expected Output
No crash. function replace() itself does nothing, since there is no "" in the series.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
</details>
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|
MDU6SXNzdWU2MjkyNDcxNDg=
| 34,531
|
Extreme performance issue in pandas 1.0.3 when setting a new column with DatetimeIndex
|
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| 6
| 2020-06-02T14:24:25Z
| 2021-08-07T22:33:17Z
| null |
NONE
| null |
When adding a column to a DataFrame with one level having a DateTime-like dtype, the dtype of the values to be added is explicitly casted to object type in multi.py if the indexes of the values to be setted and the frames index are not identical in pandas version 1.0.3. Those object typed values are beiing transformed to Timestamps later on. This consumes a lot of time for big dataframes.
Comparing Pandas version 0.22.0 and 1.0.3 yields 0.124 seconds vs. 35.274 seconds on my machine on following reproducable setup:
# build reproducable setup
iterables = [range(10000), pd.date_range('2020-01-01', periods=200)]
idx = pd.MultiIndex.from_product(iterables, names=['id', 'date'])
df = pd.DataFrame(data=np.random.randn(10000 * 200), index=idx, columns=["value"])
new_col = df[df.index.get_level_values(1) != pd.to_datetime('2020-01-01')] # drop first record of each id
print(df.shape, new_col.shape)
# profile performance of set_item
import cProfile
pr = cProfile.Profile()
pr.enable()
df['new_col'] = new_col['value']
pr.disable()
pr.print_stats(sort=2)
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| 629,255,209
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MDU6SXNzdWU2MjkyNTUyMDk=
| 34,532
|
BUG: Groupby sum, different result between 0.25.3 and 1.0.4
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| 7
| 2020-06-02T14:35:06Z
| 2020-09-26T12:54:26Z
| 2020-09-26T12:54:25Z
|
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.
---
This is most likely a known problem, but I did not find an exact issue where to copy this behavior.
I ran into a problem between functionalities between 0.25.3 and 1.0.4.
Now:
```python
import pandas
df = pandas.DataFrame({"id": [1, 1], "value_1": [None, None], "value_2": [0, 0]})
df.loc[df["value_1"].isnull(), "value_1"] = 0
df.groupby("id").sum()
```
#### Problem description
In 1.0.4 the output is:
```python
value_2
id
1 0
```
Is this now the intended behaviour?
This is most likely because:
```python
df.dtypes
id int64
value_1 object
value_2 int64
```
#### Expected Output
In 0.25.3
```python
value_1 value_2
id
1 0 0
```
dtypes are
```python
df.dtypes
id int64
value_1 int64
value_2 int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.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_GB.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
Cython : None
pytest : 5.3.2
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 : None
pytables : None
pytest : 5.3.2
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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MDExOlB1bGxSZXF1ZXN0NDI2NjM1MzA1
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DOC: remove an extra colon
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| 1
| 2020-06-02T14:47:48Z
| 2020-06-02T21:30:44Z
| 2020-06-02T19:56:26Z
|
CONTRIBUTOR
| null |
fix a typo by removing an extra colon
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MjkyOTQ2MzY=
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BUG: pd.Series produces NaN for valid input as per documentation
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| 6
| 2020-06-02T15:25:23Z
| 2021-02-07T16:26:21Z
| 2021-02-07T16:26:21Z
|
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
```python
import pandas as pd
dept = {'CS': 'Engineering',
'ENG': 'Arts',
'PHY': 'Science'}
dept = pd.Series(dept, index=['A', 'B', 'C'])
print(dept)
```
#### Problem description
pandas series is created by passing dict as input data and also index as additional argument. The indexes are updated but the values are changed to NaN. Whereas if I remove the index argument, it works fine.
#### Expected Output
CS Engineering
ENG Arts
PHY Science
dtype: object
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_India.1252
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU2Mjk1MDE0MDI=
| 34,535
|
Pickle incompatibility between 0.25 and 1.0 when saving a MultiIndex dataframe
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| 9
| 2020-06-02T20:44:11Z
| 2021-05-03T22:17:17Z
| 2020-08-05T13:23:38Z
|
NONE
| null |
This seems to have caused the problem described here:
https://stackoverflow.com/questions/61641738/pandas-1-0-cannot-pickle-load-dict-containing-dataframe-with-multiindex
which I'm now also experiencing. I dumped a MultiIndex dataframe containing ndarrays to a pickle on disk under pandas 0.25.x in Python 3.6, and now I'm getting:
`AttributeError: Can't get attribute 'FrozenNDArray' on <module 'pandas.core.indexes.frozen'`
when trying to load it in pandas 1.0.3 (still on Python 3.6). Any suggestions/workarounds? Should I instead open up a new issue?
This solved issue seems related, but is for Python 2.7:
https://github.com/pandas-dev/pandas/issues/31988
This comment in the original rationale for getting rid of `FrozenNDArray` mentions `pandas.compat.pickle_compat.py`, which seems relevant:
https://github.com/pandas-dev/pandas/issues/9031#issuecomment-306659873
_Originally posted by @mspacek in https://github.com/pandas-dev/pandas/pull/29335#issuecomment-628138718_
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|
BUG: where(...) unexpectedly casts None to np.NA for series with pd.Int64Dtype
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| 2020-06-02T20:46:44Z
| 2021-08-03T19:10:49Z
| 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.
---
#### Problem description
When trying to replace all `pd.NA` values with `None` using `where(...)` on a `pd.Series` with dtype `pd.Int64Dtype`, `None` values are automatically coerced to `pd.NA` in a way inconsistent with other dtypes. Additionally, the `try_cast` flag doesn't control this behavior.
#### Expected Output
I expect that when `try_cast=False`, `None` will not be coerced into `np.NA`, and the dtype of the series will instead be cast into `object` to accommodate the newly introduced `None` value, as occurs for other dtypes.
#### Example
In the following:
```python
s = pd.Series([1, 2, 3, pd.NA], dtype=pd.Int64Dtype())
s.where(pd.notnull(s), None)
```
the `None` I'm trying to replace `pd.NA` with is automatically cast back into `pd.NA`. This occurs even though `try_cast` is set to `False` by default, which I would expect to control this casting behavior.
```
0 1
1 2
2 3
3 <NA>
dtype: Int64
```
This is in contrast to the behavior of other dtypes, like numpy's float dtype:
```python
s = pd.Series([1, 2, 3, np.nan], dtype=np.dtype("float64"))
s.where(pd.notnull(s), None)
```
which outputs the series with the dtype coerced to `object` after incorporating `None`:
```
0 1
1 2
2 3
3 None
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
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 : 2.11.1
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 : 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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MDExOlB1bGxSZXF1ZXN0NDI2ODI5MjE3
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|
CI/TST #34131 fixed test_floordiv_axis0_numexpr_path
|
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| 3
| 2020-06-02T21:04:50Z
| 2021-01-02T08:31:59Z
| 2020-06-14T20:46:34Z
|
CONTRIBUTOR
| null |
xref #34131
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Test `/pandas/tests/frame/test_floordiv_axis0_numexpr_path` is slow. It builds a df with 10^6 elements to tests functions `floordiv` and `pow`. The number of elements can be reduced with keeping the same min/max values inside the dataframe.
Before/after timeit comparison:
```
26.6 s ± 1.04 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
37.6 ms ± 3.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
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MDExOlB1bGxSZXF1ZXN0NDI2ODM2OTg3
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|
REF: add to_offset to tslibs namespace
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| 2
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| 2020-06-03T22:32:53Z
| 2020-06-03T22:31:31Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDI2ODYzMjk3
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REF: avoid use of to_perioddelta
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| 2
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| 2021-11-20T23:22:30Z
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|
MEMBER
| null |
tslibs is nominally self-contained, but several offsets methods still rely on DatetimeArray/PeriodArray behavior. This is the first of several steps whittling that away.
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BUG: Index slice an all-zero float sparse series by indexer does not preserve dtypes
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| 3
| 2020-06-03T02:51:33Z
| 2020-07-08T11:47:09Z
| 2020-07-08T11:47:09Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import scipy as sp
import scipy.sparse
spmatrix = sp.sparse.csr_matrix((2, 2))
spmatrix[0, 0] = 1
df = pd.DataFrame.sparse.from_spmatrix(spmatrix)
# work as expected, cool
df.loc[1]
# 0 0.0
# 1 0.0
# Name: 0, dtype: Sparse[float64, 0.0]
# Indexing by index list. I expect 1,0, 0.0, not cool
df.loc[[1]]
# 0 1
# 1 0.0 0
# whenever indexing an all-zero series, it returns int type.
df.loc[[1]].loc[[1]]
# 0 1
# 1 0 0
# checking the dtype, it has been turned into int64 instead of float64
df.loc[[1]].dtypes
# 0 Sparse[float64, 0]
# 1 Sparse[int64, 0]
# dtype: object
# indexing by bool list also not cool
df.loc[[True, True]]
# 0 1
# 0 1.0 0
# 1 0.0 0
# Slicing is cool
df.loc[0:2]
# 0 1
# 0 1.0 0.0
# 1 0.0 0.0
# If we explicitly specify its dtype, the type still not preserved.
import numpy as np
df.astype(np.float32).dtypes
# 0 Sparse[float32, 0.0]
# 1 Sparse[float32, 0.0]
df.astype(np.float32).loc[[1]].dtypes
# 0 Sparse[float64, 0.0]
# 1 Sparse[float64, 0.0]
```
#### Problem description
Index slice an all-zero sparse series by indexer (whether by bool array or index array) results in type int64. It should preserve type.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 57034774bc16958b085ad6ca6d5379b9ac0289b3
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-101-generic
Version : #102-Ubuntu SMP Mon May 11 10:07:26 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1742.g5703477
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : 0.29.19
pytest : 5.4.3
hypothesis : 5.16.0
sphinx : 3.0.4
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
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
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
numba : 0.49.1
```
</details>
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MDU6SXNzdWU2Mjk3MTczODg=
| 34,541
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DOC: documentation link to whatsnew v1.0.4 not working
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| 2020-06-03T06:03:24Z
| 2020-06-04T20:55:17Z
| 2020-06-04T20:55:17Z
|
MEMBER
| null |
#### Location of the documentation
full whatsnew link on
https://github.com/pandas-dev/pandas/releases/tag/v1.0.4
#### Documentation problem
The link to https://pandas.pydata.org/docs/whatsnew/v1.0.4.html is not working though https://pandas.pydata.org/docs/whatsnew/v1.0.3.html works
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> I propose to create a single document that contains all the coding guidelines.
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| 2020-06-03T07:13:59Z
| 2020-06-03T07:14:17Z
| 2020-06-03T07:14:17Z
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NONE
| null |
> I propose to create a single document that contains all the coding guidelines.
perhaps the google style could be a good template or basis for outline http://google.github.io/styleguide/pyguide.html
personally i prefer a well structured reference style document to a informal prose on the subject.
I think that the reason we have the two documents is that the contributing guidelines contains the descriptive introduction for first time contributors and the code style document contains more details. This second document was only recently introduced and adding the content and some migration of content from the contributing guidlines is WIP.
contributions and PRs welcome.
_Originally posted by @simonjayhawkins in https://github.com/pandas-dev/pandas/issues/33851#issuecomment-621145606_
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ENH: Allow join based on conditional statement
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| 2020-06-03T09:34:03Z
| 2020-06-03T09:41:45Z
| null |
NONE
| null |
In pandas, it is only possible to perform joins based on conditional statements that consider whether they provided keys are *equal*. It would be great if pandas provided a way in which joins can be performed based on some other logical condition. This is possible in many SQL engines and allows for elegant solution of specific use cases.
# Examples
## Setup
```python
df1 = pd.DataFrame({'col_a': [1,2,3], 'col_b': ["A", "B", "C"]})
print(df1)
```
```
col_a col_b
0 1 A
1 2 B
2 3 C
```
```python
df2 = pd.DataFrame({'col_a': [0, 2, 3], 'col_c': ["Z", "X", "Y"]})
print(df2)
```
```
col_a col_c
0 0 Z
1 2 X
2 3 Y
```
## Current behavior (equal joins)
Based on conditional ``df1.col_a == df2.col_b``
```python
df_equal = pd.merge(left=df1, right=df2, on="col_a")
print(df_equal)
```
```
col_a col_b col_c
0 2 B X
1 3 C Y
```
## Desired behavior
### Not equal to join
Based on conditional `df1.col_a != df2.col_a`
```
col_a col_b col_c
0 1 A Z
1 1 A Y
2 1 A X
3 2 B Z
4 2 B X
5 3 C Z
6 3 C Y
```
### Greater than join
Based on conditional `df1.col_a > df2.col_a`
```
col_a col_b col_c
0 1 A Z
1 2 B Z
2 3 C Z
3 3 C Y
```
And any other conditional based on logic operators.
# Some considerations
Currently, it it is possible with pandas to join on multiple keys. It would be nice if it is possible to provide multiple logical conditions to perform joining on multiple keys, and allow "mixing" of the logical conditions (i.e. join on `df1.col_a != df2.col_a & df1.col_b == df1.col_c`). Note also that the second conditional is applied to columns of different names (i.e. `col_b` of `df1` and `col_c` of `df2`).
## Real-world scenario
Such joins can be very useful in dataframes that have datetime-like objects stored in them. Consider a dataframe `df_process` that contains `process_id` `process_start_date` and `process_end_date` columns for some business process, and a second dataframe `df_events` that contains a `event_id`, `event_date` column for particular events. If you want to find all events that occur during some business process, you can easily obtain these by applying the conditional join statement:
`df_event.event_date >= df_process.process_start_date & df_event.event_date <= df_process.process_end_date`
# Implementation suggestion
I'm not familiar with how joins are performed in pandas at the moment, but it seems to me that some equality check must be performed to decide to join particular records from the dataframes involved in the join. That means (if this is correct) that there is already some conditional built-in that evaluates to `True` or `False` based on the equality (`==`) comparison. My request should allow passing a different conditional to this part of the pandas join machinery.
Hopefully it's clear what I am requesting!
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DOC: DatetimeIndex day says the same as month
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| 2020-06-03T09:46:35Z
| 2020-06-22T07:23:32Z
| 2020-06-22T07:23:31Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.DatetimeIndex.day.html#pandas.DatetimeIndex.day
#### Documentation problem
Right now it says the same as month, `The month as of January=1, December=12`
#### Suggested fix for documentation
Something along the lines of `Day of the month, from 1 through 31`
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DEPR: Deprecate tshift and integrate it to shift
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| 8
| 2020-06-03T10:41:20Z
| 2021-03-20T02:35:51Z
| 2020-06-15T13:20:34Z
|
MEMBER
| null |
- [x] closes #11631
- xref #34452
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 2020-06-03T10:54:00Z
| 2020-06-09T12:32:44Z
| 2020-06-09T12:32:21Z
|
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
# Your code here
import pandas as pd
# day|month first (no error is raised, and it shouldnt continue normally)
print('First case - year last | no error raised')
df = pd.DataFrame({'timestamp':['11-05-2020','12-05-2020','14-05-2020','13-05-2020','01-06-2020','02-06-2020']})
print(pd.to_datetime(df['timestamp']))
# Year first (an error is raised, thus it is what I would expect)
print('\n\nSecond case - year first | error raised')
df = pd.DataFrame({'timestamp':['2020-11-05','2020-12-05','2020-14-05','2020-13-05','2020-01-06','2020-02-06']})
pd.to_datetime(df['timestamp'])
```
#### Problem description
The function to_datetime do not keep the same date format throughout the column when using inferred format. This happens when the dates to parse do NOT start with the year.
Using the table below as input ('test.csv'):
timestamp
2020-11-05
2020-12-05
2020-14-05
2020-13-05
2020-01-06
2020-02-06
Problem:
1) The problem happens when the dates do not start with the year. In this case, pandas switch the month and day (see row 2 and 3), thus changing the format detected.
0 2020-11-05
1 2020-12-05
2 2020-05-14
3 2020-05-13
4 2020-01-06
5 2020-02-06
2) When the dates start with the year and error is raised (for me this should also happen in the previous case).
ValueError: month must be in 1..12
#### Expected Output
raise an error if the datetime format of the entry values changes in the same column
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.3.2
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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| 629,897,199
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MDU6SXNzdWU2Mjk4OTcxOTk=
| 34,547
|
reindex_like(s, method='ffill') is different than reindex_like(s).fillna(method='ffill')
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| 2020-06-03T10:54:53Z
| 2021-08-07T22:36:28Z
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NONE
| null |
I don't know whether this a bug or a feature, but the behavior is not clear to me after reading the `reindex_like` docs.
There is a note "Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index." - but what it actually does is to fill "as if" the index after reindexing was sorted.
#### Sample code
>>> import pandas as pd
>>> s1 = pd.Series(['[0, 1)', '[1, 3)', '[3, 4)', '[4, 6)', '[6, inf)'], index=[0, 1, 3, 4, 6], dtype='string')
>>> s2 = pd.Series(['']*8, index=[6, 2, 5, 0, 4, 7, 1, 3], dtype='string')
>>>
>>> s1
0 [0, 1)
1 [1, 3)
3 [3, 4)
4 [4, 6)
6 [6, inf)
dtype: string
>>> s2
6
2
5
0
4
7
1
3
dtype: string
>>> s1.reindex_like(s2).fillna(method='ffill')
6 [6, inf)
2 [6, inf)
5 [6, inf)
0 [0, 1)
4 [4, 6)
7 [4, 6)
1 [1, 3)
3 [3, 4)
dtype: string
>>> s1.reindex_like(s2, method='ffill')
6 [6, inf)
2 [1, 3)
5 [4, 6)
0 [0, 1)
4 [4, 6)
7 [6, inf)
1 [1, 3)
3 [3, 4)
dtype: string
I expected the same result with both methods. Should they behave differently?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.4.2
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 : None
pytables : None
pytest : 5.4.2
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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MDU6SXNzdWU2Mjk5Mjk3MDE=
| 34,548
|
Chunksize from json memory consumption as high as without chunksize
|
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| 2
| 2020-06-03T11:47:53Z
| 2020-12-23T16:32:22Z
| 2020-12-23T16:32:22Z
|
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.
---
While working on the PR (#33962) for adding nrows to read_json it was seen that maybe the chunksize parameter for read_json is not doing its task. ASV benchmarks were carried out but as this issue is not related to the PR we decided to create a new issue for it
Here is the link to the paste that has all the results
[paste](https://pastebin.com/E22PAxKe)
The results are nicely added this to this spreadsheet
[spreadsheet](https://docs.google.com/spreadsheets/d/1MjE2iMrlYnWbnfSyizfYI3IbwSK1z760VKeoYfCBeJs/edit?usp=sharing)
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| 629,992,301
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MDExOlB1bGxSZXF1ZXN0NDI3MjAwMDc4
| 34,549
|
Ci #34131 rm slow fixtures
|
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| 2020-06-03T13:25:02Z
| 2020-06-07T08:59:19Z
| 2020-06-07T08:59:01Z
|
CONTRIBUTOR
| null |
- [ x ] Part of #34131
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ FAIL ] tests passed for 11 of 12 files changed. 6 tests fail inside the file `pandas/core/arrays/datetimes.py` , but there are not concerned by this PR. So I decide to push anyway.
I went through tests tagged with `@pytest.mark.slow` and take a look at the duration. The test is unmarked if the test duration is below 1 second.
note: I considered the total time for each combination in case of parameters
A total of 76 flags `slow` were unmark, so we can focus on "real" slow tests.
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MDU6SXNzdWU2MzAwNTUzNzM=
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|
BUG: Series.unique segfaults on invalid unicode
|
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| 2020-06-18T23:08:41Z
| 2020-06-18T23:08:41Z
|
NONE
| null |
**Not present anymore, might be fixed by accident, but no could not find a PR that did that.**
#### Code Sample, a copy-pastable example
```python
import pandas as pd
ser = pd.Series(["\ud83d"])
ser.unique()
```
#### Problem description
```
UnicodeEncodeError: 'utf-8' codec can't encode character '\ud83d' in position 0: surrogates not allowed
Exception ignored in: 'pandas._libs.tslibs.util.get_c_string_buf_and_size'
UnicodeEncodeError: 'utf-8' codec can't encode character '\ud83d' in position 0: surrogates not allowed
Segmentation fault (core dumped)
```
#### Expected Output
Not crashing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-33-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 3.0.3
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 : 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 : 5.4.1
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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BUG: DataFrame.drop_duplicates confuses NULL bytes
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| 4
| 2020-06-03T14:46:30Z
| 2021-10-15T07:03:42Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import pandas.testing as pdt
df = pd.DataFrame({"col": ["", "\0"]})
ser = df["col"].copy()
ser_actual = ser.drop_duplicates()
ser_expected = pd.Series(["", "\0"], name="col")
pdt.assert_series_equal(ser_actual, ser_expected) # passes
df_actual = df.drop_duplicates()
df_expected = pd.DataFrame({"col": ["", "\0"]})
pdt.assert_frame_equal(df_actual, df_expected) # fails, only a single row left
```
#### Problem description
Test fails, esp. note the inconsistent behavior between `Series.drop_duplicates` and `DataFrame.drop_duplicates`.
#### Expected Output
Test passes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-33-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 3.0.3
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 : 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 : 5.4.1
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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| 34,552
|
DOC: fix PR06 errors in Timedeltas docstrings (parameter type)
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|
CONTRIBUTOR
| null |
- [x] closes #28253
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
DOC: fix PR06 (parameter type) errors in Timestamp docstrings
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- [ ] closes #xxxx
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CLN: Update imports
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MDExOlB1bGxSZXF1ZXN0NDI3MzYwMDEw
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REF: simplify wrapping in apply_index
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| 2020-06-03T17:49:42Z
| 2020-06-03T22:28:14Z
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MEMBER
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Make the caller (DatetimeArray._add_offset) responsible for wrapping, so we can move the liboffsets methods towards only needing the ndarrays.
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ENH: Rolling Calculations Ignore Extended Window Specification
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| 0
| 2020-06-03T18:02:48Z
| 2020-10-31T15:34:38Z
| 2020-10-31T15:34:38Z
|
NONE
| null |
#### Is your feature request related to a problem?
Rolling windows API is not coherent to `scipy.signal` API.
#### Describe the solution you'd like
Support for weighted rolling window functions in `pandas` is based on `scipy.signal`. Precisely, `pandas` uses `scipy.signal.get_window` in order to get weights for a specific window specification. The issue is that `scipy` allows more accurate window specification. Namely, one can use non-symetric exponential window as following.
```python
scipy.signal.windows.exponential(window_size, sym=False)
```
Moreover, `pandas` API specification requires paramter `tau` what is not necesaray in fact. So, API could be even simpler.
A possible solution to use `win_type` in order to`getattrib` of `scipy.signal.windows` module. If there is a function named as `win_type` then `pandas` should invoke the function with all passed paramters to aggregation function over rolling window. In the snippet below, `.sum()` implies invocation of `scipy.signal.windows.exponential(2, sym=False)`.
```python
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
df.rolling(2, win_type='exponential').sum(sym=True)
```
#### API breaking implications
API won't be broken. However, I would like to relax some contraints. Namely, aggregations over rolling window should be calculated without explicit window parameter specification.
#### Describe alternatives you've considered
Alternative solution looks like the following. It is simple and a bit verbose.
```python
window = sp.signal.windows.exponential(2, sym=False)
df.rolling(2, min_periods=2) \
.apply(lambda x: (x * window).sum())
```
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MDU6SXNzdWU2MzAyMjAyNTU=
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|
BUG: DataFrame size overflows on Windows
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[] | null | 5
| 2020-06-03T18:22:22Z
| 2020-09-04T15:03:07Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
a = np.ones(2**30 + 1, dtype=np.int8)
df = pd.DataFrame.from_dict({'a': a, 'b': a})
print(df.size) # --> -2147483646
print(df.sum(axis=0)) # -->
# a NaN
# b NaN
# dtype: float64
```
#### Problem description
On windows, dataframes with more than 2^31 elements (max 32 bit int) result in negative sizes and nan results from operations performed on them.
This problem only seems to exist on windows (64-bit Windows-10-10.0.18362-SP0). This seems to be from the assumption that `np.prod(self.shape)` will not overflow, but numpy [makes no such guarantee](https://numpy.org/doc/stable/reference/generated/numpy.prod.html).
#### Expected Output
positive size, non-nan sum
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.16.4
pytz : 2018.9
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
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 : 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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MDExOlB1bGxSZXF1ZXN0NDI3Mzg2NTgz
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DOC: Add bcpandas to Ecosystem in docs
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| 4
| 2020-06-03T18:46:57Z
| 2020-06-14T18:01:51Z
| 2020-06-14T15:51:17Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Hi, I wrote this utility which I use in production almost daily at my job, and it's been super helpful for me personally to fill a gap - slow writes from Pandas to MS SQL. Figured I'd suggest it in the pandas ecosystem if it could be useful to others.
Wasn't sure where to put it, and didn't want to start a new section, so I put it in `Out-of-core`. It appeared to be sorted alphabetically, so I put it in that order.
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MDExOlB1bGxSZXF1ZXN0NDI3NDQxNjY0
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CLN: remove ABCTimedelta
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MEMBER
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Hoping we can eventually remove ABCTimestamp too, not sure.
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BUG: fix resolution_string docstring
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MEMBER
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broken off from #34499
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DOC: Fixed PR06 (wrong parameter type) in pandas.Timestamp
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ENH: more functions like between_time()
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| 2020-06-03T23:43:19Z
| 2021-08-07T22:40:58Z
| 2021-08-07T22:40:58Z
|
NONE
| null |
I have a time index for data frame in full date time format: eg,
> 2019-04-18 11:23:00
> 2020-06-03 11:00:00
Currently it is easy to slice with `between_time("08:00","09:00")` to get rows from 8am-9am across all the days I have in the data frame.
However, if I want to get all data in April across years, I wish I could do` between_month(4,4)`. Similarly, `between_day(1,15)`, or `between_year(2019,2020)`
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CLN: circular/runtime imports in tslibs
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MEMBER
| null |
ATM tzconversion depends on timedeltas (for delta_to_nanoseconds), which throws a wrench in the erstwhile dependency hierarchy. By making the one usage of delta_to_nanoseconds a runtime import, we make it possible to remove a bunch of other runtime imports.
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BUG: pd.Series/DataFrame.truncate() drops multiindex names
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| 1
| 2020-06-04T02:24:56Z
| 2020-06-20T20:04:28Z
| 2020-06-20T20:04:28Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# create multiindex with names L1/L2
mi = pd.MultiIndex.from_product([[1, 2, 3, 4], ['A', 'B']], names=['L1', 'L2'])
s1 = pd.Series(range(mi.shape[0]), index=mi)
# truncate drops the names in the index of s2
s2 = s1.truncate(before=2, after=3)
```
#### Problem description
The current version seems to drop index level names. This was not the case in pandas version 0.23.4
#### Expected Output
```
s1
Out[1]:
L1 L2
1 A 0
B 1
2 A 2
B 3
3 A 4
B 5
4 A 6
B 7
dtype: int64
# In pandas 0.23.4
s2
Out[2]:
L1 L2
2 A 2
B 3
3 A 4
B 5
dtype: int64
# In pandas 1.0.3 (drops axis labels L1, L2 along truncation axis)
s2
Out[2]:
2 A 2
B 3
3 A 4
B 5
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.13-1.el7.elrepo.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 : 46.4.0.post20200518
Cython : 0.29.17
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
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 : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
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 : None
numba : 0.49.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDI3NTY4NjMz
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BUG: Fix na_rep when using pd.NA in _format_strings (#33950)
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NONE
| null |
- [x] closes #33950
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: address FIXMEs in liboffsets
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MDU6SXNzdWU2MzA1NjM4MzI=
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ENH: pipe function enhancement
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| 2020-08-21T17:05:45Z
| 2020-06-04T14:19:19Z
|
NONE
| null |
feature request about `.pipe`
many data science need know the details of processing...
so if we define lots of functions to processing the raw data, and we need the details of each processing, I'm not familiar to the pipe function, but this way may be elegant way to summary this processing...
df.pipe(func0).pipe(func1).pipe(func2).summary()
which summary can summary all your functions in pipe line,such as shape or some others function that can apply to the result of dataframe/series
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DOC: The argument of pandas.DataFrame.to_markdown is unfriendly
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CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_markdown.html
#### Documentation problem
The document of `pandas.DataFrame.to_markdown` is unfriendly because many options are shown as `**kwargs`.
I think these arguments should be documented explicitly like a `to_csv`.
https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_csv.html
#### Suggested fix for documentation
All arguments supported by `to_markdown` should be shown at document.
If don't mind, I would like to tackle this improvement.
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DOC: Wrong description of behaviour Series.pop()
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| 2020-06-04T09:54:31Z
| 2020-06-23T17:35:08Z
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|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.pop.html
#### Documentation problem
Documentation describes that the operation would not return the popped column and would instead return the modified version of the Series (making it appear as a non-inplace function). Actual behavior however returns the value of the popped column and executes the operation in-place.
#### Suggested fix for documentation
Use the documentation of the similar and well described method of Dataframe.pop() and adjust it for Series (return a value instead of a Series).
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MDExOlB1bGxSZXF1ZXN0NDI3NzgyMzY1
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Export InvalidIndexError
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| 6
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| 2020-06-14T20:37:30Z
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CONTRIBUTOR
| null |
- [x] closes #xxxx (there is no such an issue)
- [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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MDU6SXNzdWU2MzA3ODEyMjQ=
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BUG: problem with index set_names when names is a list
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[] | null | 2
| 2020-06-04T12:32:01Z
| 2020-08-21T17:05:14Z
| 2020-06-04T13:31:26Z
|
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.
---
#### Problem description
I have a question:
why raise a ValueError here https://github.com/pandas-dev/pandas/blob/1ce1c3c1ef9894bf1ba79805f37514291f52a9da/pandas/core/indexes/base.py#L1239 when values can be a list and thus len of the list can be > 1
and please also check this: https://github.com/pandas-dev/pandas/blob/1ce1c3c1ef9894bf1ba79805f37514291f52a9da/pandas/core/indexes/base.py#L1246 if it's a list why select the first element?
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TST: Added test to check that the freqstr attribute of the index is p…
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| 2020-06-04T19:10:00Z
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|
NONE
| null |
…reserved after a shift operation. Run black and flake8.
- [x] closes #21275
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (omitted, test only)
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BUG: Pandas changes dtypes of columns when no float (or other) assignments are done to this column
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| 2020-06-09T22:31:41Z
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NONE
| null |
```python
df = pd.DataFrame()
df["a"] = int(0)
for col_name in ["b", "c"]:
df[col_name] = 0.
print(df.dtypes)
for idx, b in enumerate([1,2,3]):
df.loc[df.shape[0]] = {
'a' : int(idx),
'b' : float(b),
'c' : float(b),
}
print(df.dtypes)
```
#### Problem description
In the above example, pandas changes the dtype of the column `a` of dataframe `df` from _int64_ to _float64_, while there is no operation, that assigns non-integer value to this column.
#### Expected Output
Currently, the output of this code is:
```
a int64
b float64
c float64
dtype: object
a float64
b float64
c float64
dtype: object
```
while the expected one is:
```
a int64
b float64
c float64
dtype: object
a int64
b float64
c float64
dtype: object
```
<details>
<summary>pd.show_versions() output</summary>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.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 : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
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 : None
numba : None
```
</details>
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DOC: fixed PR06 in pandas.Timedeltas
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CONTRIBUTOR
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| 630,934,297
|
MDU6SXNzdWU2MzA5MzQyOTc=
| 34,575
|
CI is failing due to benchmark failing at arithmetic.ApplyIndex.time_apply_index (0.65%)
|
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| 2020-06-04T15:31:09Z
| 2020-06-04T19:04:33Z
| 2020-06-04T19:04:33Z
|
CONTRIBUTOR
| null |
ASV benchmarks are failing on the benchmarks for arithmetic.ApplyIndex.time_apply_index
I believe the benchmarks are failing after #34499 was merged.
The latest benchmark failed [link](https://github.com/pandas-dev/pandas/pull/33962/checks?check_run_id=738856351)
Related errors
```
##[error][ 0.65%] ··· arithmetic.ApplyIndex.time_apply_index 3/10 failed
[ 0.65%] ··· =================================== ==========
offset
----------------------------------- ----------
<YearEnd: month=12> 1.55±0ms
<YearBegin: month=1> 1.37±0ms
<QuarterEnd: startingMonth=3> 1.71±0ms
<QuarterBegin: startingMonth=3> 1.59±0ms
<MonthEnd> 2.25±0ms
<MonthBegin> 1.34±0ms
<DateOffset: days=2, months=2> 3.16±0ms
<BusinessDay> failed
<SemiMonthEnd: day_of_month=15> failed
<SemiMonthBegin: day_of_month=15> failed
=================================== ==========
[ 0.65%] ···· For parameters: <BusinessDay>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 1397, in pandas._libs.tslibs.offsets.BusinessDay.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
For parameters: <SemiMonthEnd: day_of_month=15>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 2319, in pandas._libs.tslibs.offsets.SemiMonthOffset.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
For parameters: <SemiMonthBegin: day_of_month=15>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 2319, in pandas._libs.tslibs.offsets.SemiMonthOffset.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
```
I am not sure what was the right way to report this thus created this issue. Do tell me if this is an incorrect place, incorrect way or incorrect language
|
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MDU6SXNzdWU2MzA5MzQ5NDY=
| 34,576
|
index=False by default when exporting dataframes
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[] | null | 6
| 2020-06-04T15:32:05Z
| 2021-11-07T09:37:06Z
| 2021-08-07T22:43:35Z
|
NONE
| null |
#### Is your feature request related to a problem?
Every single time I have exported a dataframe (usually with .to_csv), I have not needed the index.
#### Describe the solution you'd like
Change the default from True to False.
#### API breaking implications
All code that has not explicitely set index=True should make it.
#### Describe alternatives you've considered
Leave things as they are now.
Not using the index is something usual or am I biased by my own experience?
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MDU6SXNzdWU2MzA5ODg1OTk=
| 34,577
|
BUG: read_table cannot handle multi-character separators in memory_map mode
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| 2020-06-04T16:45:40Z
| 2020-06-05T04:52:14Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas
dataframe = pandas.read_table('file.txt',
header=None,
sep=' - ',
names=['key', 'value'],
memory_map=True)
```
#### Data Sample
```
key1 - value1
key2 - value2
key3 - value3
```
#### Problem description
I'm getting an exception:
> **Exception has occurred: TypeError**
cannot use a string pattern on a bytes-like object
from this line:
https://github.com/pandas-dev/pandas/blob/14eda586582513c68f32f0a1f00ecfe8d6c7f8f3/pandas/io/parsers.py#L2472
since `f.readline()` returns a byte-string.
And if I change separator to `sep=b' - '`, a similar exception:
> **Exception has occurred: TypeError**
cannot use a bytes pattern on a string-like object
gets raised from the following line:
https://github.com/pandas-dev/pandas/blob/14eda586582513c68f32f0a1f00ecfe8d6c7f8f3/pandas/io/parsers.py#L2475
since `for line in f:` yields normal strings.
#### Expected Output
Exception should not be raised either with `sep=' - '` or with `sep=b' - '`.
Not sure which one is the correct, although I see no issues with `sep=' - '` when `memory_map=True` is not set.
#### Workaround
Changing lines
https://github.com/pandas-dev/pandas/blob/14eda586582513c68f32f0a1f00ecfe8d6c7f8f3/pandas/io/parsers.py#L2474-L2475
to
```python
while True:
line = f.readline()
if not line:
break
yield pat.split(line.strip())
```
fixes `read_table` for byte-string separators (e.g. `sep=b' - '`).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
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 : 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 : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI3OTgzNTEx
| 34,578
|
Solve ci issue related to #34555
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[] | null | 1
| 2020-06-04T17:29:12Z
| 2020-06-04T17:30:34Z
| 2020-06-04T17:30:34Z
|
CONTRIBUTOR
| null |
- [x] closes #34575
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
solved issues with benchmarks
benchmarks running fine now
Current right output
```
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[ 0.00%] ·· Benchmarking existing-py_home_moha_venv_pandas-dev_bin_python
[ 50.00%] ··· arithmetic.ApplyIndex.time_apply_indexgit ok
[ 50.00%] ··· =================================== ==========
offset
----------------------------------- ----------
<YearEnd: month=12> 2.05±0ms
<YearBegin: month=1> 1.56±0ms
<QuarterEnd: startingMonth=3> 4.41±0ms
<QuarterBegin: startingMonth=3> 2.50±0ms
<MonthEnd> 4.32±0ms
<MonthBegin> 2.31±0ms
<DateOffset: days=2, months=2> 5.67±0ms
<BusinessDay> 10.4±0ms
<SemiMonthEnd: day_of_month=15> 14.5±0ms
<SemiMonthBegin: day_of_month=15> 14.2±0ms
=================================== ==========
```
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solves ci issues with #34575
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| 2
| 2020-06-04T18:04:59Z
| 2020-06-04T19:04:33Z
| 2020-06-04T19:04:33Z
|
CONTRIBUTOR
| null |
- [x] closes #34575
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
solved issues with benchmarks
benchmarks running fine now
Current right output
```
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[ 0.00%] ·· Benchmarking existing-py_home_moha_venv_pandas-dev_bin_python
[ 50.00%] ··· arithmetic.ApplyIndex.time_apply_indexgit ok
[ 50.00%] ··· =================================== ==========
offset
----------------------------------- ----------
<YearEnd: month=12> 2.05±0ms
<YearBegin: month=1> 1.56±0ms
<QuarterEnd: startingMonth=3> 4.41±0ms
<QuarterBegin: startingMonth=3> 2.50±0ms
<MonthEnd> 4.32±0ms
<MonthBegin> 2.31±0ms
<DateOffset: days=2, months=2> 5.67±0ms
<BusinessDay> 10.4±0ms
<SemiMonthEnd: day_of_month=15> 14.5±0ms
<SemiMonthBegin: day_of_month=15> 14.2±0ms
=================================== ==========
```
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MDU6SXNzdWU2MzEwNzc3OTU=
| 34,580
|
DEPR: DateOffset.apply and DateOffset.apply_index
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| 7
| 2020-06-04T19:06:03Z
| 2020-07-15T12:25:34Z
| 2020-07-15T12:25:34Z
|
CONTRIBUTOR
| null |
```pytb
In [1]: import pandas as pd
In [2]: rng = pd.date_range(start="1/1/2000", periods=10, freq="T")
In [3]: pd.offsets.YearEnd()
Out[3]: <YearEnd: month=12>
In [4]: pd.offsets.YearEnd().apply_index(rng)
Out[4]:
DatetimeIndex(['2000-12-31 00:00:00', '2000-12-31 00:01:00',
'2000-12-31 00:02:00', '2000-12-31 00:03:00',
'2000-12-31 00:04:00', '2000-12-31 00:05:00',
'2000-12-31 00:06:00', '2000-12-31 00:07:00',
'2000-12-31 00:08:00', '2000-12-31 00:09:00'],
dtype='datetime64[ns]', freq='T')
In [5]: pd.__version__
Out[5]: '1.0.4'
```
vs
```python
In [1]: import pandas as pd
In [2]: rng = pd.date_range(start="1/1/2000", periods=10, freq="T")
In [3]: pd.offsets.YearEnd().apply_index(rng)
Out[3]:
array([978220800000000000, 978220860000000000, 978220920000000000,
978220980000000000, 978221040000000000, 978221100000000000,
978221160000000000, 978221220000000000, 978221280000000000,
978221340000000000])
In [4]: pd.__version__
Out[4]: '1.1.0.dev0+2021.g74538104bc'
```
---
see https://github.com/pandas-dev/pandas/pull/34579#discussion_r435465450
errors are here: https://github.com/pandas-dev/pandas/pull/33962#issuecomment-638834572
these don't need to be exposed to users and we should deprecate; it seems that in the offsets reorg some of the offsets Month? are no longer exposing these methods as well (so need to test).
```[ 0.65%] ···· For parameters: <BusinessDay>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 1397, in pandas._libs.tslibs.offsets.BusinessDay.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
For parameters: <SemiMonthEnd: day_of_month=15>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 2319, in pandas._libs.tslibs.offsets.SemiMonthOffset.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
For parameters: <SemiMonthBegin: day_of_month=15>
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 1058, in main_run
result = benchmark.do_run()
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 537, in do_run
return self.run(*self._current_params)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 627, in run
samples, number = self.benchmark_timing(timer, min_repeat, max_repeat,
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 694, in benchmark_timing
timing = timer.timeit(number)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/timeit.py", line 177, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py", line 599, in <lambda>
func = lambda: self.func(*param)
File "/home/runner/work/pandas/pandas/asv_bench/benchmarks/arithmetic.py", line 469, in time_apply_index
offset.apply_index(self.rng)
File "pandas/_libs/tslibs/offsets.pyx", line 87, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
File "pandas/_libs/tslibs/offsets.pyx", line 2319, in pandas._libs.tslibs.offsets.SemiMonthOffset.apply_index
AttributeError: 'PeriodIndex' object has no attribute '_addsub_int_array'
```
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MDU6SXNzdWU2MzExMTQyMjA=
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|
BUG: read_csv with quoting=csv.QUOTE_NONNUMERIC error trying to convert header row
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| 2020-06-04T20:10:56Z
| 2020-08-09T11:11:15Z
| 2020-08-09T11:11:15Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
attempt to read_csv file with first row header. Using option quoting=csv.QUOTE_NONNUMERIC and header=0. causes:
"ValueError: could not convert string to float: 'frid'"
**(frid is my column name defined in the header row)
My text type fields are quoted and numeric are not. Looks like the quoting=csv.QUOTE_NONNUMERIC tries to convert non-quoted values to numeric, however it is also doing this to the header row.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Most methods of dumping to csv do not quote the header row names. read_csv() function should not attempt to convert unquoted header row names.
#### Expected Output
Header row should not be subject to data conversion, as header names are always text.
#### Output of ``pd.show_versions()``
0.25.1
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
0.25.1
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TST: series offset artithmetic tests
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[] | null | 6
| 2020-06-04T21:53:57Z
| 2020-08-01T14:07:20Z
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|
NONE
| null |
…racting a series of DateOffsets to / from a series of Timestamps (test_series_add_daytime_offset, test_series_add_daytime_offset)
- [x] closes #19211
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (omitted, test only)
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MDU6SXNzdWU2MzExODI3NzE=
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ENH: Add number of missing values and cardinality to df.describe()
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[] | null | 2
| 2020-06-04T22:19:19Z
| 2021-08-07T22:44:25Z
| null |
CONTRIBUTOR
| null |
It would be really handy to have the number of missing values as well as the cardinality, i.e. the lenght of df.x.value_counts() for each column x in the describe table.
Count is indirectly reflecting this value, but ultimatily you have to think about the corner and first calculate the length of the dataframe before it leads to the numbe of missing values.
The cardinality is important for categorical features.
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DOC: DataFrame.reindex improve summary and return type
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- [x] closes #33888
- [x] tests added / passed (passes `scripts/validate_docstrings.py pandas.DataFrame.reindex`)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry (Not Required)
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REF: ensure we have offset objects in plotting functions
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MDU6SXNzdWU2MzEyMTEzNDg=
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ENH: to_offset "year"/"quarter"/"month"/"day"/...?
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| 2020-06-04T23:36:20Z
| 2021-08-07T22:44:42Z
| null |
MEMBER
| null |
In the process of untangling Resolution/FreqGroup/PeriodDtype/Offsets im finding it would be helpful if `to_offset("year")` behaved like `to_offset("Y")` etc. But apparently these are specifically [deprecated](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/scalar/period/test_period.py#L724).
This test was introduced [here](https://github.com/pandas-dev/pandas/commit/4da9b15f58cfcde998d6279a90d55a170145f8c2) (commit but no PR) and is listed as closing #11854 and #12540, but neither of those really make it clear to me why we wouldn't allow these.
Any objection to having to_offset recognize these strings?
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MDExOlB1bGxSZXF1ZXN0NDI4MTYzNDI0
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|
ENH: Resolutions for month/qtr/year
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| 5
| 2020-06-05T00:59:20Z
| 2020-06-09T23:39:59Z
| 2020-06-09T22:32:55Z
|
MEMBER
| null |
This does three things
1) Use Resolution objects instead of strings in the places in core.indexes. This makes whats going on much clearer.
2) To make 1) possible, extend Resolution to include month, quarter, and year. This means that `pd.period_range("2017", periods=3, freq="A").resolution` is now "year" instead of "day", which is more accurate.
3) with year/quarter/month added, `Resolution` and `FreqGroup` will be re-labellings of each other, so we can de-duplicate them. They will end up living in tslibs.dtypes.
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REF: implement c_FreqGroup, FreqGroup in tslibs.dtypes
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| 2020-06-05T03:56:42Z
| 2020-06-08T23:11:06Z
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|
MEMBER
| null |
These are already used in libperiod, better to be explicit about it.
Coming up: make FreqGroup into an enum with helper methods to convert between FreqGroup/PeriodDtype/DateOffset/Resolution
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MDExOlB1bGxSZXF1ZXN0NDI4MjEyNzEz
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DataFrame.truncate drops MultiIndex names
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| 2020-06-05T04:23:41Z
| 2020-06-21T03:24:54Z
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|
MEMBER
| null |
- [x] closes #34564
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [x] added tests
- [ ] passes tests (fixed for `DataFrame`, still issue for `Series`)
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MDExOlB1bGxSZXF1ZXN0NDI4MjE2MTUx
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REF: mix PeriodPseudoDtype into PeriodDtype
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| 2020-06-05T04:37:11Z
| 2020-06-08T16:25:09Z
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MEMBER
| null |
This will allow us to start getting rid of `get_freq_code` usages and related libfrequencies functions that are unnecessarily roundabout.
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MDExOlB1bGxSZXF1ZXN0NDI4MjI0NDUy
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DOC: updated base.py and datetimes.py in core/indexes for PR08
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MDU6SXNzdWU2MzEzMTUzMjA=
| 34,592
|
BUG: Series.where doesn't work with empty lists
|
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| 1
| 2020-06-05T05:08:05Z
| 2020-06-08T02:15:44Z
| 2020-06-08T02:15:44Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas. (Checked 1.0.3)
- [ ] (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
>>> pd.Series([], dtype=float).where([])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/nix/store/vqd2pmsja84h36wz6c878bgbpz46cy2h-python3.7-pandas-1.0.3/lib/python3.7/site-packages/pandas/core/generic.py", line 8919, in where
cond, other, inplace, axis, level, errors=errors, try_cast=try_cast
File "/nix/store/vqd2pmsja84h36wz6c878bgbpz46cy2h-python3.7-pandas-1.0.3/lib/python3.7/site-packages/pandas/core/generic.py", line 8673, in _where
raise ValueError(msg.format(dtype=cond.dtype))
ValueError: Boolean array expected for the condition, not float64
```
For comparison, if the list isn't empty, it works:
```python
>>> pd.Series([42], dtype=float).where([True])
0 42
dtype: int64
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
If `where` accepts lists as its mask argument, it should work for empty lists too. Either don't support lists at all or support empty lists too.
#### Expected Output
```
>>> pd.Series([], dtype=float).where([])
Series([], dtype: float64)
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.41
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : None
setuptools : None
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MzEzNjE3NzQ=
| 34,593
|
DOC: Can't figure out how to get exponential working
|
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[] | null | 8
| 2020-06-05T07:02:26Z
| 2020-06-18T00:11:58Z
| 2020-06-18T00:11:58Z
|
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.
---
**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
to_test = pd.DataFrame({ "data": [1,2,3,4,5,6,7,8,9,10] })
exponent = to_test.rolling((3,4), win_type="exponential").mean()
print(exponent)
```
```error
ValueError: exponential window requires tau
```
```python
import pandas as pd
to_test = pd.DataFrame({ "data": [1,2,3,4,5,6,7,8,9,10] })
exponent = to_test.rolling(3, win_type="exponential", tau=4).mean()
print(exponent)
```
```error
TypeError: rolling() got an unexpected keyword argument 'tau'
```
#### Problem description
I can't get exponential rolling working, and there's no documentation on this.
#### Expected Output
The exponential mean of the first 4 values
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.0.0
Cython : 0.29.17
pytest : 5.4.2
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 : 7.14.0
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 : 0.17.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI4MzE0MTA2
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|
Improve document for **kwargs argument of pandas.DataFrame.to_markdown
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| 2020-06-07T21:00:00Z
| 2020-06-07T20:59:52Z
|
CONTRIBUTOR
| null |
- [x] closes #34568
- [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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BUG: fix Series.where(cond) when cond is empty
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| 1
| 2020-06-05T10:13:34Z
| 2020-06-08T08:31:23Z
| 2020-06-08T02:15:45Z
|
MEMBER
| null |
- [ ] closes #34592
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #21947 for fix for DataFrame.where
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MDU6SXNzdWU2MzE1MjMwMTI=
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|
BUG: DataFrame.attrs are lost when writing to HDF5
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open
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| null |
[] | null | 4
| 2020-06-05T11:32:34Z
| 2021-05-19T16:52:48Z
| 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. Version 1.0.3
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [9]: df = pd.DataFrame(index=[1, 2, 3], columns=list("abcde"), data=np.ones((3,5)))
In [10]: df.attrs["foo"] = "bar"
In [11]: df.to_hdf("test_df.h5", key="key")
In [12]: df_from_h5 = pd.read_hdf("test_df.h5")
In [13]: assert df.attrs == df_from_h5.attrs, "attrs have gone"
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-17-1fab1bc115de> in <module>
----> 1 assert df.attrs == df_from_h5.attrs, "attrs have gone"
AssertionError: attrs have gone
```
#### Problem description
The metadata stored in attributes is gone after the DataFrame was read back from disk. I understand the attrs dict is WIP. I hope this issue will help to move this forward! Thanks!
Related: #29062
#### Expected Output
The attrs should be the same as in the DataFrame written to disk.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.15-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.utf8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1
Cython : 0.29.19
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
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 : None
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
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 : 0.15.2.dev47+g33a66d63
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.1
</details>
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|
BUG: Reindex doesn't add NaN when using level=1
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[] | null | 1
| 2020-06-05T12:12:34Z
| 2020-06-06T09:56:45Z
| 2020-06-05T16:02:26Z
|
CONTRIBUTOR
| null |
- [ ] 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
index = pd.MultiIndex.from_tuples([('bar', 'one'),
('bar', 'two'),
('baz', 'one'),
('baz', 'two'),
('foo', 'one'),
('foo', 'two'),
('qux', 'one'),
('qux', 'two')],
names=['first', 'second'])
np.random.seed(0)
s = pd.Series(np.random.randn(8), index=index)
first second
bar one 1.764052
two 0.400157
baz one 0.978738
two 2.240893
foo one 1.867558
two -0.977278
qux one 0.950088
two -0.151357
dtype: float64
```
When I reindex by level 1.
Current output:
```python
s.reindex(['two','one','three'],level=1)
first second
bar two 0.400157
one 1.764052
baz two 2.240893
one 0.978738
foo two -0.977278
one 1.867558
qux two -0.151357
one 0.950088
dtype: float64
```
#### Expected Output
This output can reproduced by doing `s.unstack().reindex(['two','one','three'],axis=1).stack(dropna=False) ` or `s.reindex(pd.MultiIndex.from_product([s.index.levels[0],['two','one','three']]))`
```python
first second
bar two 0.400157
one 1.764052
three NaN
baz two 2.240893
one 0.978738
three NaN
foo two -0.977278
one 1.867558
three NaN
qux two -0.151357
one 0.950088
three NaN
dtype: float64
```
#### Problem description
`reindex` is dropping `NaN` while using it on a `MultiIndex` over level 1. The normal behaviour of `reindex` produces `NaN` for new indices, It should also in the above case.
#### 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 : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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 : 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 : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MzE1NzEzNzc=
| 34,598
|
BUG: Groupby: Int-Datatype (of group-by-columns) always casted to int64
|
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| false
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[] | null | 4
| 2020-06-05T12:59:55Z
| 2020-09-04T13:42:52Z
| 2020-09-04T13:42:48Z
|
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
import numpy as np
df = pd.DataFrame({'col1':[1,2,3],'col2':[10,11,12]})
df['col1'] = df['col1'].astype(np.int8)
print(df.dtypes)
print(df.groupby('col1', as_index=False)['col2'].count().dtypes)
```
#### Problem description
**Before the groupby:** The dtype of col1 is np.int8:
Output of
```python
print(df.dtypes):
col1 int8
col2 int64
dtype: object
```
**After the groupby** the dtype of col1 is np.int64
Output of
```python
print(df.groupby('col1', as_index=False)['col2'].count().dtypes):
col1 int64
col2 int64
dtype: object
```
#### Expected Output
```python
print(df.groupby('col1', as_index=False)['col2'].count().dtypes) gives output:
col1 int8
col2 int64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.4
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 10.0.1
setuptools : 45.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NDI4NDQ5NDUy
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|
BUG: Pandas changes dtypes of columns when no float (or other) assignments are done to this column #34573
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- [ ] tests added / passed
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| 4
| 2020-06-05T15:09:44Z
| 2021-08-09T18:29:37Z
| 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. (1.0.3)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [105]: index2000 = pd.DatetimeIndex(["2000-01-01 00:00:00.000000", "2000-01-01 00:00:00.000001", "2000-01-01 00:00:00.000003"])
In [106]: pd.Series(index=index2000, data=[0, None, 1]).interpolate(method="values")
Out[106]:
2000-01-01 00:00:00.000000 0.000000
2000-01-01 00:00:00.000001 0.347826
2000-01-01 00:00:00.000003 1.000000
dtype: float64
In [107]: index2001 = pd.DatetimeIndex(["2001-01-01 00:00:00.000000", "2001-01-01 00:00:00.000001", "2001-01-01 00:00:00.000003"])
In [108]: pd.Series(index=index2001, data=[0, None, 1]).interpolate(method="values")
Out[108]:
2001-01-01 00:00:00.000000 0.000000
2001-01-01 00:00:00.000001 0.347826
2001-01-01 00:00:00.000003 1.000000
dtype: float64
In [109]: index2020 = pd.DatetimeIndex(["2020-01-01 00:00:00.000000", "2020-01-01 00:00:00.000001", "2020-01-01 00:00:00.000003"])
In [110]: pd.Series(index=index2020, data=[0, None, 1]).interpolate(method="values")
Out[110]:
2020-01-01 00:00:00.000000 0.000000
2020-01-01 00:00:00.000001 0.333333
2020-01-01 00:00:00.000003 1.000000
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Interpolating over datetime index doesn't work correctly - or there's something really weird. In the above examples, I only changed the year. I have a value 0 at microsecond 0, value 1 at microsecond 3. Then, interpolating to microsecond 1 should give a value 1/3. But the value appears to be something different for some years. Why?
#### Expected Output
I would have expected 0.333333 interpolated value in all cases, not just for year 2020.
```python
In [105]: index2000 = pd.DatetimeIndex(["2000-01-01 00:00:00.000000", "2000-01-01 00:00:00.000001", "2000-01-01 00:00:00.000003"])
In [106]: pd.Series(index=index2000, data=[0, None, 1]).interpolate(method="values")
Out[106]:
2000-01-01 00:00:00.000000 0.000000
2000-01-01 00:00:00.000001 0.333333
2000-01-01 00:00:00.000003 1.000000
dtype: float64
In [107]: index2001 = pd.DatetimeIndex(["2001-01-01 00:00:00.000000", "2001-01-01 00:00:00.000001", "2001-01-01 00:00:00.000003"])
In [108]: pd.Series(index=index2001, data=[0, None, 1]).interpolate(method="values")
Out[108]:
2001-01-01 00:00:00.000000 0.000000
2001-01-01 00:00:00.000001 0.333333
2001-01-01 00:00:00.000003 1.000000
dtype: float64
In [109]: index2020 = pd.DatetimeIndex(["2020-01-01 00:00:00.000000", "2020-01-01 00:00:00.000001", "2020-01-01 00:00:00.000003"])
In [110]: pd.Series(index=index2020, data=[0, None, 1]).interpolate(method="values")
Out[110]:
2020-01-01 00:00:00.000000 0.000000
2020-01-01 00:00:00.000001 0.333333
2020-01-01 00:00:00.000003 1.000000
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.4.41
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.2.1
pip: 19.0.3
setuptools: 41.0.0
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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DOC/BUG: correlation between series of different lengths
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[] | null | 1
| 2020-06-05T15:13:14Z
| 2021-08-07T22:50:11Z
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NONE
| null |
Hi,
Is it intentional that pandas does not through an error message when computing a correlation between two series of different lengths?
e.g. pd.Series([1,2,3]).corr(pd.Series([3,2]))
Thanks,
-Matan
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| 2
| 2020-06-05T16:06:11Z
| 2020-11-25T20:51:28Z
| 2020-11-25T20:51:28Z
|
NONE
| null |
- [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(
[[1, 2], [3, 4], [5, 6], [7, 8]],
index=pd.MultiIndex.from_product([['a', 'b'], [0, 1]]))
In [137]: df
Out[137]:
0 1
a 0 1 2
1 3 4
b 0 5 6
1 7 8
df2 = pd.DataFrame([[9, 10], [11, 12]], index=[1, 0])
In [139]: df2
Out[139]:
0 1
1 9 10
0 11 12
df.loc[('a', df2.index), :] = df2.values
In [141]: df
Out[141]:
0 1
a 0 9 10
1 11 12
b 0 5 6
1 7 8
```
#### Problem description
When assigning to `df` in the last step, I use the index of `df2` to correctly align the rows of the two DataFrames, i.e., to select the order of elements in `df` to assign to. It turns out that this order is ignored in MultiIndex indexing. I believe this is dangerous, because `df2.values` on the right hand side is the only way to pass the values in `df2` to `df` as long as `df2` hasn't got the same levels as `df` (cf. #10440). Unsuspecting users who believe that MultiIndex indexing will work as indexing on a normal Index (I haven't found a warning about this in the docs) will most likely miss that their values have been sorted unexpectedly.
#### Expected Output
```python
In [141]: df
Out[141]:
0 1
a 0 11 12
1 9 10
b 0 5 6
1 7 8
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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 : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDI4NTc4MTE4
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DOC: updated io/pytables.py to fix PR08
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| 3
| 2020-06-05T16:50:48Z
| 2020-06-08T01:52:36Z
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CONTRIBUTOR
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BUG: Positional Arguments Passed as a Keyword Argument in Custom Rolling Aggregation
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| 6
| 2020-06-05T16:53:39Z
| 2020-06-15T12:53:18Z
| 2020-06-15T12:53:18Z
|
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.
---
#### Problem description
According to [docstring][1] of `Rolling.apply` arguments `args` and `kwargs` allow to pass to a custom function positional and keyword arguments enumerated in `kwargs`. However, `Rolling.apply` passes both of them as keyword arguments. In other word, it passes keyword `args ` consisting of "positional" arguments. This is definitely buggy behaviour.
The reason is that a closure function is built in [a wrong way][2]. The issue states for `cython` backend as well as `numba` backend. The possible should looks like the following.
```diff
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index a01a753e8..32360848a 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -1313,11 +1313,11 @@ class _Rolling_and_Expanding(_Rolling):
window_func = partial(
self._get_cython_func_type("roll_generic"),
- args=args,
- kwargs=kwargs,
+ *args,
raw=raw,
offset=offset,
func=func,
+ **kwargs,
)
def apply_func(values, begin, end, min_periods, raw=raw):
```
[1]: https://github.com/pandas-dev/pandas/blob/1.0.x/pandas/core/window/rolling.py#L1237-L1240
[2]: https://github.com/pandas-dev/pandas/blob/1.0.x/pandas/core/window/rolling.py#L1314-L1321
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#34569 Added proper description for pandas.Series.pop
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| 12
| 2020-06-05T18:35:17Z
| 2020-06-23T17:35:15Z
| 2020-06-23T17:35:08Z
|
CONTRIBUTOR
| null |
- [x] closes #34569
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Updated documentation will look like this: https://i.imgur.com/Tl9JO5X.png
This is my first commit to the project. If any changes/modifications let me know.
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PERF: isinstance ABCIndexClass and ABCExtensionArray
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| 2
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| 2020-06-06T09:16:17Z
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|
MEMBER
| null |
```
%timeit isinstance(pd.Series, pd.core.dtypes.generic.ABCIndexClass)
# 607 ns ± 12.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - PR
# 937 ns ± 37.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - Master
%timeit isinstance(pd.Series, pd.core.dtypes.generic.ABCExtensionArray)
# 634 ns ± 26.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - PR
# 759 ns ± 40.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - Master
%timeit isinstance(pd.Series, pd.core.dtypes.generic.ABCSeries)
# 638 ns ± 49.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - PR
# 644 ns ± 20.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) - Master
```
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Coding Style Guidline.rst
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|
NONE
| null |
Created a unified version of the Coding Style Guidelines
Extracted rules from various pandas website information as well as submission scripts that verify style requirements
Work in progress
- [x] closes #33851
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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I can't get this to work
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NONE
| null |
https://github.com/pandas-dev/pandas/blob/1ce1c3c1ef9894bf1ba79805f37514291f52a9da/pandas/core/frame.py#L6253-L6323
if I run the lines I get a syntax error with the ":" after column. If I try to import and test on a dataframe i get an error that .explode is not an attribute. :'(
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TST: mark tzlocal tests as slow, closes #34413
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| 1
| 2020-06-05T20:50:23Z
| 2020-06-08T02:05:00Z
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|
MEMBER
| null |
- [x] closes #34413
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Add ddof to cov methods
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- [ x] closes #28823
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: make DateOffset apply_index methods operate on ndarrays where feasible
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CLN: update tslibs/tseries test locations/imports
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| 3
| 2020-06-06T00:49:42Z
| 2020-06-18T00:12:10Z
| 2020-06-18T00:11:58Z
|
CONTRIBUTOR
| null |
For example, std needs to specify when win_types is gaussian. However, std should be specified in the operation argument, not as one of the rolling arguments. This change is to clarify this point.
- [ ] closes #34593
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Improve document for **kwargs argument of pandas.Series.to_markdown
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|
CONTRIBUTOR
| null |
This is a follow-up PR of #34594 .
`pandas.Series.to_markdown` has also same argument.
This PR adds example for `pandas.Series.to_markdown` with tabulate option.
Related Issue: #34568
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