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https://api.github.com/repos/pandas-dev/pandas/issues/30314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30314/events | https://github.com/pandas-dev/pandas/pull/30314 | 539,359,471 | MDExOlB1bGxSZXF1ZXN0MzU0MzUxMDcw | 30,314 | DOC/CLN: move NDFrame.groupby to (DataFrame|Series).groupby | {
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} | 10 | 2019-12-17T23:08:14Z | 2020-01-01T11:37:23Z | 2020-01-01T03:35:13Z | CONTRIBUTOR | null | By moving this method up to dataFrame/Series, we get better doc strings and more precise type hints.
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https://api.github.com/repos/pandas-dev/pandas/issues/30315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30315/events | https://github.com/pandas-dev/pandas/issues/30315 | 539,424,975 | MDU6SXNzdWU1Mzk0MjQ5NzU= | 30,315 | join doesn't add rsuffix to all columns - multiindex - intermittent | {
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | null | 5 | 2019-12-18T03:10:36Z | 2019-12-19T09:47:52Z | 2019-12-19T09:46:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from pandas import Timestamp
d = {('collection_end_time',
'is_public_holiday_flag'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): True, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): True},
('collection_end_time',
'is_school_holiday_flag'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): True, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): True},
('detector_volume',
'1200_07'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): 17.0, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): 14.0},
('detector_volume',
'1200_08'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): 33.0, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): 14.0},
('ratio',
'1200'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): 0.5, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): 0.9333333333333333},
('segment',
'100'): {Timestamp('2017-01-01 00:05:00+1100', tz='Australia/Sydney'): 0.0, Timestamp('2017-01-01 00:10:00+1100', tz='Australia/Sydney'): 0.0}}
df1 = pd.DataFrame.from_dict(d)
columns = ['detector_volume']
if 'detector_error_flag' in df_volume.columns.get_level_values(0):
columns.append('detector_error_flag')
if 'ratio' in df_volume.columns.get_level_values(0):
columns.append('ratio')
df2 = df1[columns].shift(periods=5, freq=pd.offsets.Minute())
df1.join(df2, how='inner', rsuffix=f'_prev5')
```
#### Problem description
The last line of code sometimes works and sometimes doesn't add a suffix to all columns. You can see in this screenshot that `_prev5` has not been added to the last `('ratio', '1200')` column.
Please note that this issue is **intermittent** - right now the way I workaround it is by doing a "factory reset runtime" in colab which seems to deploy a new vm. Then the join [usually] works again. This leads me to believe there is an access to an uninitialized portion of memory. I had this issue a couple of months ago but it went away by itself so I didn't bother opening an issue. But I've hit it again yesterday and today.
#### Expected Output
Last column should be `('ratio_prev5', '1200')`
#### Output of ``pd.show_versions()``
<details>
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.29.14
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 5.5.0
pandas_datareader: 0.7.4
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.3
sqlalchemy : 1.3.11
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30316/events | https://github.com/pandas-dev/pandas/issues/30316 | 539,470,922 | MDU6SXNzdWU1Mzk0NzA5MjI= | 30,316 | Not possible to concatenate DataFrames with sparse data without copying | {
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{
"color": "009800",
"default": false,
"description": "Sparse Data Type",
"id": 49182326,
"name": "Sparse",
"node_id": "MDU6TGFiZWw0OTE4MjMyNg==",
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"description"... | closed | false | null | [] | null | 2 | 2019-12-18T06:04:38Z | 2019-12-18T07:52:34Z | 2019-12-18T07:52:22Z | NONE | null | #### Code Sample
```python
a = pd.DataFrame([0, 0, 1, 0, 2], columns=['a'])
a = a.astype(pd.SparseDtype('float', 0.0))
b = pd.DataFrame([0, 2, 3, 0, 2], columns=['b'])
b = b.astype(pd.SparseDtype('float', 0.0))
pd.concat([a, b], axis=1, copy=False)
```
#### Problem description
Hello! Since `SparseArray` is no longer a subclass of `numpy.ndarray` (pandas>=0.24.0) it is not possible to concatenate dataframes with `SparseArray` dtype using `pandas.concat`.
The problem is `SparseArray` has not attribute `view` which is used in `pandas.core.internals.managers.py` at function `concatenate_block_managers` when parameter `copy` is `False`.
#### Actual Output
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d61b58c595ad> in <module>
----> 1 pd.concat([a, b], axis=1, copy=False)
~/.conda/envs/pandas_dev/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
256 )
257
--> 258 return op.get_result()
259
260
~/.conda/envs/pandas_dev/lib/python3.7/site-packages/pandas/core/reshape/concat.py in get_result(self)
471
472 new_data = concatenate_block_managers(
--> 473 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy
474 )
475 if not self.copy:
~/.conda/envs/pandas_dev/lib/python3.7/site-packages/pandas/core/internals/managers.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
2044 values = values.copy()
2045 elif not copy:
-> 2046 values = values.view()
2047 b = b.make_block_same_class(values, placement=placement)
2048 elif is_uniform_join_units(join_units):
AttributeError: 'SparseArray' object has no attribute 'view'
```
#### Output of ``pd.show_versions()``
<details>
### INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.1.3.el7.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30317/events | https://github.com/pandas-dev/pandas/issues/30317 | 539,519,101 | MDU6SXNzdWU1Mzk1MTkxMDE= | 30,317 | .to_parquet() to a StringIO() causes python to crash | {
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"color": "5319... | closed | false | null | [] | null | 9 | 2019-12-18T08:01:40Z | 2020-10-13T18:38:00Z | 2019-12-19T05:23:49Z | NONE | null | ```python
import pandas as pd
from io import StringIO
df = pd.DataFrame({'x': [1, 2, 2, 4]})
df.to_parquet('some_file') # works fine
s = StringIO()
df.to_parquet(s) # this causes the crash with following error
```
Error:
```
libc++abi.dylib: terminating with uncaught exception of type parquet::ParquetException: Arrow error: IOError: string argument expected, got 'bytes'
[1] 42832 abort python
```
Edit:
The cause for the error is that instead of a String buffer, we should use Bytes buffer (io.BytesIO). But anyhow, the exception should be handled and shouldn't cause the interpreter itself to die. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30318/events | https://github.com/pandas-dev/pandas/pull/30318 | 539,528,872 | MDExOlB1bGxSZXF1ZXN0MzU0NDg4MzAy | 30,318 | TST: add test for Series.item backed with ExtensionArray | {
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} | 0 | 2019-12-18T08:26:17Z | 2019-12-18T12:31:08Z | 2019-12-18T12:31:08Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/30175
cc @jbrockmendel
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https://api.github.com/repos/pandas-dev/pandas/issues/30319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30319/events | https://github.com/pandas-dev/pandas/pull/30319 | 539,596,834 | MDExOlB1bGxSZXF1ZXN0MzU0NTQzNjcz | 30,319 | DOC: Remove reference to moving windows regressions | {
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} | 1 | 2019-12-18T10:37:27Z | 2019-12-19T22:29:42Z | 2019-12-18T12:24:11Z | CONTRIBUTOR | null | Remove outdated reference to moving regression which was dropped
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 5 | 2019-12-18T12:03:13Z | 2020-01-24T04:25:17Z | 2020-01-24T04:25:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import json
import pandas as pd
test_data = [{"col": "31900441201190696999"}, {"col": "Text"}]
test_json = json.dumps(test_data)
pd.read_json(test_json)
```
#### Problem description
The current behaviour doesn't return a dateframe for a valid JSON. Note when the number is smaller, it works fine. It also works when only big numbers are present. It would be cool to have it work with big numbers as it works for small numbers.
#### Expected Output
A dataframe with a number and string
```
col
0 3.190044e+19
1 Text
```
#### Output of ``pd.read_json()``
<details>
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 592, in read_json
result = json_reader.read()
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 717, in read
obj = self._get_object_parser(self.data)
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 739, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 855, in parse
self._try_convert_types()
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 1151, in _try_convert_types
lambda col, c: self._try_convert_data(col, c, convert_dates=False)
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 1131, in _process_converter
new_data, result = f(col, c)
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 1151, in <lambda>
lambda col, c: self._try_convert_data(col, c, convert_dates=False)
File ".../.venv/lib/python3.6/site-packages/pandas/io/json/_json.py", line 927, in _try_convert_data
new_data = data.astype("int64")
File ".../.venv/lib/python3.6/site-packages/pandas/core/generic.py", line 5882, in astype
dtype=dtype, copy=copy, errors=errors, **kwargs
File ".../.venv/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 581, in astype
return self.apply("astype", dtype=dtype, **kwargs)
File ".../.venv/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 438, in apply
applied = getattr(b, f)(**kwargs)
File ".../.venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 559, in astype
return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
File ".../.venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 643, in _astype
values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
File ".../.venv/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 707, in astype_nansafe
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
File "pandas/_libs/lib.pyx", line 547, in pandas._libs.lib.astype_intsafe
OverflowError: Python int too large to convert to C long
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30321/events | https://github.com/pandas-dev/pandas/pull/30321 | 539,658,911 | MDExOlB1bGxSZXF1ZXN0MzU0NTk0NzQw | 30,321 | Switching %r for repr(op_str) in test-expressions.py for issue 29886 | {
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} | 1 | 2019-12-18T12:44:47Z | 2019-12-18T19:32:58Z | 2019-12-18T19:32:46Z | CONTRIBUTOR | null | Swaps the following in pandas/tests/test_expressions.py:
From:
`msg = "operator %r not implemented for bool dtypes"`
`f = getattr(operator, opname)`
`err_msg = re.escape(msg % op_str)`
To:
`msg = f"operator {repr(op_str)} not implemented for bool dtypes"`
`f = getattr(operator, opname)`
`err_msg = re.escape(msg)`
This is for [#29886](https://github.com/pandas-dev/pandas/issues/29886) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30322 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30322/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30322/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30322/events | https://github.com/pandas-dev/pandas/pull/30322 | 539,690,087 | MDExOlB1bGxSZXF1ZXN0MzU0NjIwODgy | 30,322 | ENH: add ExtensionArray.to_numpy to have control over conversion to numpy array | {
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} | 19 | 2019-12-18T13:45:15Z | 2020-01-07T16:26:17Z | 2020-01-07T16:26:03Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/30038
Would still need to add this to the other arrays with NA (IntegerArray, StringArray), and need to pass through such option from Series.to_numpy. But already putting this up to check if we are OK with such interface and behaviour. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30323/events | https://github.com/pandas-dev/pandas/issues/30323 | 539,719,595 | MDU6SXNzdWU1Mzk3MTk1OTU= | 30,323 | json_normalize skips an entry in a pymongo cursor | {
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"description": "read_json, to_json, json... | open | false | null | [] | null | 4 | 2019-12-18T14:36:29Z | 2021-03-02T12:41:59Z | null | NONE | null | I am sorry for not being able to provide a reproducible example, but any attempt to reduce the problem to something limited makes the problem disappear.
```python
In [54]: res = client.events.api.longterm.find({'foo': 'bar'})
In [55]: res.count()
Out[55]: 76845
In [56]: len(pd.io.json.json_normalize(res))
Out[56]: 76844
In [57]: res = client.events.api.longterm.find({'foo': 'bar'})
In [58]: len(pd.io.json.json_normalize(list(res)))
Out[58]: 76845
```
#### Problem description
I have a pretty large collection of documents in MongoDB, which I am querying using pymongo. The resulting cursor is passed to `pd.io.json.json_normalize` to convert the resulting data into a data frame. In one example, which I am unfortunately unable to reduce to something reproducible, a single element of the 76845 entries in the cursor is not present in the resulting data frame. If I convert the cursor to a list before using `json_normalize`, all entries are present. The affected document itself looks completely sane and without anything suspicious. Moreover, the default for `json_normalize` should be to raise errors and not to swallow rows.
#### Expected Output
All 76845 rows are present in the result when passing the cursor directly to `json_normalize`.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.3-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
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 : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30324 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30324/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30324/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30324/events | https://github.com/pandas-dev/pandas/issues/30324 | 539,786,620 | MDU6SXNzdWU1Mzk3ODY2MjA= | 30,324 | errors='ignore' does not work on df.replace({col : type}) if col not found | {
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"description": "Incorrect or improved er... | open | false | null | [] | null | 3 | 2019-12-18T16:27:15Z | 2021-07-25T02:58:23Z | null | NONE | null | This may be considered a feature request
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'a' : [1, 2, 3]})
df.astype({'b' : str}, errors='ignore')
KeyError: 'Only a column name can be used for the key in a dtype mappings argument.'
```
#### Problem description
A KeyError is still raised if the column name is not found. I'd think `errors='ignore'` should suppress this error, which would make it consistent with other methods, such as `df.rename(errors='ignore')`.
#### Expected Output
Unchanged dataframe.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30325/events | https://github.com/pandas-dev/pandas/issues/30325 | 539,812,992 | MDU6SXNzdWU1Mzk4MTI5OTI= | 30,325 | Inconsistent Data Types and Failure to Recognize Type Annotations | {
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[examples.zip](https://github.com/pandas-dev/pandas/files/3979600/examples.zip)
#### Code Sample, a copy-pastable example if possible
```python
#%%
import matplotlib as ml
import pandas as pd
import re
#%%
dataFile1 = 'bitcoin_cash_price.csv'
dataFile2 = 'dash_price.csv'
#%%
def cleanData(data: str) -> (int,None):
cleanedData = re.findall("[0-9]+", data)
out = ""
for stuff in cleanedData:
out = out + stuff
if out != '':
return int(out)
else:
return None
# %%
bitcoinDF = pd.read_csv(dataFile1)
dashPriceDF = pd.read_csv(dataFile2)
# %%
#Market Cap is initially of type string
bitcoinDF["Market Cap"].apply(type)
# %%
#The cleanData function is applied with an type annotation of (int,None)
bitcoinDF["Market Cap"] = bitcoinDF["Market Cap"].apply(cleanData)
#%%
#Pandas casts them as float
bitcoinDF["Market Cap"].apply(type)
# %%
#The second data set is also a str
dashPriceDF["Market Cap"].apply(type)
#%%
#cleanData is once again applied
dashPriceDF["Market Cap"] = dashPriceDF["Market Cap"].apply(cleanData)
#%%
#Pandas correctly classifies it as type int and the behavior is inconsistent compared to the other dataset
dashPriceDF["Market Cap"].apply(type)
#The expected behavior should be that pandas will go off a type annotation when calling apply before guessing at the data type.
```
#### Problem description
The current behavior is an issue because the pandas library ignores any type annotations when using the apply method. When using the apply method the type annotation should take precedence before pandas attempts to guess at the data type.
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https://api.github.com/repos/pandas-dev/pandas/issues/30326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30326/events | https://github.com/pandas-dev/pandas/pull/30326 | 539,821,567 | MDExOlB1bGxSZXF1ZXN0MzU0NzMwNzg2 | 30,326 | TST: follow-up to #30318 | {
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} | 1 | 2019-12-18T20:48:07Z | 2019-12-18T22:57:13Z | 2019-12-18T22:57:05Z | CONTRIBUTOR | null | * Refer to public API
* Fixed formatting
* Remove ref to undocumented function | {
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} | 8 | 2019-12-18T20:51:47Z | 2019-12-20T02:40:31Z | 2019-12-20T02:40:30Z | CONTRIBUTOR | null | I think that's because we include a `:toctree:` on line 4,832
```
.. autosummary::
:toctree: ../reference/api/
read_sql_table
read_sql_query
read_sql
DataFrame.to_sql
```
We likely don't need that `toctree`. Deleting it may fix the issue.
To check, we probably need to do a full doc build. | {
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} | 3 | 2019-12-18T21:30:56Z | 2019-12-21T11:22:16Z | 2019-12-19T15:43:50Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.show_versions()
import seaborn as sns
```
#### Problem description
installed master with `pip install https://github.com/pandas-dev/pandas/archive/master.zip`
things that use the version don't work
<details>
<summary>show_versions()</summary>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-3d232a07e144> in <module>()
----> 1 pd.show_versions()
15 frames
/usr/local/lib/python3.6/dist-packages/pandas/util/_print_versions.py in show_versions(as_json)
94 for modname in deps:
95 mod = import_optional_dependency(
---> 96 modname, raise_on_missing=False, on_version="ignore"
97 )
98 ver: Optional[str]
/usr/local/lib/python3.6/dist-packages/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version)
84 )
85 try:
---> 86 module = importlib.import_module(name)
87 except ImportError:
88 if raise_on_missing:
/usr/lib/python3.6/importlib/__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
/usr/local/lib/python3.6/dist-packages/pandas_datareader/__init__.py in <module>()
1 from ._version import get_versions
----> 2 from .data import (DataReader, Options, get_components_yahoo,
3 get_dailysummary_iex, get_data_enigma, get_data_famafrench,
4 get_data_fred, get_data_google, get_data_moex,
5 get_data_morningstar, get_data_quandl, get_data_stooq,
/usr/local/lib/python3.6/dist-packages/pandas_datareader/data.py in <module>()
5 import warnings
6
----> 7 from pandas_datareader.av.forex import AVForexReader
8 from pandas_datareader.av.quotes import AVQuotesReader
9 from pandas_datareader.av.sector import AVSectorPerformanceReader
/usr/local/lib/python3.6/dist-packages/pandas_datareader/av/__init__.py in <module>()
1 import os
2
----> 3 from pandas_datareader.base import _BaseReader
4 from pandas_datareader._utils import RemoteDataError
5
/usr/local/lib/python3.6/dist-packages/pandas_datareader/base.py in <module>()
8 from pandas import read_csv, concat
9 from pandas.io.common import urlencode
---> 10 from pandas_datareader.compat import bytes_to_str, string_types, binary_type, \
11 StringIO
12
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py in <module>()
9 PANDAS_VERSION = LooseVersion(pd.__version__)
10
---> 11 PANDAS_0190 = (PANDAS_VERSION >= LooseVersion('0.19.0'))
12 PANDAS_0200 = (PANDAS_VERSION >= LooseVersion('0.20.0'))
13 PANDAS_0210 = (PANDAS_VERSION >= LooseVersion('0.21.0'))
/usr/lib/python3.6/distutils/version.py in __ge__(self, other)
68
69 def __ge__(self, other):
---> 70 c = self._cmp(other)
71 if c is NotImplemented:
72 return c
/usr/lib/python3.6/distutils/version.py in _cmp(self, other)
335 if self.version == other.version:
336 return 0
--> 337 if self.version < other.version:
338 return -1
339 if self.version > other.version:
TypeError: '<' not supported between instances of 'str' and 'int'
</details>
<details>
<summary>import seaborn as sns</summary>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-46-52e5c5e254b6> in <module>()
----> 1 import seaborn as sns
2 cmap_green_0 = sns.light_palette("green", as_cmap=True, reverse=True)
3 cmap_red_1 = sns.light_palette("red", as_cmap=True)
4 table_styles = [
5 {'selector': 'th', 'props': [('font-size', '0.9px'), ('padding', '0')]},
5 frames
/usr/local/lib/python3.6/dist-packages/seaborn/__init__.py in <module>()
4
5 # Import seaborn objects
----> 6 from .rcmod import *
7 from .utils import *
8 from .palettes import *
/usr/local/lib/python3.6/dist-packages/seaborn/rcmod.py in <module>()
3 import functools
4 import matplotlib as mpl
----> 5 from . import palettes, _orig_rc_params
6
7
/usr/local/lib/python3.6/dist-packages/seaborn/palettes.py in <module>()
10 from .external.six.moves import range
11
---> 12 from .utils import desaturate, set_hls_values, get_color_cycle
13 from .colors import xkcd_rgb, crayons
14
/usr/local/lib/python3.6/dist-packages/seaborn/utils.py in <module>()
15
16 from distutils.version import LooseVersion
---> 17 pandas_has_categoricals = LooseVersion(pd.__version__) >= "0.15"
18 mpl_ge_150 = LooseVersion(mpl.__version__) >= "1.5.0"
19
/usr/lib/python3.6/distutils/version.py in __ge__(self, other)
68
69 def __ge__(self, other):
---> 70 c = self._cmp(other)
71 if c is NotImplemented:
72 return c
/usr/lib/python3.6/distutils/version.py in _cmp(self, other)
335 if self.version == other.version:
336 return 0
--> 337 if self.version < other.version:
338 return -1
339 if self.version > other.version:
TypeError: '<' not supported between instances of 'str' and 'int'
</details>
#### Expected Output
<details>
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0+unknown
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.29.14
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 5.5.0
pandas_datareader: 0.7.4
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.3
sqlalchemy : 1.3.11
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
</details>
#### Output of ``pd.show_versions()``
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-3d232a07e144> in <module>()
----> 1 pd.show_versions()
15 frames
/usr/local/lib/python3.6/dist-packages/pandas/util/_print_versions.py in show_versions(as_json)
94 for modname in deps:
95 mod = import_optional_dependency(
---> 96 modname, raise_on_missing=False, on_version="ignore"
97 )
98 ver: Optional[str]
/usr/local/lib/python3.6/dist-packages/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version)
84 )
85 try:
---> 86 module = importlib.import_module(name)
87 except ImportError:
88 if raise_on_missing:
/usr/lib/python3.6/importlib/__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
/usr/local/lib/python3.6/dist-packages/pandas_datareader/__init__.py in <module>()
1 from ._version import get_versions
----> 2 from .data import (DataReader, Options, get_components_yahoo,
3 get_dailysummary_iex, get_data_enigma, get_data_famafrench,
4 get_data_fred, get_data_google, get_data_moex,
5 get_data_morningstar, get_data_quandl, get_data_stooq,
/usr/local/lib/python3.6/dist-packages/pandas_datareader/data.py in <module>()
5 import warnings
6
----> 7 from pandas_datareader.av.forex import AVForexReader
8 from pandas_datareader.av.quotes import AVQuotesReader
9 from pandas_datareader.av.sector import AVSectorPerformanceReader
/usr/local/lib/python3.6/dist-packages/pandas_datareader/av/__init__.py in <module>()
1 import os
2
----> 3 from pandas_datareader.base import _BaseReader
4 from pandas_datareader._utils import RemoteDataError
5
/usr/local/lib/python3.6/dist-packages/pandas_datareader/base.py in <module>()
8 from pandas import read_csv, concat
9 from pandas.io.common import urlencode
---> 10 from pandas_datareader.compat import bytes_to_str, string_types, binary_type, \
11 StringIO
12
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py in <module>()
9 PANDAS_VERSION = LooseVersion(pd.__version__)
10
---> 11 PANDAS_0190 = (PANDAS_VERSION >= LooseVersion('0.19.0'))
12 PANDAS_0200 = (PANDAS_VERSION >= LooseVersion('0.20.0'))
13 PANDAS_0210 = (PANDAS_VERSION >= LooseVersion('0.21.0'))
/usr/lib/python3.6/distutils/version.py in __ge__(self, other)
68
69 def __ge__(self, other):
---> 70 c = self._cmp(other)
71 if c is NotImplemented:
72 return c
/usr/lib/python3.6/distutils/version.py in _cmp(self, other)
335 if self.version == other.version:
336 return 0
--> 337 if self.version < other.version:
338 return -1
339 if self.version > other.version:
TypeError: '<' not supported between instances of 'str' and 'int'
</details>
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} | 4 | 2019-12-18T21:40:10Z | 2020-01-21T21:11:55Z | 2020-01-21T21:11:55Z | CONTRIBUTOR | null | I'm currently testing a numpy deprecation to assess its impact on the ecosystem (https://github.com/numpy/numpy/pull/10615). To this end, I'd like to run pandas tests with `-Werror`. Doing that, I see that pandas throws a bunch of warnings at other places. Are those intentional? Perhaps pandas should test warning-free out of the box. | {
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} | 0 | 2019-12-18T23:58:18Z | 2019-12-20T16:31:12Z | 2019-12-20T11:56:25Z | MEMBER | null | - [x] closes #12724
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} | 0 | 2019-12-19T01:55:08Z | 2019-12-19T14:08:35Z | 2019-12-19T14:08:35Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1348.g18bd98fde'
In [2]: from pandas.core.dtypes.cast import infer_dtype_from_scalar
In [3]: # this is fine
...: infer_dtype_from_scalar(pd.Interval(0, 1), pandas_dtype=False)
Out[3]: (numpy.object_, Interval(0, 1, closed='right'))
In [4]: # this is should infer interval[int64] as dtype
...: infer_dtype_from_scalar(pd.Interval(0, 1), pandas_dtype=True)
Out[4]: (numpy.object_, Interval(0, 1, closed='right'))
```
#### Problem description
`infer_dtype_from_scalar` with `pandas_dtype=True` infers the dtype for `Interval` objects as `np.object_` instead of as an `IntervalDtype`.
#### Expected Output
I'd expect `Out[4]` to have the dtype as `IntervalDtype(subtype=np.int64)`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 18bd98fde954806cf0247aa961cc2e9e567871ba
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.26.0.dev0+1348.g18bd98fde
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.3.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
pytest : 5.3.2
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 1 | 2019-12-19T03:40:19Z | 2019-12-19T17:48:38Z | 2019-12-19T17:42:19Z | MEMBER | null | Its superfluous AFAICT | {
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} | 1 | 2019-12-19T03:54:04Z | 2019-12-19T18:19:39Z | 2019-12-19T17:41:47Z | MEMBER | null | also fail-fast and get an annotation in the bargain | {
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} | 1 | 2019-12-19T05:07:01Z | 2019-12-19T18:09:27Z | 2019-12-19T17:43:15Z | MEMBER | null | in the next pass we'll make create_axes return a new object, and instead of altering a bunch of attributes in create_axes, we'll pass them to the constructor for our new object just before returning
Doing all that makes for a pretty big diff, so this splits out some pieces. The biggest one is implementing _get_blocks_and_items, which I find helpful since its a big chunk of code that i dont have to keep re-checking if it alters state. | {
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} | 16 | 2019-12-19T10:11:40Z | 2020-01-21T21:12:02Z | 2020-01-21T21:11:56Z | CONTRIBUTOR | null | Fixes #30334.
Some of the warnings are coming from within pandas (like the FutureWarning on truediv), some are numpy warnings (like the nan-warning on sign). All in all changes are minor.
Tests are passing with `-Werror` for me now. | {
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```python
df = pd.DataFrame({k: np.random.random(100) for k in "ABC"})
# wrong output
df.boxplot(medianprops=dict(color="C1", lw=2))
# right output
plt.boxplot(df.transpose(), medianprops=dict(color="C1", lw=2))
```
#### Problem description
The problem comes from the boxplot function in a dataframe. This function is linked to the matplotlib function. I got an issue when I try to change the color of the medians using the `medianprops` argument of the function (which is a matplotlib argument). If I use the same instruction directly with the boxplot function in matplotlib I got the right behavior. But, I can change the `linewidth` or `linestyle` for example.
Here I put a screenshot of what I obtain if I use the `boxplot` method from pandas or directly from matplotlib. You can see that using the matplotlib function, the color of the median line is "correct" (the one I asked) while you cannot change the color of the line from the pandas function.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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"description": "pandas testing functions... | open | false | null | [] | null | 3 | 2019-12-19T10:38:31Z | 2021-07-25T02:59:43Z | null | MEMBER | null |
#### Code Sample, a copy-pastable example if possible
```python
>>>
>>> import pandas as pd
>>> pd.__version__
'0.26.0.dev0+1328.g03dffb1eb'
>>>
>>> import pandas.testing as tm
>>> s1 = pd.Series([10000000000000000], dtype="Int64")
>>> s1
0 10000000000000000
dtype: Int64
>>>
>>> s2 = pd.Series([9999999999999999], dtype="Int64")
>>> s2
0 9999999999999999
dtype: Int64
>>>
>>> tm.assert_series_equal(s1,s2)
>>>
```
#### Problem description
does not raise
#### Expected Output
`AssertionError: ExtensionArray are different`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 03dffb1eb312d1bc535e26f43e32c11e0584c8c5
python : 3.7.3.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 : en_GB.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+1328.g03dffb1eb
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.1
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
discovered while investigating #30268 | {
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} | 7 | 2019-12-19T16:28:18Z | 2020-02-11T23:05:12Z | 2020-02-11T23:05:06Z | CONTRIBUTOR | null | On master, this takes a boolean mask on a 10,000 element series take me ~300us
```python
In [2]: s = pd.Series(np.arange(10000))
...: m1 = np.zeros(len(s), dtype="bool")
...: m2 = pd.array(m1, dtype="boolean")
In [3]: %timeit s[m1]
275 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Much of that time is spent in a `try / except` inside `Index.get_value`
```python
%load_ext line_profiler
%lprun -f pd.Index.get_value s[m1]
4481 1 1.0 1.0 0.1 try:
4482 1 626.0 626.0 63.9 return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
4483 1 1.0 1.0 0.1 except KeyError as e1:
```
That can never succeed for a boolean mask. By skipping that path entirely, we improve perf on this example by ~2x
```python
In [3]: %timeit s[m1]
155 µs ± 4.33 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
We might be able to restructure `Index.get_value` or `Series.__getitem__` a bit to not go down this path when we have a boolean ndarray as a mask. | {
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} | 7 | 2019-12-19T16:41:57Z | 2020-01-24T13:10:30Z | 2019-12-27T16:58:06Z | MEMBER | null | - [ ] closes #11052
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-12-19T17:26:29Z | 2020-04-03T04:33:48Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure()
data = [['Auto', 'A', 10], ['Auto', 'A', 2], ['Telco', 'B', 15], ['Manf', 'C'],
['Retail', 'A', ], ['Retail', 'B', ]]
df = pd.DataFrame(data, columns = ['INDUSTRY', 'PRODUCT', 'COST'])
table_byindustry = pd.pivot_table(df, index=['INDUSTRY'], columns=['PRODUCT'], values='COST',
fill_value=0, margins=True, margins_name='Total', aggfunc='count')
ax = sns.heatmap(table_byindustry, cmap='RdYlGn', linewidths=.1, annot=True, vmin=0, vmax=100, fmt="g", xticklabels=True)
ax.set_ylabel("Industry", fontsize=12)
ax.set_xlabel("Product", fontsize=12, labelpad=1)
ax.xaxis.tick_bottom()
ax.set_ylim(bottom=5,top=0)
```
#### Problem description
Pivot Table Margins ("Total") isn't showing 0 values for rows, yet will do so for columns (e.g., Product C). As a result, the heatmap is empty for the "Total" column related to the rows ('Manf' and 'Retail') in my example. It is treating those rows as NaN for Margin, and setting fill_value at 0 doesn't fix it. See attached image. Thank you for your efforts to fix this issue.
#### Expected Output
Based on the color map, there should be a color for every cell in the pivot table, including the Margins. In addition, if the value is 0, it should be shown when the margins=True. It seems as if the treatment is inconsistent (i.e., view the total for Product C vs. the totals for 'Manf' and 'Retail' in Industry).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.88-1-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.2
seaborn : 0.9.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30352 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30352/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30352/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30352/events | https://github.com/pandas-dev/pandas/issues/30352 | 540,468,549 | MDU6SXNzdWU1NDA0Njg1NDk= | 30,352 | Too long error when accessing invalid DataFrame column | {
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"id": 2822098,
"name": "Indexing",
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"color": "00... | closed | false | null | [] | null | 3 | 2019-12-19T18:02:24Z | 2019-12-19T18:20:46Z | 2019-12-19T18:14:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame([1,2,3], columns=['A'])
>>> A['B'] # Accessing invalid colum B
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~\Miniconda3\envs\py37\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'B'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-25-c3fe2ff63dbf> in <module>
----> 1 df['B']
~\Miniconda3\envs\py37\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2993 if self.columns.nlevels > 1:
2994 return self._getitem_multilevel(key)
-> 2995 indexer = self.columns.get_loc(key)
2996 if is_integer(indexer):
2997 indexer = [indexer]
~\Miniconda3\envs\py37\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2897 return self._engine.get_loc(key)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2901 if indexer.ndim > 1 or indexer.size > 1:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'B'
```
#### Problem description
This error is extremely long and makes it hard to debug a very simple error when the code is part of a larger block of code.
#### Expected Output
```python
KeyError: 'DataFrame' object has no column 'B'
# And ideally, if `df.name` exists and in this case is set to "myframe:
KeyError: 'DataFrame' 'myframe' has no column 'B' # or something similar
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : 2.2.0
blosc : 1.8.1
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-12-19T18:10:33Z | 2020-05-09T19:23:30Z | 2020-05-09T19:23:30Z | NONE | null | #### Code Sample
```python
import pandas as pd
idx = pd.timedelta_range(start='8H', freq='10S', periods=5040)
s = pd.Series(range(len(idx)), index=idx)
s.resample('3H').min()
```
#### Observed Output
```
08:00:00 0.0
11:00:00 1080.0
14:00:00 2160.0
17:00:00 3240.0
20:00:00 4320.0
23:00:00 NaN
Freq: 3H, dtype: float64
```
#### Expected Output
```
08:00:00 0.0
11:00:00 1080.0
14:00:00 2160.0
17:00:00 3240.0
20:00:00 4320.0
Freq: 3H, dtype: float64
```
#### Problem description
Downsampling and then applying (any) function to the Resampler object seems to add an extra bin in some cases. It is unclear when this exactly occurs, but these two conditions seem necessary (but not sufficient) to reproduce the bug:
- the resampling period does not exactly fit in the original index
- the original index starts with a non-zero Timedelta
This guess is based on the fact that changing `'8H'` to `'0H'` in the code above returns an expected output. Equally so does changing the resampling period to `'2H'`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30354 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30354/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30354/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30354/events | https://github.com/pandas-dev/pandas/pull/30354 | 540,475,267 | MDExOlB1bGxSZXF1ZXN0MzU1Mjc4NjUx | 30,354 | TST: collect arithmetic test helpers | {
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} | 1 | 2019-12-19T18:17:42Z | 2019-12-23T20:15:32Z | 2019-12-23T19:30:20Z | MEMBER | null | broken off from a WIP branch that parametrizes/fixturizes/de-duplicates a bunch of timedelta64 arithmetic tests | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30355 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30355/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30355/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30355/events | https://github.com/pandas-dev/pandas/pull/30355 | 540,497,640 | MDExOlB1bGxSZXF1ZXN0MzU1Mjk4NDg1 | 30,355 | CLN: format replaced with f-strings #29547 | {
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] | closed | false | null | [] | null | 1 | 2019-12-19T18:53:12Z | 2019-12-19T21:37:33Z | 2019-12-19T21:37:19Z | CONTRIBUTOR | null | - [x] ref #29547
- [x] passes `black pandas`
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30356 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30356/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30356/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30356/events | https://github.com/pandas-dev/pandas/issues/30356 | 540,499,394 | MDU6SXNzdWU1NDA0OTkzOTQ= | 30,356 | #30214 (Parallelized Build / CI) caused a build failure for me | {
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} | 14 | 2019-12-19T18:56:53Z | 2020-01-01T01:34:36Z | 2020-01-01T01:34:36Z | CONTRIBUTOR | null | Currently I can't get pandas build.
I get the message when running ``python setup.py build_ext --inplace -j 4``:
```
Compiling pandas\_libs/parsers.pyx because it changed.
Compiling pandas\_libs/tslibs/timestamps.pyx because it changed.
Compiling pandas\_libs/window/aggregations.pyx because it changed.
Compiling pandas\io/msgpack/_packer.pyx because it changed.
Compiling pandas\io/msgpack/_unpacker.pyx because it changed.
Compiling pandas\_libs/groupby.pyx because it changed.
Compiling pandas\_libs/index.pyx because it changed.
Compiling pandas\_libs/internals.pyx because it changed.
Compiling pandas\_libs/lib.pyx because it changed.
Compiling pandas\_libs/parsers.pyx because it changed.
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 105, in spawn_main
Compiling pandas\_libs/tslibs/timestamps.pyx because it changed.
Compiling pandas\_libs/window/aggregations.pyx because it changed.
Compiling pandas\io/msgpack/_packer.pyx because it changed.
Compiling pandas\io/msgpack/_unpacker.pyx because it changed.
Compiling pandas\_libs/groupby.pyx because it changed.
Compiling pandas\_libs/index.pyx because it changed.
Compiling pandas\_libs/internals.pyx because it changed.
Compiling pandas\_libs/lib.pyx because it changed.
Compiling pandas\_libs/parsers.pyx because it changed.
exitcode = _main(fd)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 263, in run_path
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 263, in run_path
Compiling pandas\_libs/tslibs/timestamps.pyx because it changed.
Compiling pandas\_libs/window/aggregations.pyx because it changed.
Compiling pandas\io/msgpack/_packer.pyx because it changed.
Compiling pandas\io/msgpack/_unpacker.pyx because it changed.
pkg_name=pkg_name, script_name=fname)
pkg_name=pkg_name, script_name=fname)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 85, in _run_code
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 96, in _run_module_code
exec(code, run_globals)
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 815, in <module>
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 105, in spawn_main
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 815, in <module>
exitcode = _main(fd)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 114, in _main
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 543, in maybe_cythonize
prepare(preparation_data)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 263, in run_path
return cythonize(extensions, *args, **kwargs)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\site-packages\Cython\Build\Dependencies.py", line 1073, in cythonize
pkg_name=pkg_name, script_name=fname)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 96, in _run_module_code
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 543, in maybe_cythonize
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 85, in _run_code
return cythonize(extensions, *args, **kwargs)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\site-packages\Cython\Build\Dependencies.py", line 1073, in cythonize
exec(code, run_globals)
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 815, in <module>
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
nthreads, initializer=_init_multiprocessing_helper)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 119, in Pool
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 543, in maybe_cythonize
nthreads, initializer=_init_multiprocessing_helper)
return cythonize(extensions, *args, **kwargs)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\site-packages\Cython\Build\Dependencies.py", line 1073, in cythonize
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 119, in Pool
nthreads, initializer=_init_multiprocessing_helper)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 119, in Pool
Compiling pandas\_libs/groupby.pyx because it changed.
Compiling pandas\_libs/index.pyx because it changed.
context=self.get_context())
context=self.get_context())
context=self.get_context())
Compiling pandas\_libs/internals.pyx because it changed.
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 176, in __init__
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 176, in __init__
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 176, in __init__
Compiling pandas\_libs/lib.pyx because it changed.
Compiling pandas\_libs/parsers.pyx because it changed.
Compiling pandas\_libs/tslibs/timestamps.pyx because it changed.
Compiling pandas\_libs/window/aggregations.pyx because it changed.
Compiling pandas\io/msgpack/_packer.pyx because it changed.
Compiling pandas\io/msgpack/_unpacker.pyx because it changed.
self._repopulate_pool()
self._repopulate_pool()
self._repopulate_pool()
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
Traceback (most recent call last):
w.start()
w.start()
w.start()
File "<string>", line 1, in <module>
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\process.py", line 112, in start
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\process.py", line 112, in start
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\process.py", line 112, in start
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 815, in <module>
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "C:\Users\TP\Documents\Python\pandasdev\pandasdev\setup.py", line 543, in maybe_cythonize
return cythonize(extensions, *args, **kwargs)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\site-packages\Cython\Build\Dependencies.py", line 1073, in cythonize
nthreads, initializer=_init_multiprocessing_helper)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 119, in Pool
context=self.get_context())
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 176, in __init__
self._repopulate_pool()
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 241, in _repopulate_pool
w.start()
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
self._popen = self._Popen(self)
self._popen = self._Popen(self)
self._popen = self._Popen(self)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 322, in _Popen
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 322, in _Popen
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 322, in _Popen
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
return Popen(process_obj)
return Popen(process_obj)
return Popen(process_obj)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
prep_data = spawn.get_preparation_data(process_obj._name)
prep_data = spawn.get_preparation_data(process_obj._name)
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
_check_not_importing_main()
_check_not_importing_main()
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
_check_not_importing_main()
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable. is not going to be frozen to produce an executable.''')
File "C:\Users\TP\Miniconda3\envs\pandas-dev\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable. is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
```
I can't really parse what's going on, but the build seems to go back to the same RuntimeError repeatedly.
I've triaged the issue to stem from #30214. Any idea what's happening, @WillAyd ?
## Workaround
I can work around the issue by building with parallelization, i.e. run ``python setup.py build_ext --inplace -j 0`` instead of ``python setup.py build_ext --inplace -j 4``.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 95e1a63dd3382db6663bc8a2b334b422b93dd7fe
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.26.0.dev0+1358.g95e1a63dd
numpy : 1.17.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.13
pytest : 5.2.2
hypothesis : 4.28.2
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.2
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30357 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30357/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30357/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30357/events | https://github.com/pandas-dev/pandas/issues/30357 | 540,508,246 | MDU6SXNzdWU1NDA1MDgyNDY= | 30,357 | Pandas assign malfunctions with if/else conditional check | {
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#### Problem description
I need to create a new column within a pandas daframe from the values of another column. For instance, provided the city column in the following dataframe , the new column will duplicate the value from the city column only if it is within a list , otherwise the correspnding netries woill be populated as "other". The following code snippet works like a charm.
```python
df = pd.DataFrame({'city': ['Kolkata','Delhi','Mumbai','Bankura','Dhaka','Jaipur','Goa',
'Delhi','Mumbai','Kolkata'],'temp':[i for i in range(10)]})
df['MajorCity'] = df.apply(lambda x: x.city if x.city in ['Kolkata','Delhi','Mumbai'] else 'other',axis=1)
print(df)
```
#### Output
city | temp | MajorCity
-- | -- | --
Kolkata | 0 | Kolkata
Delhi | 1 | Delhi
Mumbai | 2 | Mumbai
Bankura | 3 | other
Dhaka | 4 | other
Jaipur | 5 | other
Goa | 6 | other
Delhi | 7 | Delhi
Mumbai | 8 | Mumbai
Kolkata | 9 | Kolkata
But when I try to implement it with pandas assign function as below
```python
# Your code here
df = pd.DataFrame({'city': ['Kolkata','Delhi','Mumbai','Bankura','Dhaka','Jaipur','Goa',
'Delhi','Mumbai','Kolkata'],'temp':[i for i in range(10)]})
df.assign(MajorCity = lambda x:x.city if x.city in ['Kolkata','Delhi','Mumbai'] else 'other')
```
I received the following error:
### Error message:
ValueError Traceback (most recent call last)
<ipython-input-122-81155431f14d> in <module>
1 df = pd.DataFrame({'city': ['Kolkata','Delhi','Mumbai','Bankura','Dhaka','Jaipur','Goa',
2 'Delhi','Mumbai','Kolkata'],'temp':[i for i in range(10)]})
----> 3 df.assign(MajorCity = lambda x:x.city if x.city in ['Kolkata','Delhi','Mumbai'] else 'other')
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in assign(self, **kwargs)
3667 if PY36:
3668 for k, v in kwargs.items():
-> 3669 data[k] = com.apply_if_callable(v, data)
3670 else:
3671 # <= 3.5: do all calculations first...
~/anaconda3/lib/python3.7/site-packages/pandas/core/common.py in apply_if_callable(maybe_callable, obj, **kwargs)
363
364 if callable(maybe_callable):
--> 365 return maybe_callable(obj, **kwargs)
366
367 return maybe_callable
<ipython-input-122-81155431f14d> in <lambda>(x)
1 df = pd.DataFrame({'city': ['Kolkata','Delhi','Mumbai','Bankura','Dhaka','Jaipur','Goa',
2 'Delhi','Mumbai','Kolkata'],'temp':[i for i in range(10)]})
----> 3 df.assign(MajorCity = lambda x:x.city if x.city in ['Kolkata','Delhi','Mumbai'] else 'other')
~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in __nonzero__(self)
1553 "The truth value of a {0} is ambiguous. "
1554 "Use a.empty, a.bool(), a.item(), a.any() or a.all().".format(
-> 1555 self.__class__.__name__
1556 )
1557 )
##### ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 4.6.2
hypothesis : None
sphinx : 2.1.0
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 1 | 2019-12-19T20:06:59Z | 2019-12-20T16:17:15Z | 2019-12-20T15:33:54Z | MEMBER | null | The removal of `ix` highlighted how difficult it is to tell how thorough tests are for some funcs/methods. This is one of several PRs intended to clarify where the tests for X live, and ideally to make that follow a standard pattern across DataFrame/Series/Index subclasses (and EA where relevant) | {
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} | 7 | 2019-12-19T20:39:43Z | 2020-08-14T01:18:40Z | 2020-08-14T01:18:39Z | NONE | null | #### Code Sample
```python
pd.read_csv('s3://other_owner_bucket_name/data', file_system=s3fs.FileSystem(key='abc', secret='123'))
```
Or
```python
pd.read_csv('s3://other_owner_bucket_name/data', storage_options={'key':'abc', 'secret':'123'})
```
#### Problem description
Hi Team, I am currently using pandas for query a lot of CSV data from different file system such as AWS, GCP. However, with a current option, it just allows default key/secret credential from configuration file **[default]**. So I would like to ask for an option to pass a pre-created file system to read CSV files from different clouds with different credentials.
Best Regards
Giang | {
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] | closed | false | null | [] | null | 2 | 2019-12-19T20:53:51Z | 2020-09-05T19:03:49Z | 2020-09-05T19:03:49Z | MEMBER | null | These files are big enough that GH has trouble displaying a blame. I think we should consider splitting pieces of these off into other files. Brainstorming:
- `_make_logical_function`, `_make_cum_function`, `_make_stat_function_ddof`, `_make_stat_function`, `_make_min_count_stat_function` at the bottom of generic could have their own file, maybe `ops.unary`
- shared_docs could go in a dedicated file (this might also avoid some circular import issues IIRC)
- IO pass-through methods could go into mixin classes (xref #30350 motivating this idea)
- to_json, to_hdf, to_sql, to_pickle, to_clipboard, to_xarray, to_latex, to_csv make up ~1200 lines of generic
- to_gbq, to_stata, to_feather, to_parquet, to_html make up ~450 lines of frame.py
Thoughts? | {
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prefix_of_filename_1.csv
prefix_of_filename_2.csv
```python
pd.read_csv('file://folder_name/prefix_of_filename*.csv')
df.to_csv('file://folder_name/file_name*.csv', name_function=name, chunksize='64mb')
```
#### Problem description
Hi Team,
I would like to request a feature to read and write multiple files at a same time. It would be nice to have features like that for preparing data for concurrent data reading system.
Kind Regards
Giang | {
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} | 1 | 2019-12-19T21:37:30Z | 2019-12-20T16:18:07Z | 2019-12-20T15:33:02Z | MEMBER | null | Per the docstring in each of the new directories:
```
Test files dedicated to individual (stand-alone) DataFrame methods
Ideally these files/tests should correspond 1-to-1 with tests.series.methods
These may also present opportunities for sharing/de-duplicating test code.
```
If this approach is approved, I'll then go through the remaining test files (in particular test_analytics) for things that have become "misc" piles.
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} | 1 | 2019-12-19T21:43:26Z | 2019-12-20T07:28:44Z | 2019-12-20T02:28:05Z | CONTRIBUTOR | null | - [x] ref #29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-12-19T22:04:49Z | 2019-12-20T12:25:05Z | 2019-12-20T12:25:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
groups = np.array(["A", "B"])
years = np.arange(2015, 2018)
values = np.arange(1,3)
table = np.array([years.repeat(len(groups)), np.tile(groups, len(years)), np.tile(values, len(years))]).T
table = pd.DataFrame(table, columns = ["year", "group", "val"])
table["year"] = table["year"].astype(int)
table["val"] = table["val"].astype(int)
table["group"] = table["group"].astype(str)
grp = table.groupby("year")
grp.rank()
```
#### Problem description
Running the rank method on the grouped table throws `TypeError: 'NoneType' object is not callable`
#### Expected Output
It should return ranked data for each year, or a descriptive error message.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-70-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30365 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30365/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30365/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30365/events | https://github.com/pandas-dev/pandas/issues/30365 | 540,600,801 | MDU6SXNzdWU1NDA2MDA4MDE= | 30,365 | IntervalIndex Comparisons of Signed / Unsigned | {
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} | 1 | 2019-12-19T22:07:08Z | 2020-01-01T02:45:56Z | 2020-01-01T02:45:56Z | MEMBER | null | Compiling intervaltree.pxi.in yields 128 warnings in the build, most of which deal with the comparison of signed and unsigned data.
These warnings add to the compile time, but also for sure highlight bugs like this:
```python
# Create an IntervalIndex using values only in the range of uint64
>>> ser = pd.Series([1], index=pd.IntervalIndex.from_tuples([((2 ** 63), (2 ** 64)-1)]))
# Negative value isn't in those ranges, but overflow yields undefined behavior
>>> ser[[-20]]
(9223372036854775808, 18446744073709551615] 1
dtype: int64
```
I think should probably separate unsigned vs signed parametrization | {
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] | closed | false | null | [] | null | 5 | 2019-12-19T22:17:03Z | 2020-01-01T23:58:07Z | 2020-01-01T23:58:03Z | MEMBER | null | closes #30365 , #27169
Unclear if we currently consider this a feature, but I couldn't find anything explicitly asking for it so trying the removal route first
Not sure what the other alternative would be for parametrization, but I think generally we need to be careful when doing comparisons between signed and unsigned in templating or fused types
Side benefit - this gets rid of 128 build warnings and a slight perf boost to compilation
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} | 5 | 2019-12-19T22:24:48Z | 2020-01-14T08:25:52Z | 2020-01-14T08:25:46Z | NONE | null | #### Code Sample
This generates a series of ints for dates, which when trying to be imported elsewhere fails
```python
# Fails
dbSlice.to_parquet(outFile, index= False)
```
Instead, [consider something like this](https://medium.com/@andyreagan/tricks-for-coercing-pandas-into-parquet-d1137421db88) to run as a preprocessor for dates before saving as parquet as the default (or at least a flag):
```python
# Works
dbSlice['date'] = dbSlice.loc[~dbSlice.date.isnull(),'date'].apply(lambda x: x.isoformat())
dbSlice.to_parquet(outFile, index= False)
```
#### Problem description
The whole purpose of exporting the dataframe is data portability. Mangling data types is a big no-no, especially if there's no obvious way for other programs to read it. Exporting dates as an ISO string is easily interpretable by other software.
My particular use case wasn't even strange, just importing the parquet into Spark.
#### Expected Output
Any output interpretable by other software
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.17.3
pytz : 2018.4
dateutil : 2.7.2
pip : 19.3
setuptools : 41.0.1
Cython : 0.27.3
pytest : 4.3.1
hypothesis : None
sphinx : 1.6.6
blosc : None
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.1.1
html5lib : 0.9999999
pymysql : 0.8.0
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : 0.3.1
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 2.2.3
numexpr : 2.6.4
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.2.1
tables : 3.4.2
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
</details>
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} | 5 | 2019-12-20T01:47:44Z | 2020-01-16T00:33:26Z | 2020-01-02T01:06:23Z | MEMBER | null | I think just easier to ensure The GIL is acquired within the C function rather than all of the current casting / indirection.
Eliminates the following warning:
```sh
warning: pandas/_libs/parsers.pyx:1656:34: Casting a GIL-requiring function into a nogil function circumvents GIL validation
``` | {
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} | 1 | 2019-12-20T05:01:35Z | 2019-12-20T16:23:03Z | 2019-12-20T12:27:14Z | MEMBER | null | The usual goal: make cython code depend on non-cython code less
The new goal: limit the places where we import from `pd.io.common` so I can track down the many encoding/path/compression... inconsistencies. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30374 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30374/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30374/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30374/events | https://github.com/pandas-dev/pandas/pull/30374 | 540,772,032 | MDExOlB1bGxSZXF1ZXN0MzU1NTM4NTcy | 30,374 | BUG: passing non-printable unicode to datetime parsing functions | {
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} | 12 | 2019-12-20T05:33:14Z | 2020-04-05T17:34:53Z | 2019-12-21T20:10:22Z | MEMBER | null | ATM these segfault (on OSX at least). There are more of these that I'll get in upcoming passes (I think no more than 2 more will be needed), keeping this one small to make the issue obvious and improve some of the typing in functions that will be checked in the next passes.
Needs double-checking: is the `val.isprintable()` check equivalent to "`PyUnicode_AsUTF8AndSize(val, length)` wont segfault"? or is that just an approximation? | {
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} | 2 | 2019-12-20T10:08:42Z | 2019-12-20T16:02:05Z | 2019-12-20T16:02:05Z | MEMBER | null | Admittely this is very minor, but
there's a few parts in the codebase (e.g. `pandas/tests/io/test_common.py`) where there is
```
@td.skip_if_no("pathlib")
```
As of `v1.0`, the minimum Python version supported will be 3.6.1, so `pathlib` will always be present as part of the standard library. | {
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} | 2 | 2019-12-20T12:54:07Z | 2019-12-20T16:01:53Z | 2019-12-20T14:59:06Z | MEMBER | null | - [ ] closes #30375
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-12-20T14:02:17Z | 2019-12-26T13:40:53Z | 2019-12-26T13:35:10Z | CONTRIBUTOR | null | - [x] closes #8594
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
description:
In core.base
```
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != "ignore":
raise KeyError(f"{labels[mask]} not found in axis")
```
single index check and raise if there is no matching indexer. But 'MultiIndex' case,
In indexes.multi
```
codes = com.index_labels_to_array(codes)
i = self._get_level_number(level)
index = self.levels[i]
values = index.get_indexer(codes)
mask = ~algos.isin(self.codes[i], values)
return self[mask]
```
no check. So i add check logic and raise exception if there is no matching indexer.
This bug cause from 'MultiIndex', so do not distinguish between "Series" and "DataFrame"
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https://api.github.com/repos/pandas-dev/pandas/issues/30378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30378/events | https://github.com/pandas-dev/pandas/issues/30378 | 541,020,929 | MDU6SXNzdWU1NDEwMjA5Mjk= | 30,378 | Pandas date_range error when the date falls in a DST starting day. | {
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... | open | false | null | [] | null | 5 | 2019-12-20T14:24:56Z | 2021-07-25T03:01:49Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
pd.date_range(start=pd.Timestamp('2016-10-05 00:00:00'), end=pd.Timestamp('2016-10-30 00:00:00'), freq='1D', tz='America/Sao_Paulo')
```
#### Problem description
[When I try to generate a range of daily data with America/Sao_Paulo timezone, it breaks with NonExistentTimeError: 2016-10-16 00:00:00. My pandas version is 0.25.3]
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https://api.github.com/repos/pandas-dev/pandas/issues/30379 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30379/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30379/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30379/events | https://github.com/pandas-dev/pandas/pull/30379 | 541,066,080 | MDExOlB1bGxSZXF1ZXN0MzU1Nzg5Njg0 | 30,379 | Remove unnecessary `@td.skip_if_no("pathlib")` | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30382/events | https://github.com/pandas-dev/pandas/pull/30382 | 541,221,074 | MDExOlB1bGxSZXF1ZXN0MzU1OTIxMDQz | 30,382 | CLN: move code out of a try clause in merge.py | {
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] | closed | false | null | [] | null | 0 | 2019-12-20T22:46:31Z | 2019-12-24T21:15:52Z | 2019-12-24T21:15:52Z | MEMBER | null | I'm seeing in test some things like
```def _skip_if_no_pchip():
try:
from scipy.interpolate import pchip_interpolate # noqa
except ImportError:
import pytest
pytest.skip("scipy.interpolate.pchip missing")
def _skip_if_no_akima():
try:
from scipy.interpolate import Akima1DInterpolator # noqa
except ImportError:
import pytest
pytest.skip("scipy.interpolate.Akima1DInterpolator missing")
```
AFAICT pchip_interpolate goes back to at least scipy 0.13.0, and we require 0.19.0, so this check should be unnecessary (or more accurately, replaced with a skip_if_no_scipy). Not sure about akima, but we should track these down and put them all into test_decorators or compat or something | {
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} | 1 | 2019-12-20T23:10:46Z | 2019-12-23T15:37:44Z | 2019-12-23T15:13:08Z | MEMBER | null | In parallel with, and orthogonal to, #30381.
Identified a couple of tests that were in the wrong places, e.g. the one moved int test_cumulative. | {
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} | 5 | 2019-12-20T23:25:25Z | 2019-12-30T23:28:09Z | 2019-12-27T16:52:54Z | MEMBER | null | - [x] closes #30296
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Hopefully this can help cython kick the tires. | {
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} | 0 | 2019-12-21T08:57:40Z | 2019-12-22T09:56:31Z | 2019-12-22T09:56:28Z | MEMBER | null | Fixes the wrong external links (see https://github.com/pandas-dev/pydata-bootstrap-sphinx-theme/issues/66), and also fixes the favicon | {
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} | 3 | 2019-12-21T14:55:53Z | 2019-12-21T22:25:38Z | 2019-12-21T20:11:01Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-12-21T18:19:58Z | 2020-01-01T03:39:08Z | 2020-01-01T03:39:08Z | NONE | null | I noticed while helping a student that there's a bug when calling a scatter plot as a method on a DataFrame. Regardless of what the index is or what the x argument is, it appears that pandas makes the x argument into the index, and then searches for the x in the columns, where it no longer exists. When calling the same directly through matplotlib as plt.scatter, it worked properly.
<img width="1074" alt="Pandas_Error_scatter" src="https://user-images.githubusercontent.com/35703276/71311989-e7ba6900-23f3-11ea-9911-ebf4e2b1c463.png">
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} | 0 | 2019-12-21T21:02:25Z | 2019-12-30T12:27:31Z | 2019-12-25T22:28:19Z | MEMBER | null | There is a little more id like to do in environment.yml to put docbuild things in the same place, but holding off to keep the diff tidier. Also it appears there are some unused things in environment.yml that we can look into removing, separate PR | {
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} | 7 | 2019-12-21T21:26:50Z | 2019-12-24T01:17:51Z | 2019-12-23T08:49:27Z | MEMBER | null | xref #26344
If this works, i think the remaining places to bashify are
- identical powerhsell commands in windows.yml and posix.yml to check for test failures
- conda.recipe/bld.bat (do we need this directory at all?)
- test.bat, test_fast.bat (do we need the bash versions of these?) | {
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Non-segfault-related pieces broken off from #30374. Fix a (not-reachable-but-thats-another-conversation) tzinfo check closing #22234.
De-duplicates get_rule_month in tslibs.frequencies and tslibs.parsing
pythonizes a PyUnicode_Join usage because cython does that optimization already. | {
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} | 2 | 2019-12-22T10:53:51Z | 2019-12-28T17:29:30Z | 2019-12-27T16:02:51Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30399/events | https://github.com/pandas-dev/pandas/issues/30399 | 541,437,893 | MDU6SXNzdWU1NDE0Mzc4OTM= | 30,399 | BUG/ERR: wrong error in DataFrame.drop with non-unique datetime index + invalid keys | {
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} | 1 | 2019-12-22T11:15:19Z | 2019-12-26T00:32:30Z | 2019-12-26T00:32:30Z | MEMBER | null | Consider this example, where there is a DataFrame with a non-unique DatetimeIndex:
```
In [8]: df = pd.DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'], index=pd.date_range("2012", freq='H', periods=5))
In [9]: df = df.iloc[[0, 2, 2, 3]]
In [10]: df
Out[10]:
a b c
2012-01-01 00:00:00 -1.534726 -0.559295 0.207194
2012-01-01 02:00:00 -1.072027 0.376595 0.407512
2012-01-01 02:00:00 -1.072027 0.376595 0.407512
2012-01-01 03:00:00 0.581614 1.782635 -0.678197
```
If you then use `drop` to drop some columns, but forget to specify `columns=` or `axis=1` (so you are actually dropping rows), you get a wrong error and very confusing error message:
```
In [10]: df.drop(['a', 'b'])
...
~/scipy/pandas/pandas/core/indexes/base.py in get_indexer_non_unique(self, target)
4559 tgt_values = target._ndarray_values
4560
-> 4561 indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
4562 return ensure_platform_int(indexer), missing
4563
~/scipy/pandas/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_indexer_non_unique()
TypeError: 'NoneType' object is not iterable
```
Tested with pandas 0.25 and pandas master. | {
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} | 3 | 2019-12-22T11:56:16Z | 2020-01-02T01:44:15Z | 2020-01-02T01:41:11Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] passes `black pandas`
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} | 1 | 2019-12-22T12:14:23Z | 2019-12-27T16:12:27Z | 2019-12-27T16:12:23Z | MEMBER | null | - [x] xref #30114
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is the first step for closing the PR #30114, I will come up with another PR for `drop_duplicates` | {
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- [ ] tests added / passed
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"id": 7171206... | open | false | null | [] | null | 5 | 2019-12-22T20:01:24Z | 2021-03-25T12:50:18Z | null | MEMBER | null | I've been having a look at the pages on the GitHub wiki, and I think the content is mostly outdated, not very useful, and in some cases opinionated and not specific to pandas.
Not sure if there is anything worth keeping. But if there is, its not much, and I think it should be moved to the docs, so it's more visible, and we can open PRs on it, to update or delete the content.
Once the content we want to keep is in the docs, I'd diable the wiki. Personally I don't see any use case for it in our l project. The docs can contain everything.
Any objection? Anything that someone wants to keep? | {
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} | 1 | 2019-12-22T21:44:46Z | 2019-12-24T16:42:21Z | 2019-12-24T16:40:04Z | MEMBER | null | - [ ] closes #xxxx
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([#29886](https://github.com/pandas-dev/pandas/issues/29886)). | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/16"
} | 4 | 2019-12-22T22:05:35Z | 2019-12-24T16:46:20Z | 2019-12-24T13:44:47Z | MEMBER | null | Having two layers of templating here is really weird, this unravels one layer so this file is organized like our other tempita files. That makes future moves towards fused types and/or conditional nogil feasible. | {
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