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} | 28 | 2019-09-12T16:00:54Z | 2019-09-20T12:04:31Z | 2019-09-20T12:04:13Z | CONTRIBUTOR | null | This tries to codify the discussion about versioning from yesterday. I tried to summarize the group's thoughts. Let me know if any of my biases slipped through.
cc @pandas-dev/pandas-core
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} | 2 | 2019-09-12T16:51:32Z | 2019-09-18T17:16:24Z | 2019-09-18T13:42:37Z | CONTRIBUTOR | null | - [x] closes #27292
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Infers names in `MultiIndex.from_product` and normalizes handing of `names=None` between `from_arrays` and `from_product` | {
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} | 6 | 2019-09-12T16:53:15Z | 2019-09-14T00:40:11Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# The first case shows that dicts cause the index argument to be more of a WHERE selector for rows...
>>> pd.Series({"a": 1, "b": 2, "c": 3}, index=["a", "b"])
a 1
b 2
dtype: int64
# But the docs + using a list have an error thrown if the index size does not match the length of the data.
>>> pd.Series(["a", "b", "c"], index=[0, 1])
# Error...
ValueError: Length of passed values is 3, index implies 2
```
#### Problem description
The current behavior with dicts is inconsistent with the documentation. Moreover, there should be consistent behavior between passing a dict and passing an array into the series.
#### Expected Output
#### 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 : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.2.0
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.8
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28419 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28419/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28419/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28419/events | https://github.com/pandas-dev/pandas/pull/28419 | 492,926,632 | MDExOlB1bGxSZXF1ZXN0MzE3MDA0Njcx | 28,419 | BUG: inconsistency when passing dict to Series with index | {
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] | closed | false | null | [] | null | 3 | 2019-09-12T17:07:19Z | 2019-09-12T19:19:51Z | 2019-09-12T19:19:45Z | NONE | null | * Adds ValueError if passing a dict into pd.Series with a different size than the passed index parameter. Closes #28418
- [x] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
(Will add whatsnew / test if this solution is acceptable.)
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} | 0 | 2019-09-12T17:28:29Z | 2019-09-12T21:07:02Z | 2019-09-12T19:42:20Z | MEMBER | null | There's a couple more in this file that aren't hit in tests but that I'm not yet ready to remove. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28421/events | https://github.com/pandas-dev/pandas/issues/28421 | 492,963,428 | MDU6SXNzdWU0OTI5NjM0Mjg= | 28,421 | cont | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28422/events | https://github.com/pandas-dev/pandas/issues/28422 | 492,977,737 | MDU6SXNzdWU0OTI5Nzc3Mzc= | 28,422 | test_cross_engine_pa_fp fails locally. | {
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"d... | closed | false | null | [] | null | 12 | 2019-09-12T19:05:41Z | 2020-04-23T23:31:18Z | 2020-04-23T23:31:18Z | MEMBER | null | ```
___________________________ test_cross_engine_pa_fp ___________________________
df_cross_compat = a b d e f
0 a 1 4.0 True 2013-01-01
1 b 2 5.0 False 2013-01-02
2 c 3 6.0 True 2013-01-03
pa = 'pyarrow', fp = 'fastparquet'
def test_cross_engine_pa_fp(df_cross_compat, pa, fp):
# cross-compat with differing reading/writing engines
df = df_cross_compat
with tm.ensure_clean() as path:
df.to_parquet(path, engine=pa, compression=None)
> result = read_parquet(path, engine=fp)
pandas\tests\io\test_parquet.py:232:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas\io\parquet.py:293: in read_parquet
return impl.read(path, columns=columns, **kwargs)
pandas\io\parquet.py:198: in read
return parquet_file.to_pandas(columns=columns, **kwargs)
..\..\..\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\api.py:421: in to_pandas
check_column_names(self.columns + list(self.cats), columns, categories)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
columns = ['a', 'b', 'd', 'e', 'f']
args = (['a', 'b', 'd', 'e', 'f', {'kind': 'range', 'name': None, 'start': 0, 'step': 1, ...}], None)
arg = ['a', 'b', 'd', 'e', 'f', {'kind': 'range', 'name': None, 'start': 0, 'step': 1, ...}]
def check_column_names(columns, *args):
"""Ensure that parameters listing column names have corresponding columns"""
for arg in args:
if isinstance(arg, (tuple, list)):
> if set(arg) - set(columns):
E TypeError: unhashable type: 'dict'
..\..\..\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\util.py:92: TypeError
```
<details>
INSTALLED VERSIONS
------------------
commit : 0ab32e88481440bfb4a102bb7731cbde2e5ceafe
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.25.0+339.g0ab32e884
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.2.1
scipy : 1.3.1
sqlalchemy : 1.3.7
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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{... | closed | false | null | [] | null | 16 | 2019-09-13T01:41:20Z | 2019-09-18T01:42:07Z | 2019-09-18T01:42:07Z | MEMBER | null | It uses a boolean mask with 8 bits under the hood, so it wouldn't be too tough to implement. Doing this would make it easier to keep IntegerNA arithmetic in sync with Series arithmetic. | {
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} | 7 | 2019-09-13T02:09:27Z | 2020-11-21T22:21:46Z | 2020-11-21T22:21:46Z | NONE | null | ```python
from pandas.api.extensions import ExtensionDtype,ExtensionArray, ExtensionScalarOpsMixin
class FP:
def __add__(self, other):
return self + other
pass
@pd.api.extensions.register_extension_dtype
class FPType(ExtensionDtype):
name = 'fp'
type = FP
@classmethod
def construct_from_string(cls, string):
if string == cls.name:
return cls()
else:
raise TypeError("Cannot construct a '{}' from "
"'{}'".format(cls, string))
# collections.abc.Sequence -> data is sequence, _reduce is possible
class FPArray(ExtensionArray,ExtensionScalarOpsMixin,collections.abc.Sequence):
_dtype = FPType()
@property
def dtype(self):
return self._dtype
def __init__(self, values):
values = np.array(values) # TODO: avoid potential copy
self.data = values
def __len__(self):
return len(self.data)
def __getitem__(self, *args):
result = operator.getitem(self.data, *args)
return result
def take(self,indexer):
return self.data.take(indexer)
def __contains__(self,item):
return item in self.data
def __iter__(self):
for i in self.data:
yield i
def __reversed__(self):
return reversed(self.data)
def _reduce(self, name, skipna=True, **kwargs):
return sum(self.data+100)
```
The ExtensionArray array itself is just for illustration and is not the problem, the problematic part is
```python
arr = FPArray([FP(),FP(),FP(),FP()])
df = pd.DataFrame(arr )
```
when it reached line 444 of pandas/core/frame.py (https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L444)
```python
if not isinstance(data, abc.Sequence):
data = list(data)
```
where `data` is referring to my FPArray, if data is not abc.Sequence, my ExtensionArray will be casted to a list.
Having my ExtensionArray casted to a list mean my ExtensionArray will eventually casted back to a normal Pandas series.
The Solution should be having `ExtensionArray` to be a subclass of `abc.Sequence`.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 18 | 2019-09-13T03:22:18Z | 2019-09-18T14:06:26Z | 2019-09-18T14:02:18Z | CONTRIBUTOR | null | As discussed on the dev call.
Closes https://github.com/pandas-dev/pandas/issues/28407
Closes https://github.com/pandas-dev/pandas/issues/28025
Closes https://github.com/pandas-dev/pandas/issues/26777
Closes https://github.com/pandas-dev/pandas/issues/26227
~Not https://github.com/pandas-dev/pandas/issues/26123~
Closes https://github.com/pandas-dev/pandas/issues/25694
Closes https://github.com/pandas-dev/pandas/issues/25378
~Not https://github.com/pandas-dev/pandas/issues/25270~
~Not https://github.com/pandas-dev/pandas/issues/24817~
Closes https://github.com/pandas-dev/pandas/issues/24270
Closes https://github.com/pandas-dev/pandas/issues/22630
Closes https://github.com/pandas-dev/pandas/issues/21993
Closes https://github.com/pandas-dev/pandas/issues/21909
~Not https://github.com/pandas-dev/pandas/issues/21818~
Closes https://github.com/pandas-dev/pandas/issues/21231
Closes https://github.com/pandas-dev/pandas/issues/20766
Closes https://github.com/pandas-dev/pandas/issues/20172
~Not https://github.com/pandas-dev/pandas/issues/19278~
Closes https://github.com/pandas-dev/pandas/issues/18969
~Not https://github.com/pandas-dev/pandas/issues/18414~
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Closes https://github.com/pandas-dev/pandas/issues/5078
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} | 2 | 2019-09-13T14:42:33Z | 2019-12-08T18:01:22Z | 2019-12-08T18:01:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from functools import partial
quant50 = partial(np.percentile, q=50)
quant70 = partial(np.percentile, q=70)
test = pd.DataFrame({'col1': ['a', 'a', 'b', 'b', 'b'], 'col2': [1,2,3,4,5]})
test.groupby('col1').agg(
quantile_50=('col2', quant50),
quantile_70=('col2', quant70)
)
```
#### Problem description
I'm trying to calculate multiple percentiles, I get the following error: "Function names must be unique, found multiple named percentile"
It's an old problem https://github.com/pandas-dev/pandas/issues/7186 but I assumed that it should've been solved with the named aggregations. Now there is no need to deduce the column name from the function name, the column name was specified explicitly.
#### Expected Output
column | quantile_50 | quantile_70
a | 1.5 | 2
b | 4.0 | 3
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.1.11-200.fc29.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2017.3
dateutil : 2.7.2
pip : 19.1.1
setuptools : 39.1.0
Cython : 0.28.1
pytest : 3.3.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.3.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 2.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.9.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.1.18
tables : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28427 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28427/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28427/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28427/events | https://github.com/pandas-dev/pandas/issues/28427 | 493,385,588 | MDU6SXNzdWU0OTMzODU1ODg= | 28,427 | Resampling and counting empty Series does not have a correct dtype | {
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} | 3 | 2019-09-13T15:24:33Z | 2019-11-19T13:42:59Z | 2019-11-19T13:42:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Short examples:
```python
import pandas as pd
a = pd.Series([], dtype="datetime64[ns]", index=pd.DatetimeIndex([]))
print(a.resample("D").size().dtype) # => actual datetime64[ns], expected int64
```
Longer example:
```python
import pandas as pd
t = pd.to_datetime(0)
x = pd.Series([t], index=[t])
print(x.resample("D").size().dtype) # => int64 (OK)
y = x.iloc[0:1]
print(y.resample("D").size().dtype) # => int64 (OK)
z = x.iloc[0:0]
print(z.resample("D").size().dtype) # => actual datetime64[ns], expected int64
```
#### Problem description
When `.resample().size()` an empty dataframe, the resulting dtype is the dtype of the initial Series. It should be an integer.
This means that this kind of things fail if the input `Series` happens to be empty:
```python
monthy_rate = (x.resampe("D").size() > 0).resample("M").mean()
```
#### Expected Output
<pre>
int64
int64
int64
</pre>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.0-2-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.utf8
LOCALE : fr_FR.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
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/28428 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28428/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28428/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28428/events | https://github.com/pandas-dev/pandas/pull/28428 | 493,401,792 | MDExOlB1bGxSZXF1ZXN0MzE3Mzg4NDk1 | 28,428 | BUG: Remove error raise to allow same input function on the same column in named aggregation | {
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} | 13 | 2019-09-13T15:57:42Z | 2019-12-08T18:01:25Z | 2019-12-08T18:01:22Z | MEMBER | null | - [x] closes #28426
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
https://github.com/pandas-dev/pandas/pull/27921#issuecomment-521794197
this was kind of mentioned in the PR27921, and the case used in the issue is basically the same as having `min` or `max`. Previously it was not raising error is because I deliberately skipped this case since i thought this removal should be treated in different PR. Feel free to review and comment your ideas.
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https://api.github.com/repos/pandas-dev/pandas/issues/28429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28429/events | https://github.com/pandas-dev/pandas/pull/28429 | 493,404,306 | MDExOlB1bGxSZXF1ZXN0MzE3MzkwNTY1 | 28,429 | BUG: close hdf store on any error | {
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"color": "5319... | closed | false | null | [] | null | 17 | 2019-09-13T16:03:15Z | 2020-02-02T01:03:53Z | 2020-02-02T01:03:53Z | NONE | null | NOTE: I'm opening the PR mainly to open a discussion about the current behaviour. If this is intended, please tell me. If not and you need me to write some tests, I'll do it :)
In `read_hdf`, if the `store.select()` call throws either a
`ValueError`, a `TypeError` or a `KeyError` then the store is closed.
However, if any other exception is thrown (e.g. an `AssertionError` if
there are gaps in blocks ref_loc) , the store is not closed and some
client code could end up trying to reopen the store and hit an
error like: `the file XXX is already opened. Please close it before
reopening in write mode`. Furthermore, the exception is re-raised in all
cases.
This commit just catches any exception.
Closes #28430
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"description": "read_hdf, HDFStore",
... | open | false | null | [] | null | 0 | 2019-09-13T16:09:35Z | 2020-05-16T20:39:54Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
Code adapted from https://github.com/ToucanToco/peakina/blob/master/peakina/cache.py#L84
```python
try:
metadata = pd.read_hdf(filepath)
except Exception: # catch all, on purpose
metadata = pd.DataFrame(columns=['key', 'mtime', 'created_at'])
metadata.to_hdf(filepath, key, mode='w')
return metadata
```
#### Problem description
In `read_hdf`, if the `store.select()` call throws either a `ValueError`, a `TypeError` or a `KeyError` then the store is closed.
However, if any other exception is thrown (e.g. an `AssertionError` if there are gaps in blocks ref_loc) , the store is not closed and some client code could end up trying to reopen the store and hit an error like: `the file XXX is already opened. Please close it before
reopening in write mode`.
#### Expected Output
No reentrant exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.4
scipy : None
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28431/events | https://github.com/pandas-dev/pandas/pull/28431 | 493,411,601 | MDExOlB1bGxSZXF1ZXN0MzE3Mzk2NTk5 | 28,431 | REF: implement arithmetic, comparison, logical ops on arrays | {
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} | 9 | 2019-09-13T16:20:35Z | 2019-09-23T14:29:13Z | 2019-09-23T12:55:02Z | MEMBER | null | Following #28413, #28396, and #28395, these are all pretty straightforward refactors.
`arithmetic_op`, `comparison_op`, and `logical_op` are going to become the ops that we call `Block`-wise. Hopefully also `PandasArray` will use them directly.
Other follow-ups I have in mind:
- Move `should_extension_dispatch` and `dispatch_to_extension_op` so we dont need runtime imports
- get rid of `eval_kwargs` entirely, they're not really necessary at this point
- see if we can use `numexpr` for comparison/logical ops
- docstrings
- address inconsistencies in when we call `extract_array`
- address inconsistencies in how `Series` methods handle `__finalize__` and alignment
- simplify `logical_op`'s `na_op` | {
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} | 3 | 2019-09-13T16:29:52Z | 2019-09-17T05:45:39Z | 2019-09-17T05:45:38Z | CONTRIBUTOR | null | I messed up in https://github.com/pandas-dev/pandas/pull/28374/files#diff-2eeaed663bd0d25b7e608891384b7298R531. I meant to revert the lines that excluded cythonized files in the sdist to save it for a followup.
So I'm using this to clean up the sdist logic a bit, and exclude a few test data files that have snuck into our distribution. | {
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} | 4 | 2019-09-13T18:35:43Z | 2019-10-01T16:55:05Z | 2019-10-01T16:55:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: s = pd.Series([4,5,6])
...: s1 = pd.Series([3,6, 5])
...: s2 = pd.Series([8,8,8])
...: s.between(s1, s2)
Out[2]:
0 True
1 False
2 True
dtype: bool
```
#### Problem description
The docs for `Series.between()` at https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.between.html indicate that the `left` and `right` arguments are scalars, but it is possible (as shown above) to use a `Series` as either argument.
The docs should be updated to reflect this, including the examples.
#### Expected Output
N/A
#### Output of ``pd.show_versions()``
<details>
C:\Anaconda3\lib\site-packages\xarray\core\merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)
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 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
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 : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
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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https://api.github.com/repos/pandas-dev/pandas/issues/28436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28436/events | https://github.com/pandas-dev/pandas/pull/28436 | 493,504,113 | MDExOlB1bGxSZXF1ZXN0MzE3NDcyMjgy | 28,436 | BUG: Fix merging non-indexes causes Index dtype promotion in when keys are missing | {
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | null | 9 | 2019-09-13T20:28:31Z | 2020-01-03T14:44:03Z | 2020-01-01T18:06:43Z | CONTRIBUTOR | null | ... from left or right side. (GH28220)
Also closes GH24897, GH24212, and GH17257
- [x] closes #28220, #24897, #24212, and #17257
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Probably requires a bit more cleanup and reduction of spaghetti... Not in love with the the solution (as it's quite similar to using both indexes) and requires extension test case changes, but hoping to get some comments and feedback on making it better.
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https://api.github.com/repos/pandas-dev/pandas/issues/28437 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28437/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28437/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28437/events | https://github.com/pandas-dev/pandas/pull/28437 | 493,513,875 | MDExOlB1bGxSZXF1ZXN0MzE3NDgwNDcw | 28,437 | PERF: make io imports lazy in PY37+ | {
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"... | closed | false | null | [] | null | 0 | 2019-09-13T20:56:55Z | 2019-09-15T17:30:27Z | 2019-09-15T17:30:22Z | MEMBER | null | This shaves about 3.5-4% off of our import time.
If we want to continue down this path, we can do the same thing for `eval` and `reshape.api` pretty easily. Probably more than that, but I haven't looked that far.
FWIW: a full run of `asv` (on one commit, not `continuous`) imports pandas just over 5000 times, so these couple-dozen milliseconds add up. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28438/events | https://github.com/pandas-dev/pandas/pull/28438 | 493,527,906 | MDExOlB1bGxSZXF1ZXN0MzE3NDkyMTMw | 28,438 | BUG: Don't cast categorical nan to int | {
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} | 7 | 2019-09-13T21:42:13Z | 2019-09-19T13:41:08Z | 2019-09-18T12:35:04Z | MEMBER | null | - [x] closes #28406
- [x] passes `black pandas`
- [x] tests added / passed
- [x] whatsnew entry
This raises an error when attempting to cast a `Categorical` or `CategoricalIndex` containing `nans` to an integer dtype. Also had to remove the casting within `get_indexer_non_unique` since this won't always be possible. | {
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```python
import numpy as np
import pandas as pd
from concurrent.futures import ThreadPoolExecutor
fs = HadoopFileSystem(...)
table = fs.read_parquet(index_path, use_threads=True)
dataset_index = table.to_pandas(categories=['key'])
# dataset_index.loc[0] # <-- uncommenting fixes the issue
max_loc = dataset_index.shape[0]
def foo():
return dataset_index.loc[np.random.randint(0, max_loc - 1)]['key']
with ThreadPoolExecutor(max_workers=100) as executor:
futures = [executor.submit(foo) for i in range(100)]
a = [f.result() for f in futures]
```
#### Problem description
When loading a large parquet file (230MB on disk and ~1.5GB in memory) and trying to access `loc[x]` on a different thread, I am getting the following crash:
```
Traceback (most recent call last):
File "/home/yevgeni/uatc/av/source/python/repro_race.py", line 55, in <module>
a = [f.result() for f in futures]
File "/tmp/zz/local/lib/python2.7/site-packages/concurrent/futures/_base.py", line 455, in result
return self.__get_result()
File "/tmp/zz/local/lib/python2.7/site-packages/concurrent/futures/thread.py", line 63, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/yevgeni/uatc/av/source/python/repro_race.py", line 48, in foo
res = dataset_index.loc[idx]['key']
File "/tmp/zz/local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1500, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/tmp/zz/local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1913, in _getitem_axis
return self._get_label(key, axis=axis)
File "/tmp/zz/local/lib/python2.7/site-packages/pandas/core/indexing.py", line 141, in _get_label
return self.obj._xs(label, axis=axis)
File "/tmp/zz/local/lib/python2.7/site-packages/pandas/core/generic.py", line 3575, in xs
loc = self.index.get_loc(key)
File "/tmp/zz/local/lib/python2.7/site-packages/pandas/core/indexes/base.py", line 2604, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 987, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 993, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 147272
```
Found two possible workarounds:
- Call `dataset_index.loc[0]` before accessing the index on other threads.
- Repeatative calls to `loc[x]` on the thread will make `KeyError` go away and I would get the correct value.
Other observations: before 0.24.0 I would be able to reproduce the failure by using `dataset_index = pd.DataFrame(np.random.randn(5000000, 4), columns=list('ABCD'))`, but after 0.24.0 this reproduces only on a parquet file loaded over a slow network connection.
#### Expected Output
I would expect the code to be thread-safe..
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-60-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.0
pytest: None
pip: 19.2.3
setuptools: 41.2.0
Cython: None
numpy: 1.16.5
scipy: None
pyarrow: 0.14.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: 0.3.2
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | open | false | null | [] | null | 4 | 2019-09-14T02:23:12Z | 2020-11-26T22:06:27Z | null | CONTRIBUTOR | null | Numpy 1.17 introduced a new random module with faster PRNGs, and which drops the strict reproducibility of random streams guarantee, which allows some algorithmic improvements. In particular, the choice method is now a lot faster in the replace=False case. Would it make sense for ``random_state`` to return a np.random.Generator instead of a np.random.RandomState when numpy version >= 1.17 here: https://github.com/pandas-dev/pandas/blob/master/pandas/core/common.py#L408. This would automatically speed up the DataFrame.sample method for instance.
I can write a PR, but I'm not sure how to handle the different numpy versions. Should I just do the tests inside ``random_state`` or is something that needs to go inside numpy.compat?
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} | 1 | 2019-09-14T08:24:24Z | 2019-09-14T09:00:57Z | 2019-09-14T09:00:44Z | MEMBER | null | See https://github.com/pandas-dev/pandas/pull/28374#discussion_r324367518 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28446/events | https://github.com/pandas-dev/pandas/issues/28446 | 493,608,771 | MDU6SXNzdWU0OTM2MDg3NzE= | 28,446 | truediv argument of pandas.eval ignored. | {
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"description": "Arithmetic, Compariso... | closed | false | null | [] | null | 5 | 2019-09-14T10:25:13Z | 2019-11-30T00:01:50Z | 2019-11-30T00:01:50Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'0.25.0+350.g330bedeb7'
>>>
>>> df = pd.DataFrame({'A': range(1, 6)})
>>>
>>> df.eval('A/2')
0 0.5
1 1.0
2 1.5
3 2.0
4 2.5
dtype: float64
>>>
>>> df.eval('A/2', truediv = False)
0 0.5
1 1.0
2 1.5
3 2.0
4 2.5
dtype: float64
>>>
>>> df.eval('A/2', truediv = True)
0 0.5
1 1.0
2 1.5
3 2.0
4 2.5
dtype: float64
```
#### Problem description
The docs state..
```
truediv : bool, optional
Whether to use true division, like in Python >= 3
```
but the `truediv` argument is ignored. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28447 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28447/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28447/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28447/events | https://github.com/pandas-dev/pandas/pull/28447 | 493,628,028 | MDExOlB1bGxSZXF1ZXN0MzE3NTY0NTg0 | 28,447 | [PERF] Vectorize select_dtypes | {
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} | 17 | 2019-09-14T14:23:46Z | 2019-09-20T12:42:58Z | 2019-09-20T12:42:53Z | CONTRIBUTOR | null | - [x] closes #28317
- [x] tests added / passed (no new tests added, test_dtypes.py, passes locally)
- [x] passes `black pandas` (passed locally)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (passed locally)
- [x] whatsnew entry
- [x] ASV Benchmark
- [x] final refactoring
I was able to run this code for 10million columns in less than a second, which should solve the mentioned problem sufficiently well.
<details><summary>ASV Benchmark:</summary>
```
[ 75.00%] ··· frame_methods.SelectDtypes.time_select_dtypes ok
[ 75.00%] ··· ======== =============
n
-------- -------------
10 1.53±0.2ms
100 1.48±0.03ms
1000 1.50±0.06ms
10000 1.89±0.04ms
100000 6.55±0.2ms
======== =============
[ 75.00%] · For pandas commit ad9fe5d7 <master^2> (round 2/2):
[ 75.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 75.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[100.00%] ··· frame_methods.SelectDtypes.time_select_dtypes ok
[100.00%] ··· ======== ============
n
-------- ------------
10 2.04±0.2ms
100 10.0±0.3ms
1000 91.2±2ms
10000 898±50ms
100000 8.93±0.08s
======== ============
before after ratio
[ad9fe5d7] [ef1b9a49]
<master^2> <28317-select-dtypes-performance>
- 10.0±0.3ms 1.48±0.03ms 0.15 frame_methods.SelectDtypes.time_select_dtypes(100)
- 91.2±2ms 1.50±0.06ms 0.02 frame_methods.SelectDtypes.time_select_dtypes(1000)
- 898±50ms 1.89±0.04ms 0.00 frame_methods.SelectDtypes.time_select_dtypes(10000)
- 8.93±0.08s 6.55±0.2ms 0.00 frame_methods.SelectDtypes.time_select_dtypes(100000)
```
I hope this is sufficient
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} | 4 | 2019-09-14T16:28:24Z | 2019-10-31T15:56:19Z | 2019-10-31T15:56:19Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
from pandas import Categorical
Categorical(['2015-01-01']).astype('datetime64[ns]') # works
Categorical(['2015-01-01']).astype('datetime64[ns, MET]') # fails
```
This is because the `astype` method returns
```
np.array(self, dtype=dtype, copy=copy)
```
, and `numpy` arrays don't handle this dtype.
Is this expected behaviour?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-60-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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"description": "Code style, linting, ... | closed | false | null | [] | null | 1 | 2019-09-14T18:23:34Z | 2019-09-15T13:11:45Z | 2019-09-15T13:11:32Z | CONTRIBUTOR | null | Solves:
- Unknown parameters {kwargs, args}
- Parameter "func" type should use "str" instead of "string"
- Parameter "*args" description should start with a capital letter
- Parameter "**kwargs" description should start with a capital letter
- pandas.core.common.pipe : Unknown parameters {kwargs, args} in #27976
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} | 0 | 2019-09-14T19:05:54Z | 2019-09-15T15:23:56Z | 2019-09-15T13:14:00Z | CONTRIBUTOR | null | The sqlite connection is already in transaction when the test starts, so
when the test intentionally raises an exception, the entire transaction,
including the table creation, gets rolled back, so there is no table
to query. Wrapping the table CREATE statement in its own transaction
avoids the issue.
- [x] closes #14719
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Is a whatsnew entry needed for a test fix issue? | {
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] | open | false | null | [] | null | 9 | 2019-09-14T21:36:53Z | 2019-09-22T15:18:22Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df1 = pd.DataFrame(data=np.random.rand(100,1))
df2 = pd.DataFrame(data=np.random.rand(100,1))
a = plt.subplot()
df1.plot(ax=a,style='.')
df2.plot(ax=a,style='.')
```
#### Problem description
When plotting multiple dataframe on the same axes AND using the style '.' , only the last legend entry marker appears, the others remain blank.
Note, this problem doesn't appear when using the default plotting style.
#### Expected Output
All the legend entries should correctly match the various plots.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.16.final.0
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.2
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: 1.8.5
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28452 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28452/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28452/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28452/events | https://github.com/pandas-dev/pandas/issues/28452 | 493,905,378 | MDU6SXNzdWU0OTM5MDUzNzg= | 28,452 | crosstab sometimes erroneously handles dt operations | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-16T08:14:00Z | 2019-09-16T22:11:57Z | 2019-09-16T22:11:57Z | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'date': ['2019-01-01', '2019-02-01', '2018-01-01', '2018-02-01']})
df["date"] = pd.to_datetime(df["date"])
pd.crosstab(df["date"].dt.month, df["date"].dt.year)
```
#### Problem description
This gives my a `2x2` table with the years 2018 and 2019 on both axes, thus wrongly treating the `dt.month` operation. The same thing happens if I replace `dt.month` with `dt.day` in my example. Interestingly, `pd.crosstab(df["date"].dt.day, df["date"].dt.month)` works as expected, while `pd.crosstab(df["date"].dt.month, df["date"].dt.day)` does not!
#### Expected Output
| year | 2018 | 2019 |
|-------|------|------|
| month | | |
| 1 | 1 | 1 |
| 2 | 1 | 1 |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 2 | 2019-09-16T09:08:55Z | 2019-09-20T14:38:51Z | 2019-09-20T14:32:33Z | CONTRIBUTOR | null | - [x] part of #26792
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] annotate all rendering methods of `pandas.core.frame.DataFrame`
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"d... | open | false | null | [] | null | 1 | 2019-09-16T09:10:17Z | 2020-05-22T03:36:28Z | null | CONTRIBUTOR | null | follow up https://github.com/pandas-dev/pandas/pull/28176#pullrequestreview-286089893
Right now, `astype(str)` converts `np.nan` into string.
```
In [3]: pd.Series(['foo', np.nan]).astype(str)
Out[3]:
0 foo
1 nan
dtype: object
```
After merging #28176, `astype(str)` will preserve `np.nan`.
Review `test_astype_str_map` in pandas/tests/series/test_dtypes.py to get rid of any `skipna` keyword argument.
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"id": 49379259,
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"default": fa... | open | false | null | [] | null | 5 | 2019-09-16T14:32:17Z | 2020-05-08T21:13:48Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
from pandas.io.json import build_table_schema
df = pandas.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
build_table_schema(df)
```
produces
```
{'fields': [{'name': 'index', 'type': 'integer'},
{'name': 'col1', 'type': 'integer'},
{'name': 'col2', 'type': 'integer'}],
'primaryKey': ['index'],
'pandas_version': '0.20.0'}
```
#### Problem description
The version `0.20.0` is hard coded in the function.
#### Expected Output
`pandas.__version__` returns `'0.25.0+349.g9dc6de3.dirty'`, which should also be returned by `build_table_schema`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ac6bbaf0620a634b430f444371facc165585976f
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-55-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 0.25.0+350.gac6bbaf
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.5.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 9 | 2019-09-16T14:34:49Z | 2019-09-18T14:23:40Z | 2019-09-18T14:23:40Z | CONTRIBUTOR | null | In preparation for the removal of SparseSeries and SparseDataFrame, we
read into a Series[sparse] / DataFrame[sparse]. https://github.com/pandas-dev/pandas/pull/28425
cc @jreback @jorisvandenbossche.
I've just done the minimal amount to get this working. It may shed some light on how we can design an API for dispatching writing for 3rd party EAs (I think we should ask the EA for the values (arrays) to write, and the bits of extra metadata. Pandas should take care of the actual writing though. But that's for another day). | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-16T14:38:35Z | 2019-09-16T16:03:50Z | 2019-09-16T16:03:49Z | NONE | null | - [x] Issue #28455
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | open | false | null | [] | null | 4 | 2019-09-16T14:40:07Z | 2020-12-13T16:41:13Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
Using a sample file of:
```
email
test@gmail.com
test2@yahoo.com
test3@microsoft.com
```
```python
import pandas as pd
pd.read_csv('email.txt', sep=None)
Out[24]:
e ail
test@g ail.co NaN
test2@yahoo.co NaN NaN
test3@ icrosoft.co NaN
```
#### Problem description
There is no way to pass the 'delimiters' argument to csv.Sniffer.sniff when using pd.read_csv(sep=None) causing an invalid detection of the delimiter in files with only a single column.
#### Expected Output
```python
email
0 test@gmail.com
1 test2@yahoo.com
2 test3@microsoft.com
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.1
pytz : 2017.3
dateutil : 2.6.1
pip : 19.0.1
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : 1.7.6
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.1.1
html5lib : 1.0b10
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.5.14
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.0.4
</details>
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} | 7 | 2019-09-16T14:54:32Z | 2019-11-19T13:44:24Z | 2019-11-19T13:42:59Z | CONTRIBUTOR | null | - [X] closes #28427
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 1 | 2019-09-16T14:56:07Z | 2020-05-03T20:34:01Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'string': ['a', 'b'], 'number': [1.0, 2.0], 'mix': ['a', 2.0]})
df.to_hdf('foo.h5', 'data')
```
#### Problem description
The problem is related to the feature added in #3622. The warning for types that cannot be saved as c-types applies for all `object` columns if any such column exists in the data frame. If all columns are either strings or numbers (but not mixed within a column) no warning is produced (which is expected).
#### Output
```
python3.6/site-packages/pandas/core/generic.py:2530: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['string', 'mix']]
pytables.to_hdf(path_or_buf, key, self, **kwargs)
```
#### Expected Output
Warnings only for column `mix`.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28461 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28461/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28461/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28461/events | https://github.com/pandas-dev/pandas/issues/28461 | 494,106,452 | MDU6SXNzdWU0OTQxMDY0NTI= | 28,461 | `DataFrame.pct_change` should default `fill_method=None` instead of `'pad'` | {
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] | closed | false | null | [] | null | 9 | 2019-09-16T15:01:11Z | 2019-10-24T05:27:43Z | 2019-10-23T22:59:45Z | CONTRIBUTOR | null | Both `DataFrame.pct_change` and `Series.pct_change` should have default behavior that matches the "obvious" implementation:
```python
lag = series.shift(1)
change = (series - lag) / lag
```
Unfortunately, the default behavior is set to `fill_method='pad'` which surprises many people (#6389). We should start a deprecation cycle to change the default to behave as expected, `fill_method=None`. I don't believe this will be disruptive, as most people think that's how it behaves currently, as far as I know. | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-16T15:14:53Z | 2019-09-16T16:16:26Z | 2019-09-16T16:16:25Z | NONE | null | Dear Pull Requests integrators,
We are an international group of researchers investigating Pull Requests management activities. We implemented an automated approach, named CARTESIAN, for prioritizing Pull Requests (PRs) received by an open-source project according to the likelihood of acceptance/response. We would like to evaluate whether CARTESIAN can help integrators when reviewing PRs.
As we noticed that your project receives many PRs daily, we have taken the liberty of contacting you. Thus, we experimented CARTESIAN on Your projects, to help you in prioritizing PRs. To know more about these results, please fill in the form below.
Survey form link: https://forms.gle/P3dwRhZ7k8MDanvX6
Your participation is voluntary and confidential. We kindly request you, ONLY to INTEGRATORS, to participate in this study which is expected to take about 15 minutes of your time. You might withdraw at any time.
Best regards,
Muhammad Ilyas Azeem, National Engineering Research Center of Fundamental Software, Chinese Academy of Sciences, China
Andrea Di Sorbo, University of Sannio, Italy
Sebastiano Panichella, Zurich University of Applied Science, Switzerland
Alexander Serebrenik, Eindhoven University of Technology, The Netherlands
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} | 10 | 2019-09-16T15:16:31Z | 2019-10-22T13:29:41Z | 2019-10-22T01:31:32Z | CONTRIBUTOR | null | - [x] closes #28383
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The Test code is inspired from a similar tests in
`RaiseError`: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/arrays/categorical/test_operators.py#L97
Valid `list_like`: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/reshape/test_pivot.py#L706
This is my first pull request here and so I have taken all the necessary precautions, also I read the contributing guidelines. Not much sure of `black pandas` .
**Problem:**
If `columns` is not passed a list-like value a substantial decrease in performance was observed.
**Proposed Solution:**
As mentioned in the issue by @WillAyd that function should raise and hence based on [similar situation](https://github.com/pandas-dev/pandas/blob/2c9c4223442cd555a1fbc894eb5e89792c09ea63/pandas/core/arrays/categorical.py#L2484) in the codebase, I have edited the file to raise `TypeError`.
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} | [] | closed | false | null | [] | null | 3 | 2019-09-16T16:36:51Z | 2019-09-18T13:35:59Z | 2019-09-18T13:35:59Z | NONE | null | Now .explode() can take a list of column names and will explode multiple at the same time (given that each element across all the columns have the same length in every single row
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "np.nan, ... | closed | false | null | [] | null | 5 | 2019-09-16T17:56:07Z | 2020-10-12T21:56:48Z | 2020-10-12T21:56:48Z | MEMBER | null | Based on tests.series.test_operators `test_logical_ops_df_compat`
```
# GH#1134
s1 = pd.Series([True, False, True], index=list("ABC"))
s2 = pd.Series([True, True, False], index=list("ABD"))
exp_ser = pd.Series([True, False, False, False], index=list("ABCD"))
res_ser = s1 & s2
tm.assert_series_equal(res_ser, exp_ser)
# DataFrame doesn't fill nan with False
exp_frame = pd.DataFrame({"x": [True, False, np.nan, np.nan]}, index=list("ABCD"))
res_frame = s1.to_frame() & s2.to_frame()
assert_frame_equal(res_frame, exp_frame)
```
Is there still a compelling reason to keep this mismatched behavior? | {
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} | 10 | 2019-09-16T18:21:40Z | 2019-10-30T13:08:36Z | 2019-10-29T13:09:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
# Yields an error
animals.groupby('kind').agg(mean_height=('height', 'mean'),
perc90=('height', lambda s: np.percentile(s, q=0.90)))
def myfunc(s): return np.percentile(s, q=0.90)
# Works just fine
animals.groupby('kind').agg(mean_height=('height', 'mean'),
perc90=('height', myfunc))
```
#### Problem description
When I `groupby`+`agg` with a named function, the named aggregation works perfectly. However, when done with a lambda function instead, the following error is raised:
KeyError: "[('height', '<lambda>')] not in index"
Notice that his is not [error 7186](https://github.com/pandas-dev/pandas/issues/7186) because there are no more than one lambda here.
I've noticed that this error only happens when I mix `lambda` functions along with `str`-mapped functions, i.e. _just_
animals.groupby('kind').agg(perc90=('height', lambda s: np.percentile(s, q=0.90)))
works just fine.
#### Expected Output
mean_height perc95
kind
cat 9.3 9.1038
dog 20.0 6.2660
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1036-gcp
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
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
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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] | closed | false | null | [] | null | 1 | 2019-09-16T19:45:07Z | 2020-03-09T20:07:38Z | 2019-09-16T21:20:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
s1 = pd.Series([pd.Timestamp('2017-01-01T10', tz='UTC'), pd.Timestamp('2017-01-01T11', tz='UTC')])
s2 = pd.Series([pd.Timestamp('2017-01-01T12', tz='UTC'), pd.NaT])
df = pd.DataFrame(dict(s1=s1, s2=s2))
df.min(axis=1)
```
produces:
```
0 NaN
1 NaN
dtype: float64
```
#### Problem description
For tz-aware timestamp columns, as soon as `pd.NaT` value is present in any row in any of the columns, than the column-wise `min()/max()` operations returns all `NaN`s.
If columns are non-tz-aware, then `min()/max()` work as expected, i.e. the result is similar to the one pasted below (except for the timezone part).
#### Expected Output
```
0 2017-01-01 10:00:00+00:00
1 2017-01-01 11:00:00+00:00
dtype: datetime64[ns, UTC]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 18.0
setuptools : 41.2.0
Cython : 0.27.3
pytest : 4.4.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : None
pymysql : None
psycopg2 : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-09-16T20:22:27Z | 2019-10-07T15:01:02Z | 2019-10-07T15:01:02Z | CONTRIBUTOR | null | The length of `formatters` should match the number of columns
Both of these should error with a nice message.
```python
In [2]: df = pd.DataFrame(columns=['a', 'b', 'c'])
In [3]: df.to_html(formatters=['{}'.format])
In [4]: df.to_html(formatters=['{}'.format] * 5)
```
Currntly `In [4]` succeeds, silently ignoring the bad input. `In[3]` values with a vague `IndexError: list index out of range`. | {
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Decorator that just registers the deprecation in a dict that we can check and test-time. | {
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Currently an issue in Dask https://github.com/dask/dask/issues/5294 for implementing Named Aggregation (introduced in pandas 0.25.0) is open. To implement this it needs to use `_normalize_keyword_aggregation` and `_is_multi_agg_with_relabel`.
Making it public would be useful for frameworks like [dask](https://github.com/dask/dask) [mars](https://github.com/mars-project/mars)
cc: @TomAugspurger | {
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} | 7 | 2019-09-16T22:00:22Z | 2020-04-02T23:02:40Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
def test_idxmin(self):
df = pd.DataFrame({'accountid': ['12345'], 'createddate': [pd.Timestamp(2019, 1, 1)]})
min = df.groupby('accountid')[['createddate']].idxmin(axis=1)
print(min)
df2 = pd.DataFrame({'accountid': ['12345', '23456'], 'createddate': [pd.Timestamp(2019, 1, 1), pd.Timestamp(2019, 2, 1)]})
min2 = df2.groupby('accountid')[['createddate']].idxmin(axis=1)
print(min2)
```
#### Problem description
The first print statement shows:
0
accountid
12345 createddate
Current behavior appears to swap the column name and minimizer value.
The second print statement shows the correct result:
createddate
accountid
12345 0
23456 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.15.2
pytz : 2018.7
dateutil : 2.7.5
pip : 19.0.3
setuptools : 40.5.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 5.0.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.0
gcsfs : None
lxml.etree : None
matplotlib : 3.0.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.0
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
**Problem:** Currently if you give two Series with the same name to pd.crosstab it will ignore one of the columns. By minimally modifying a current test, it no longer passes.
**Replicable example of the issue (more examples in the tests of this PR):**
```
import pandas as pd
s1 = pd.Series(range(3), name='foo')
s2 = s1 + 1
expected = pd.crosstab(s1, s2.rename("bar")) # If we rename one of the series we get the correct result
result = pd.crosstab(s1, s2)
pd.testing.assert_frame_equal(result, expected)
```
**Proposed solution (implemented in this PR):**
- ~~Raise an exception if the user tries to use duplicate column names, duplicated index, or a name is shared between the index and columns~~.
Updated (implemented) proposal:
- Find which names are duplicated and add a number to them
- If a name is shared between rows and columns, add a "_row" and "_col" to them.
**Alternative solutions:**
- Append a number without checking to each name.
- Pro: Simpler code (don't have to look for duplicates nor know how many times a name is duplicated).
- Con: Might be confusing for the user to see an error in column "column_name_3", specially if their names already had numbers in them.
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} | 2 | 2019-09-17T01:54:13Z | 2019-09-19T13:40:57Z | 2019-09-17T22:10:54Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
Raises a `ValueError` when attempting to cast a `Float64Index` with infinite values to a (non-`ExtensionArray`) integer dtype. Currently this returns a garbage value:
```
idx
# Float64Index([1.0, 2.0, inf], dtype='float64')
idx.astype(int)
# Int64Index([1, 2, -9223372036854775808], dtype='int64')
```
Related PR: https://github.com/pandas-dev/pandas/pull/28438 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28476 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28476/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28476/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28476/events | https://github.com/pandas-dev/pandas/issues/28476 | 494,374,956 | MDU6SXNzdWU0OTQzNzQ5NTY= | 28,476 | BUG: DataFrame[SparseArray] coerces to dense after np.sqrt | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-17T03:03:35Z | 2019-09-17T20:11:15Z | 2019-09-17T18:12:45Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
X = np.array([[0, 1, 0], [4, 0, 0]])
X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
X
X.sparse
np.sqrt(X).sparse
```
#### Problem description
The data shouldn't be coerced to dense. Here is the output for the above.
```python
>>> import numpy as np
>>> import pandas as pd
>>> X = np.array([[0, 1, 0], [4, 0, 0]])
>>> X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
>>> X
0 1 2
0 0.0 1.0 0.0
1 4.0 0.0 0.0
>>> X.sparse
<pandas.core.arrays.sparse.SparseFrameAccessor object at 0x7fdd8d38f0f0>
>>> np.sqrt(X).sparse
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 5179, in __getattr__
return object.__getattribute__(self, name)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/accessor.py", line 175, in __get__
accessor_obj = self._accessor(obj)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/arrays/sparse.py", line 1957, in __init__
self._validate(data)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/arrays/sparse.py", line 2134, in _validate
raise AttributeError(self._validation_msg)
AttributeError: Can only use the '.sparse' accessor with Sparse data.
```
#### Expected Output
I would expect the resulting array to have the same sparsity structure as previously, given that the ufunc has not changed the zeros.
```python
>>> import numpy as np
>>> import pandas as pd
>>> X = np.array([[0, 1, 0], [4, 0, 0]])
>>> X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
>>> X
0 1 2
0 0.0 1.0 0.0
1 4.0 0.0 0.0
>>> X.sparse
<pandas.core.arrays.sparse.SparseFrameAccessor object at 0x7fdd8d38f0f0>
>>> np.sqrt(X).sparse
<pandas.core.arrays.sparse.SparseFrameAccessor object at 0x7fdd8d38f0f0>
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.10-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28477 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28477/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28477/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28477/events | https://github.com/pandas-dev/pandas/issues/28477 | 494,379,133 | MDU6SXNzdWU0OTQzNzkxMzM= | 28,477 | BUG: DataFrame[SparseArray] coerces to dense with .T | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-17T03:22:02Z | 2019-09-17T18:10:36Z | 2019-09-17T18:10:36Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
X = np.array([[0, 1, 0], [4, 0, 0]])
X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
X.dtypes
X.T.dtypes
```
#### Problem description
Given the entire dataframe is sparse, it should probably remain sparse on transpose?
Current output:
```
>>> import numpy as np
>>> import pandas as pd
>>> X = np.array([[0, 1, 0], [4, 0, 0]])
>>> X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
>>> X.dtypes
0 Sparse[float64, 0.0]
1 Sparse[float64, 0.0]
2 Sparse[float64, 0.0]
dtype: object
>>> X.T.dtypes
0 float64
1 float64
dtype: object
```
#### Expected Output
```
>>> import numpy as np
>>> import pandas as pd
>>> X = np.array([[0, 1, 0], [4, 0, 0]])
>>> X = pd.DataFrame(X).astype(pd.SparseDtype(float, fill_value=0.0))
>>> X.dtypes
0 Sparse[float64, 0.0]
1 Sparse[float64, 0.0]
2 Sparse[float64, 0.0]
dtype: object
>>> X.T.dtypes
0 Sparse[float64, 0.0]
1 Sparse[float64, 0.0]
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.10-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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} | 16 | 2019-09-17T05:05:20Z | 2019-11-07T21:19:58Z | 2019-11-07T21:19:58Z | NONE | null | When you group some statistical counts for every day, it is possible that on some day there is no counts at all. This will result in empty groups in the groupby object. Performing value_counts() on such groupby objects causes crash.
The following example illustrates the problem:
```
import pandas as pd
df = pd.DataFrame({'Timestamp':[1565083561, 1565083561+86400, 1565083561+86500, 1565083561+86400*2, 1565083561+86400*3, 1565083561+86500*3, 1565083561+86400*4],
'Food':['apple', 'apple', 'banana', 'banana', 'orange', 'orange', 'pear']})
df['Datetime'] = pd.to_datetime(df['Timestamp'].apply(lambda t:str(t)), unit='s')
display(df)
dfg = df.groupby(pd.Grouper(freq='1D', key='Datetime'))
[print(g) for g in dfg]
display(dfg['Food'].value_counts())
df = df.drop([3])
display(df)
dfg = df.groupby(pd.Grouper(freq='1D', key='Datetime'))
[print(g) for g in dfg]
display(dfg['Food'].value_counts())
```
**This table does not contain days with empty data, value_counts() does not crash:**
Timestamp | Food | Datetime
-- | -- | --
1565083561 | apple | 2019-08-06 09:26:01
1565169961 | apple | 2019-08-07 09:26:01
1565170061 | banana | 2019-08-07 09:27:41
1565256361 | banana | 2019-08-08 09:26:01
1565342761 | orange | 2019-08-09 09:26:01
1565343061 | orange | 2019-08-09 09:31:01
1565429161 | pear | 2019-08-10 09:26:01
**After groupby each day:**
(Timestamp('2019-08-06 00:00:00', freq='D'), Timestamp Food Datetime
0 1565083561 apple 2019-08-06 09:26:01)
(Timestamp('2019-08-07 00:00:00', freq='D'), Timestamp Food Datetime
1 1565169961 apple 2019-08-07 09:26:01
2 1565170061 banana 2019-08-07 09:27:41)
(Timestamp('2019-08-08 00:00:00', freq='D'), Timestamp Food Datetime
3 1565256361 banana 2019-08-08 09:26:01)
(Timestamp('2019-08-09 00:00:00', freq='D'), Timestamp Food Datetime
4 1565342761 orange 2019-08-09 09:26:01
5 1565343061 orange 2019-08-09 09:31:01)
(Timestamp('2019-08-10 00:00:00', freq='D'), Timestamp Food Datetime
6 1565429161 pear 2019-08-10 09:26:01)
**Result of value_counts():**
Datetime Food
2019-08-06 apple 1
2019-08-07 apple 1
` ` banana 1
2019-08-08 banana 1
2019-08-09 orange 2
2019-08-10 pear 1
Name: Food, dtype: int64
**This table contains a day with empty data (2019-08-08), value_counts() will crash:**
Timestamp | Food | Datetime
-- | -- | --
1565083561 | apple | 2019-08-06 09:26:01
1565169961 | apple | 2019-08-07 09:26:01
1565170061 | banana | 2019-08-07 09:27:41
1565342761 | orange | 2019-08-09 09:26:01
1565343061 | orange | 2019-08-09 09:31:01
1565429161 | pear | 2019-08-10 09:26:01
**After groupby each day (note the empty group on 2019-08-08):**
(Timestamp('2019-08-06 00:00:00', freq='D'), Timestamp Food Datetime
0 1565083561 apple 2019-08-06 09:26:01)
(Timestamp('2019-08-07 00:00:00', freq='D'), Timestamp Food Datetime
1 1565169961 apple 2019-08-07 09:26:01
2 1565170061 banana 2019-08-07 09:27:41)
_(Timestamp('2019-08-08 00:00:00', freq='D'), Empty DataFrame
Columns: [Timestamp, Food, Datetime]
Index: [])_
(Timestamp('2019-08-09 00:00:00', freq='D'), Timestamp Food Datetime
4 1565342761 orange 2019-08-09 09:26:01
5 1565343061 orange 2019-08-09 09:31:01)
(Timestamp('2019-08-10 00:00:00', freq='D'), Timestamp Food Datetime
6 1565429161 pear 2019-08-10 09:26:01)
**value_counts() crashes:**
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-543-5efc1c882109> in <module>
14 [print(g) for g in dfg]
15 print('This will cause crash:')
---> 16 display(dfg['Food'].value_counts())
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in value_counts(self, normalize, sort, ascending, bins, dropna)
1137
1138 # multi-index components
-> 1139 labels = list(map(rep, self.grouper.recons_labels)) + [llab(lab, inc)]
1140 levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
1141 names = self.grouper.names + [self._selection_name]
~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py in repeat(a, repeats, axis)
469
470 """
--> 471 return _wrapfunc(a, 'repeat', repeats, axis=axis)
472
473
~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
54 def _wrapfunc(obj, method, *args, **kwds):
55 try:
---> 56 return getattr(obj, method)(*args, **kwds)
57
58 # An AttributeError occurs if the object does not have
ValueError: operands could not be broadcast together with shape (5,) (4,)
```
It turns out that this might result from a design flaw in DataFrame construction that it skips empty rows:
`pd.DataFrame.from_dict(data={'row1':{'a':1, 'b':2}, 'row2': {'a':3, 'b':4, 'c':5}, 'row3':{}}, orient='index').fillna(0)`
| a | b | c
-- | -- | -- | --
row1 | 1 | 2 | 0
row2 | 3 | 4 | 5.0
Take note that row3 is not constructed at all. The correct behavior should output:
| a | b | c
-- | -- | -- | --
row1 | 1 | 2 | 0.0
row2 | 3 | 4 | 5.0
row3 | 0 | 0 | 0.0
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] | open | false | null | [] | null | 1 | 2019-09-17T05:41:23Z | 2020-02-25T21:09:46Z | null | CONTRIBUTOR | null | A consistent naming convention should be used for type aliases.
Maybe CamelCase as the stdlib typing module is in Camel Case, but suggestions are welcome.
Inconsistent names could be found in `pandas.io.formatters.formatter`, `formatters_type` and `float_format_type`. Look for other similar names too and fix them all. | {
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} | 2 | 2019-09-17T10:43:18Z | 2020-11-30T13:21:49Z | 2020-11-30T13:21:49Z | NONE | null | #### Problem Description
`combine_first` returns weird results in case there exist unmatched columns between two timestamp dataframes. this issue probably relates to #24357 (not retaining dtypes)
```python
import pandas as pd
x = pd.DataFrame([pd.NaT,pd.NaT],columns=['a']).T
y = pd.DataFrame([[pd.NaT,pd.NaT],pd.to_datetime(['20190101','20190102'])],index=['a','b'],columns=[1,2])
```
<details>
x|0|1
---|---|---
a|NaT|NaT
y|1|2
---|---|---
a|NaT|NaT
b|2019-01-01|2019-01-02
</details>
##### Output of ``x.combine_first(y)``
current|0|1|2
---|---|---|---
a|NaT|NaT|-9.223372e+18
b|NaT|2019-01-01|1.546387e+18
##### Output of ``x.reindex(columns=x.columns|y.columns).combine_first(y)``
expected|0|1|2
---|---|---|---
a|NaT|NaT|NaT
b|NaT|2019-01-01|2019-01-02
#### INSTALLED VERSIONS
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-80.7.2.el8_0.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2 | {
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"description": ... | closed | false | null | [] | null | 2 | 2019-09-17T12:52:40Z | 2019-09-19T15:19:35Z | 2019-09-19T15:19:35Z | MEMBER | null | Making the CI scripts simpler, and fixing typos (this was part of #26949, which couldn't be merged because `--dist=loadfile` was making the tests much slower).
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```python
# n = df1.groupby(['Year', 'State', 'Regulator', 'Industry','Product', 'Count']).sum() # <-- this produces the error
```
#### Problem description
[Hi, I think there's a problem dropping/excluding data points with groupby.sum function. I've performed the following code (see above), which at hindsight seemed ok until I compared with the same data using Excel and/or simple plot of the dataset. In addition, removing 'Count' will throw off values on other df columns. Thanks for checking this out.]
#### Expected Output
Year | 2012
State | **Alabama**
Regulator | **SEC**
Insurance/Annuity Products | 2
Stocks | 4
Year | 2012
State | **Alabama**
Regulator | **FDIC**
Debit Card | 1
Residential Mortgage | 3
#### Output of ``pd.df``
Year | 2012
State | **Alabama**
Regulator | **FDIC**
Debit Card | 1
Residential Mortgage | 1
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} | 5 | 2019-09-17T15:30:02Z | 2019-09-17T16:22:15Z | 2019-09-17T16:10:43Z | MEMBER | null | @simonjayhawkins do I need to update a config file or enable something? | {
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] | closed | false | null | [] | null | 3 | 2019-09-17T15:44:47Z | 2021-02-05T20:40:32Z | 2021-02-05T20:40:32Z | NONE | null | Matplotlib has a nice formatter class named matplotlib.dates.ConciseDateFormatter, which is a candidate to become Matplotlib's default date formatter in future versions (as of MPL 3.1.1, see
[Matplotlib documentation](https://matplotlib.org/gallery/ticks_and_spines/date_concise_formatter.html)).
It would be nice if the converters defined in pandas.plotting._matplotlib.converter.py
could provide the same date formatting, especially regarding the pandas data types like Period. | {
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} | 2 | 2019-09-17T16:57:48Z | 2020-12-08T02:44:48Z | 2020-12-08T02:44:48Z | NONE | null | Currently, there is no way to specify the schema when calling pandas.io.sql.get_schema, the schema is a helpful parameter when working with certain databases such as MSSQL.
```python
import pandas.io.sql
# current state
table_name = 'my_new_table'
sql_create_statement = pandas.io.sql.get_schema(df, table_name, con)
# create statement looks like
"""
CREATE TABLE [my_new_table] (
[column1] VARCHAR(max) NULL,
....
)
"""
```
this adds an extra step to have to parse the file in order to add the schema, otherwise when I execute the script later, the table is created in user default schema (dbo in most cases). This issue can be easily addressed by adding `schema` parameter to `_create_sql_schema` since `SQLTable` already supports `schema`
```python
import pandas.io.sql
# future state
table_name = 'my_new_table'
schema = 'pypi'
sql_create_statement = pandas.io.sql.get_schema(df, table_name, schema=schema)
# create statement will look like this for MSSQL if con parameter is passed
"""
CREATE TABLE pypi.[my_new_table] (
[Column1],
....
)
"""
```
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} | 5 | 2019-09-17T20:07:23Z | 2021-09-12T17:40:59Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from scipy import sparse
X_sp = sparse.coo_matrix((2**30, 2**10))
X_pd = pd.DataFrame.sparse.from_spmatrix(X_sp)
X_sp.sum(axis=1)
X_sp.sum(axis=0)
X_pd.sum(axis=1)
X_pd.sum(axis=0)
```
#### Problem description
The new sparse dataframe is coerced to dense when computing the sum.
```python
>>> import pandas as pd
>>> from scipy import sparse
>>> X_sp = sparse.coo_matrix((2**30, 2**10))
>>> X_pd = pd.DataFrame.sparse.from_spmatrix(X_sp)
>>> X_sp.sum(axis=1)
matrix([[0.],
[0.],
[0.],
...,
[0.],
[0.],
[0.]])
>>> X_sp.sum(axis=0)
matrix([[0., 0., 0., ..., 0., 0., 0.]])
>>> X_pd.sum(axis=1)
Traceback (most recent call last):
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/frame.py", line 7908, in _reduce
values = self.values
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 5443, in values
return self._data.as_array(transpose=self._AXIS_REVERSED)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 822, in as_array
arr = mgr._interleave()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 840, in _interleave
result = np.empty(self.shape, dtype=dtype)
numpy.core._exceptions.MemoryError: Unable to allocate array with shape (1024, 1073741824) and data type float64
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 11585, in stat_func
min_count=min_count,
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/frame.py", line 7953, in _reduce
result = f(data.values)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 5443, in values
return self._data.as_array(transpose=self._AXIS_REVERSED)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 822, in as_array
arr = mgr._interleave()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 840, in _interleave
result = np.empty(self.shape, dtype=dtype)
numpy.core._exceptions.MemoryError: Unable to allocate array with shape (1024, 1073741824) and data type float64
>>> X_pd.sum(axis=0)
# hangs forever
^C
Traceback (most recent call last):
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/frame.py", line 7908, in _reduce
values = self.values
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 5443, in values
return self._data.as_array(transpose=self._AXIS_REVERSED)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 822, in as_array
arr = mgr._interleave()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 840, in _interleave
result = np.empty(self.shape, dtype=dtype)
numpy.core._exceptions.MemoryError: Unable to allocate array with shape (1024, 1073741824) and data type float64
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/generic.py", line 11585, in stat_func
min_count=min_count,
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/frame.py", line 7935, in _reduce
result = opa.get_result()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/apply.py", line 186, in get_result
return self.apply_standard()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/apply.py", line 292, in apply_standard
self.apply_series_generator()
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/apply.py", line 308, in apply_series_generator
results[i] = self.f(v)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/frame.py", line 7893, in f
return op(x, axis=axis, skipna=skipna, **kwds)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/nanops.py", line 70, in _f
return f(*args, **kwargs)
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/nanops.py", line 495, in nansum
values, skipna, fill_value=0, mask=mask
File "/home/scottgigante/sandbox/lib/python3.7/site-packages/pandas/core/nanops.py", line 309, in _get_values
values = values.copy()
KeyboardInterrupt
```
#### Expected Output
The output should be computed successfully as in the scipy case.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.10-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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} | 14 | 2019-09-17T20:42:48Z | 2020-09-22T13:41:53Z | 2020-09-22T13:41:25Z | CONTRIBUTOR | null | We should be able to test astype to our own type
```diff
diff --git a/pandas/tests/extension/base/casting.py b/pandas/tests/extension/base/casting.py
index 7146443bf8..b33ab93031 100644
--- a/pandas/tests/extension/base/casting.py
+++ b/pandas/tests/extension/base/casting.py
@@ -1,3 +1,5 @@
+import pytest
+
import pandas as pd
from pandas.core.internals import ObjectBlock
@@ -21,3 +23,9 @@ class BaseCastingTests(BaseExtensionTests):
result = pd.Series(data[:5]).astype(str)
expected = pd.Series(data[:5].astype(str))
self.assert_series_equal(result, expected)
+
+ @pytest.mark.parametrize('copy', [True, False])
+ def test_astype_own_type(self, data, copy):
+ result = data.astype(data.dtype, copy=copy)
+ assert (result is data) is (not copy)
+ self.assert_extension_array_equal(result, data)
```
A few tests fail this. Would welcome investigation.
`$ pytest pandas/tests/extension/ -k test_astype_own_type -v --tb=line`
```
========================================================================= FAILURES ==========================================================================
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/base.py:390: TypeError: data type not understood
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/base.py:390: TypeError: data type not understood
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/base.py:390: TypeError: data type not understood
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/base.py:390: TypeError: data type not understood
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
/Users/taugspurger/sandbox/pandas/pandas/tests/extension/base/casting.py:30: AssertionError
``` | {
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} | 17 | 2019-09-18T01:15:41Z | 2019-11-13T01:28:58Z | 2019-11-13T01:28:54Z | CONTRIBUTOR | null | xref #28209, https://github.com/python-sprints/pandas-mentoring/issues/161
Validate that we use
```python
class Foo:
"""
Doc.
"""
pass
```
instead of:
```python
class Bar:
"""
Doc.
"""
pass
```
(note the blank line between `class Bar:` and the docstring. | {
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} | 13 | 2019-09-18T02:23:19Z | 2020-04-21T17:37:25Z | 2020-04-16T23:37:56Z | NONE | null | ## Summary
*pyarrow* can load parquet files directly from S3. So can Dask. *pandas* seems to not be able to. This would be really cool and since you use *pyarrow* underneath it should be easy.
For example in *pyarrow*, even with push-down filters:
```python
stocks_close_ds = ParquetDataset(
'data/v4.parquet',
filters=[('Ticker','=',ticker)]
)
table = stocks_close_ds.read()
stocks_close_df = table.to_pandas()
```
## Code
This code loads field names from an S3 file then loads an S3 directory containing lots of parquet files. Note that I have repeatedly verified that files are there and are publicly accessible. *boto3* can directly access them individually.
```python
# Tags and their indexes were precomputed using PySpark
sorted_all_tags = json.loads(read_from_s3('sorted_all_tags.2000.json', overwrite=True))
max_index = sorted_all_tags[-1][0] + 1
# Pandas can load Parquet data using pyarrow or fastparquet
s3_path = f's3://{BUCKET}/{PATH}/Questions.Stratified.Final.2000.parquet'
posts_df = pd.read_parquet(
s3_path,
columns=['_Body'] + ['label_{}'.format(i) for i in range(0, max_index)],
engine='pyarrow'
)
posts_df.head(2)
```
It throws this exception if I use `s3://bucket/path/file_dir`:
```python
ArrowIOError Traceback (most recent call last)
<ipython-input-6-f37a90c29660> in <module>
5 s3_path,
6 columns=['_Body'] + ['label_{}'.format(i) for i in range(0, max_index)],
----> 7 engine='pyarrow'
8 )
9 posts_df.head(2)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, **kwargs)
294
295 impl = get_engine(engine)
--> 296 return impl.read(path, columns=columns, **kwargs)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/parquet.py in read(self, path, columns, **kwargs)
123 kwargs["use_pandas_metadata"] = True
124 result = self.api.parquet.read_table(
--> 125 path, columns=columns, **kwargs
126 ).to_pandas()
127 if should_close:
~/anaconda3/envs/deep/lib/python3.6/site-packages/pyarrow/parquet.py in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, filesystem, filters)
1212 filesystem=filesystem, filters=filters)
1213 else:
-> 1214 pf = ParquetFile(source, metadata=metadata, memory_map=memory_map)
1215 return pf.read(columns=columns, use_threads=use_threads,
1216 use_pandas_metadata=use_pandas_metadata)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pyarrow/parquet.py in __init__(self, source, metadata, common_metadata, memory_map)
130 memory_map=True):
131 self.reader = ParquetReader()
--> 132 self.reader.open(source, use_memory_map=memory_map, metadata=metadata)
133 self.common_metadata = common_metadata
134 self._nested_paths_by_prefix = self._build_nested_paths()
~/anaconda3/envs/deep/lib/python3.6/site-packages/pyarrow/_parquet.pyx in pyarrow._parquet.ParquetReader.open()
~/anaconda3/envs/deep/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIOError: Invalid Parquet file size is 0 bytes
```
It throws this exception if I use `s3://bucket/path/file_dir/* or *.parquet`:
```python
FileNotFoundError Traceback (most recent call last)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/s3.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
29 try:
---> 30 filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
31 except (compat.FileNotFoundError, NoCredentialsError):
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in open(self, path, mode, block_size, **kwargs)
668 f = self._open(path, mode=mode, block_size=block_size,
--> 669 autocommit=ac, **kwargs)
670 if not ac:
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in _open(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, **kwargs)
302 s3_additional_kwargs=kw, cache_type=cache_type,
--> 303 autocommit=autocommit)
304
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in __init__(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type)
919 super().__init__(s3, path, mode, block_size, autocommit=autocommit,
--> 920 cache_type=cache_type)
921 self.s3 = self.fs # compatibility
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in __init__(self, fs, path, mode, block_size, autocommit, cache_type, **kwargs)
863 if not hasattr(self, 'details'):
--> 864 self.details = fs.info(path)
865 self.size = self.details['size']
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in info(self, path, version_id)
478 raise ValueError('Failed to head path %r: %s' % (path, e))
--> 479 return super().info(path)
480
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in info(self, path, **kwargs)
488 else:
--> 489 raise FileNotFoundError(path)
490
FileNotFoundError: BUCKET/PATH/Questions.Stratified.Final.2000.parquet/*
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
<ipython-input-9-5104e6b4fbad> in <module>
9 s3_path,
10 columns=['_Body'] + ['label_{}'.format(i) for i in range(0, max_index)],
---> 11 engine='pyarrow'
12 )
13 posts_df.head(2)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, **kwargs)
280
281 impl = get_engine(engine)
--> 282 return impl.read(path, columns=columns, **kwargs)
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/parquet.py in read(self, path, columns, **kwargs)
123
124 def read(self, path, columns=None, **kwargs):
--> 125 path, _, _, should_close = get_filepath_or_buffer(path)
126
127 kwargs['use_pandas_metadata'] = True
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/common.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
214 encoding=encoding,
215 compression=compression,
--> 216 mode=mode)
217
218 if is_gcs_url(filepath_or_buffer):
~/anaconda3/envs/deep/lib/python3.6/site-packages/pandas/io/s3.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
37 # for that bucket.
38 fs = s3fs.S3FileSystem(anon=True)
---> 39 filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
40 return filepath_or_buffer, None, compression, True
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in open(self, path, mode, block_size, **kwargs)
667 ac = kwargs.pop('autocommit', not self._intrans)
668 f = self._open(path, mode=mode, block_size=block_size,
--> 669 autocommit=ac, **kwargs)
670 if not ac:
671 self.transaction.files.append(f)
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in _open(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, **kwargs)
301 version_id=version_id, fill_cache=fill_cache,
302 s3_additional_kwargs=kw, cache_type=cache_type,
--> 303 autocommit=autocommit)
304
305 def _lsdir(self, path, refresh=False, max_items=None):
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in __init__(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type)
918 self.s3_additional_kwargs = s3_additional_kwargs or {}
919 super().__init__(s3, path, mode, block_size, autocommit=autocommit,
--> 920 cache_type=cache_type)
921 self.s3 = self.fs # compatibility
922 if self.writable():
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in __init__(self, fs, path, mode, block_size, autocommit, cache_type, **kwargs)
862 if mode == 'rb':
863 if not hasattr(self, 'details'):
--> 864 self.details = fs.info(path)
865 self.size = self.details['size']
866 self.cache = caches[cache_type](self.blocksize, self._fetch_range,
~/anaconda3/envs/deep/lib/python3.6/site-packages/s3fs/core.py in info(self, path, version_id)
477 except ParamValidationError as e:
478 raise ValueError('Failed to head path %r: %s' % (path, e))
--> 479 return super().info(path)
480
481 def ls(self, path, detail=False, refresh=False, **kwargs):
~/anaconda3/envs/deep/lib/python3.6/site-packages/fsspec/spec.py in info(self, path, **kwargs)
487 return {'name': path, 'size': 0, 'type': 'directory'}
488 else:
--> 489 raise FileNotFoundError(path)
490
491 def checksum(self, path):
FileNotFoundError: BUCKET/PATH/Questions.Stratified.Final.2000.parquet/*
```
#### Problem description
It seems that `pd.read_parquet` can't read a directory structured Parquet file from Amazon S3. I've tried a wildcard and it also throws an error. This is a bummer :(
#### Expected Output
Load the data, return a DataFrame.
#### Output of ``pd.show_versions()``
```python
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-62-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.3
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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"description": "Interval... | open | false | null | [] | null | 0 | 2019-09-18T02:47:45Z | 2020-04-27T00:18:55Z | null | CONTRIBUTOR | null | https://github.com/AlexandreDecan/python-intervals
might provide some inspiration about Interval API that we might want to include / emulate
cc @jschendel | {
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- [x] passes `black pandas`
- [x] whatsnew entry
Fixes a bug in `astype_nansafe` where `NaT` was ignored when casting a datetime or timedelta to `int`. I put the test in `pandas/tests/dtypes/test_common.py` since I couldn't find another place where `astype_nansafe` was tested. Also adds various other tests for `astype_nansafe`.
Related: https://github.com/pandas-dev/pandas/pull/28438 | {
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} | 2 | 2019-09-18T03:20:36Z | 2019-09-20T14:14:37Z | 2019-09-20T12:51:24Z | MEMBER | null | and an unrelated excel test cleanup that slipped in somehow | {
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} | 3 | 2019-09-18T06:22:12Z | 2019-09-19T11:55:04Z | 2019-09-18T13:48:44Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- no issue opened
- [ ] tests added / passed
- no, only docs modified
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 6 | 2019-09-18T08:49:01Z | 2021-11-10T09:45:56Z | 2021-11-10T09:45:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# pandas 0.25
sample_data = pd.Series([1, 2])
result = sample_data.sample(frac=0.2)
result.empty
```
#### Problem description
When I sample with a fraction I always expect a result, returning an empty series is unexpected.
I found this issue in an older version of pandas, but it also happens in pandas 0.25
#### Expected Output
```python
Series([1], dtype: int64) # or 2 but not empty
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 40.6.2
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-09-18T09:08:55Z | 2019-09-18T18:11:07Z | 2019-09-18T18:10:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"start": ["2017-01-01 13:09:01", "2017-01-01 13:09:04", "2017-01-01 13:09:12"],
"end": ["2017-01-01 13:09:05", "2017-01-01 13:09:09", "2017-01-01 13:09:14"],
"status": ["OK", "ERROR", "OK"]})
# create an IntervalIndex from start/end
iv_idx = pd.IntervalIndex.from_arrays(pd.to_datetime(df['start']), pd.to_datetime(df['end']), closed='both')
# generate the desired index of individual times
new_idx = pd.date_range(df['start'].min(), df['end'].max(), freq='1s')
# set the index of 'status' as the IntervalIndex, then reindex to the new index
result = df['status'].set_axis(iv_idx, inplace=False).reindex(new_idx)
result
```
#### Problem description
If the interval is overlapping, it throws a key error:
```
pandas/_libs/intervaltree.pxi in pandas._libs.interval.IntervalTree.get_loc()
KeyError: 1483276150000000000
```
#### Expected Output
Something less cryptic ?
Reindexing on an overlapping intervalIndex with a DatetimeIndex should probably be checked by using `is_overlapping`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.138-89.102.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.4
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.3
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.1.5
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-09-18T14:19:29Z | 2019-09-19T09:58:03Z | 2019-09-19T05:17:22Z | CONTRIBUTOR | null | Added special character use case in the existing function "test_filename_with_special_chars"
- [x] closes #26074
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 6 | 2019-09-18T14:33:27Z | 2019-11-14T16:53:46Z | 2019-11-14T16:53:46Z | MEMBER | null | This adds the governance document to the new website.
The rest of the information in the governance repo should already be in the website, so we shouldn't need that repo anymore.
Advantages I see on having this in the website:
- Users can see the governance information from the website, with the same look and feel as the rest, and not navigating to an external github repo
- Simpler (one extra markdown file in the website vs a whole github repo)
- No repetitive information to maintain. The list of maintainers, committees... is now in `web/pandas/config.yml` and can be used in any of the pages of the website. So, in case of changes there will be a single place where to change things.
- I think having it in the main pandas repo will make it easier to keep things updated. There are currently several outdated links in the governance repo (mainly pointing to the repo in `pydata/pandas` instead of `pandas-dev/pandas`).
@jorisvandenbossche you said you saw advantages on keeping the information in a separate github repo, can you comment here about them please. | {
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} | 7 | 2019-09-18T15:11:49Z | 2019-11-11T07:10:53Z | 2019-11-11T07:10:53Z | NONE | null | Firstimer, let me know of any changes need to be done
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-09-18T15:24:02Z | 2020-11-20T14:39:29Z | 2020-11-20T02:51:29Z | MEMBER | null | Run against master:
```python
In [13]: pd.read_json("[null]", dtype=True)
Out[13]:
0
0 NaN
In [14]: pd.read_json("[null]", dtype=False)
Out[14]:
0
0 None
```
I think the second above is an issue - should probably return `np.nan` instead of `None` | {
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} | 2 | 2019-09-18T15:38:03Z | 2019-09-18T16:27:14Z | 2019-09-18T16:27:03Z | MEMBER | null | Follow up to #27838 enabled by #28444
I created a new test specific to the handling of DTA with `dtype=X` to provide a more logical separation of concerns | {
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] | closed | false | null | [] | null | 6 | 2019-09-18T17:14:51Z | 2019-09-18T22:15:18Z | 2019-09-18T22:14:37Z | NONE | null | I am migrating my `SparseDataFrame`s to `DataFrames` with sparse columns. I can no longer transpose this DataFrame and have it stay sparse. The behavior I was expecting was to have a fully sparse DataFrame maintain sparsity when transposed.
```python
>>> arrs = scipy.sparse.coo_matrix(np.random.randint(2,size=(10000,1000)))
>>> pd.DataFrame.sparse.from_spmatrix(arrs).dtypes
0 Sparse[int64, 0]
...
999 Sparse[int64, 0]
>>> pd.DataFrame.sparse.from_spmatrix(arrs.transpose()).dtypes
0 Sparse[int64, 0]
...
9999 Sparse[int64, 0]
>>> pd.DataFrame.sparse.from_spmatrix(arrs).transpose().dtypes
0 int64
...
9999 int64
>>> pd.SparseDataFrame(arrs).transpose().dtypes
0 Sparse[int64, nan]
...
9999 Sparse[int64, nan]
```
> INSTALLED VERSIONS
> ------------------
> commit : None
> python : 3.6.8.final.0
> python-bits : 64
> OS : Linux
>
> pandas : 0.25.1
> numpy : 1.17.1
> scipy : 1.3.1 | {
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} | 1 | 2019-09-18T17:33:49Z | 2019-09-20T14:25:15Z | 2019-09-20T12:34:28Z | MEMBER | null | The bug part of this is that "dateutil/something_invalid" fails to raise on master because dateutil.tz.gettz("something_invalid") returns None instead of raising. | {
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"description": "pandas t... | open | false | null | [] | null | 1 | 2019-09-18T18:54:50Z | 2020-05-22T03:37:02Z | null | MEMBER | null | @jbrockmendel the use case I was trying to explain in words is this (which currently works):
```
In [4]: df1 = pd.DataFrame({'a': DecimalArray(make_data()[:5]), 'b': DecimalArray(make_data()[:5])})
In [5]: df1
Out[5]:
a b
0 Decimal: 0.12461374221703280795736645814031362... Decimal: 0.52335889201915997137604108502273447...
1 Decimal: 0.31309497234009509014640570967458188... Decimal: 0.00133572791466307627672449598321691...
2 Decimal: 0.84547604466523051947035582998069003... Decimal: 0.73944523257987115893996588056324981...
3 Decimal: 0.78411203581007593577112402272177860... Decimal: 0.60960149820278708432397252181544899...
4 Decimal: 0.73864636517230020107405152884894050... Decimal: 0.53212511863152778257557429242297075...
In [6]: df1.dtypes
Out[6]:
a decimal
b decimal
dtype: object
In [7]: df2 = pd.DataFrame({'a': np.arange(5), 'b': np.arange(5)})
In [8]: df1 + df2
Out[8]:
a b
0 Decimal: 0.1246137422170328079573664581 Decimal: 0.5233588920191599713760410850
1 Decimal: 1.313094972340095090146405710 Decimal: 1.001335727914663076276724496
2 Decimal: 2.845476044665230519470355830 Decimal: 2.739445232579871158939965881
3 Decimal: 3.784112035810075935771124023 Decimal: 3.609601498202787084323972522
4 Decimal: 4.738646365172300201074051529 Decimal: 4.532125118631527782575574292
In [9]: (df1 + df2).dtypes
Out[9]:
a decimal
b decimal
dtype: object
```
As you mentioned, in those case the ops code would keep doing that column by column (or 1D block by 1D block) and the right integer 2D block will be splitted.
But I thought you mentioned something about such a case currently not being covered by the tests? (in which case it would be good to add some) | {
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} | 2 | 2019-09-18T19:05:05Z | 2020-10-04T04:47:47Z | 2020-10-04T04:47:47Z | MEMBER | null | `Timestamp` comparisons raise TypeError in some cases where `datetime`s do not:
```
ts = pd.Timestamp.now()
ts2 = ts.tz_localize("UTC")
dt = ts.to_pydatetime()
dt2 = ts2.to_pydatetime()
>>> ts == ts2 # <-- TypeError
>>> dt == dt2
False
```
The datetime comparison _will_ raise for inequalities, just not `==` or `!=`.
Changing this behavior would be an API change, but it would likely simplify a bunch of headaches (including a bug in array_equivalent) | {
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} | 2 | 2019-09-18T19:18:34Z | 2019-09-20T14:15:56Z | 2019-09-20T12:38:23Z | MEMBER | null | Some of this can be simplified if we change the behavior discussed in #28507. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28509 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28509/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28509/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28509/events | https://github.com/pandas-dev/pandas/pull/28509 | 495,414,708 | MDExOlB1bGxSZXF1ZXN0MzE4OTY2OTE3 | 28,509 | [WIP, ENH] Adds cumulative methods to ea | {
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"c... | closed | false | null | [] | null | 63 | 2019-09-18T19:32:41Z | 2021-10-04T00:13:12Z | 2021-10-04T00:13:12Z | CONTRIBUTOR | null | - [x] closes #28385
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Problem specific
- [X] Add abstract tests similar to pandas/tests/extension/base/reduce.py
- [X] Add TestFixtures for Accumulations
- [x] Implement Accumulation for IntegerArray
- [ ] Implement Accumulation for DecimalArray - out of scope
- [ ] Implement Accumulation for DatetileLikeArrayMixin ? - out of scope
- [x] Use BaseNoAccumulateTest where applicable - implemented this for categorical | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28510 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28510/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28510/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28510/events | https://github.com/pandas-dev/pandas/pull/28510 | 495,453,355 | MDExOlB1bGxSZXF1ZXN0MzE4OTk4ODg5 | 28,510 | Re-implemented parametrization of test_frame_from_json_to_json | {
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"d... | closed | false | null | [] | null | 2 | 2019-09-18T21:02:02Z | 2019-09-20T18:06:29Z | 2019-09-20T17:53:30Z | MEMBER | null | This piece was broken off of #27838 as it made the diff much larger, so hopefully easier to digest on its own.
As is, parametrization here has brought up a lot of rough edges which are responsible for some of the complexity. These are noted with TODOs and summarized as follows (save Py35 issues, which aren't worth addressing at this point):
- Frame order is not maintained when `numpy=False` (default) and `orient="index"`
- On windows or 32 bit platforms it appears that `np.int64` roundtrips as `np.int32` (maybe not an issue?)
- `orient="split"` does not preserve strings in the index if those strings are numeric, though it should be able to
- `convert_axes` may have surprising behavior when dealing with empty DataFrames
- DTI seem to roundtrip as strings when written with epoch format for all but `orient="split"
Not all of these are the same priority, but figure worth leaving as follow ups | {
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} | 28 | 2019-09-18T21:06:07Z | 2020-01-01T21:00:02Z | 2020-01-01T21:00:01Z | NONE | null | - [x] closes #27435
- [ ] tests added / passed
- [ ] passes `black pandas` (not applicable, `black setup.py` changes too many quotes, polluting the diff).
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @jorisvandenbossche | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28512/events | https://github.com/pandas-dev/pandas/issues/28512 | 495,481,523 | MDU6SXNzdWU0OTU0ODE1MjM= | 28,512 | DEPR: Deprecate numpy argument in read_json | {
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} | 6 | 2019-09-18T22:18:24Z | 2020-01-09T03:25:48Z | 2020-01-09T03:25:48Z | MEMBER | null | I've never really been clear on the purpose of the `numpy` argument in to_json. Some digging brought me here https://github.com/pandas-dev/pandas/pull/3876#issuecomment-19390383 where it is explained that this maintains some kind of sequence to elements. To illustrate the only difference I could find
```python
>>> pd.read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]',numpy=False, orient='records')
a b
0 1 2
1 1 2
>>> pd.read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]',numpy=True, orient='records')
a b
0 1 2
1 2 1
```
I might be missing the point but I don't understand why this would be useful. Objects in JSON are by definition not ordered, so this is non-compliant and I think just plain confusing.
So I think good to deprecate unless anyone has objections. | {
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