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https://api.github.com/repos/pandas-dev/pandas/issues/28813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28813/events | https://github.com/pandas-dev/pandas/issues/28813 | 503,298,543 | MDU6SXNzdWU1MDMyOTg1NDM= | 28,813 | Diff on sparse dtype | {
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} | 2 | 2019-10-07T08:17:59Z | 2020-05-22T09:58:31Z | 2020-05-22T09:58:31Z | NONE | null | Correct behaviour without Sparse[int]:
```python
a = [[0, 1], [1, 0]]
print(pd.DataFrame(a).diff())
```
yields:
```
0 1
0 NaN NaN
1 1.0 -1.0
```
Current behaviour (doesn't work):
```python
a = [[0, 1], [1, 0]]
print(pd.DataFrame(a, dtype='Sparse[int]').diff())
```
yields:
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-34-3cc99aac7c69> in <module>
3 print(pd.DataFrame(a).diff())
4
----> 5 print(pd.DataFrame(a, dtype='Sparse[int]').diff())
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in diff(self, periods, axis)
6592 """
6593 bm_axis = self._get_block_manager_axis(axis)
-> 6594 new_data = self._data.diff(n=periods, axis=bm_axis)
6595 return self._constructor(new_data)
6596
~/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py in diff(self, **kwargs)
564
565 def diff(self, **kwargs):
--> 566 return self.apply("diff", **kwargs)
567
568 def interpolate(self, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436 kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438 applied = getattr(b, f)(**kwargs)
439 result_blocks = _extend_blocks(applied, result_blocks)
440
~/.local/lib/python3.6/site-packages/pandas/core/internals/blocks.py in diff(self, n, axis)
1328 def diff(self, n, axis=1):
1329 """ return block for the diff of the values """
-> 1330 new_values = algos.diff(self.values, n, axis=axis)
1331 return [self.make_block(values=new_values)]
1332
~/.local/lib/python3.6/site-packages/pandas/core/algorithms.py in diff(arr, n, axis)
1943
1944 na_indexer = [slice(None)] * arr.ndim
-> 1945 na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
1946 out_arr[tuple(na_indexer)] = na
1947
IndexError: list assignment index out of range``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28814/events | https://github.com/pandas-dev/pandas/issues/28814 | 503,298,832 | MDU6SXNzdWU1MDMyOTg4MzI= | 28,814 | infer_dtype() function slower in latest version | {
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} | 2 | 2019-10-07T08:18:40Z | 2019-12-23T17:02:23Z | 2019-12-23T17:02:23Z | NONE | null | #### Code Sample
```python
from datetime import datetime
import numpy as np
import pandas as pd
from pandas.api.types import infer_dtype
print(pd.__version__)
RUN_COUNT = 5
df = pd.DataFrame(np.ones((100000, 1000)))
avg_times = []
for _ in range(RUN_COUNT):
start = datetime.now()
for col in df.columns:
infer_dtype(df[col], skipna=True)
avg_times.append(datetime.now() - start)
print('Average time: ', np.mean(avg_times))
```
#### Problem description
When I run the above code on my machine, there is major difference in the performance between different versions. I ran the code using new conda environments created with Python 3.7 and the specific version of Pandas. The table below shows the execution times.
| Pandas Version | Execution time |
| ------------- |:-------------:|
| 0.23.4 | 0:00:00.013999 |
| 0.25.1 | 0:00:15.623905 |
The example code is only an example. In the most cases I need this function to get more specific type of the column than the type returned by the .dtype parameter (usually when working with mixed data).
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https://api.github.com/repos/pandas-dev/pandas/issues/28815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28815/events | https://github.com/pandas-dev/pandas/pull/28815 | 503,310,459 | MDExOlB1bGxSZXF1ZXN0MzI1MTg4OTA0 | 28,815 | DOC: Fixes for SA04 errors in docstrings | {
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] | closed | false | null | [] | null | 23 | 2019-10-07T08:43:32Z | 2019-12-29T13:59:48Z | 2019-12-29T13:59:48Z | NONE | null | - [x] Fixes for #28792
- [x] tested through ./scripts/validate_docstrings.py | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28816/events | https://github.com/pandas-dev/pandas/issues/28816 | 503,336,665 | MDU6SXNzdWU1MDMzMzY2NjU= | 28,816 | .at not setting value | {
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```python
# ff= 20
# df['B'] = 0
# df.at[4, 'B'] = -ff/100
# df.at[4, 'B']
#0
```
#### Problem description
the function Dataframe.at is not setting the value if passed as variable, instead if it's passed as number, it works
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
python : 3.5.6.final.0
pandas : 0.25.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28817/events | https://github.com/pandas-dev/pandas/issues/28817 | 503,355,943 | MDU6SXNzdWU1MDMzNTU5NDM= | 28,817 | Faster SQL implementation in d6stack library | {
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"default"... | closed | false | null | [] | null | 7 | 2019-10-07T10:12:30Z | 2021-07-21T05:25:28Z | 2021-07-21T05:25:28Z | NONE | null | #### Code Sample
https://github.com/d6t/d6tstack/blob/master/examples-sql.ipynb
#### Problem description
In the example it currently takes 28s to upload data to an SQL database. With the d6stack implementation it only takes 4.7s with postgres and 7.1s with mysql which is 4 to 6 times faster than standard pandas.
#### Expected Output
Improved speed uploading data to an SQL database right out of the box. | {
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} | 6 | 2019-10-07T11:57:43Z | 2019-10-26T06:50:25Z | 2019-10-07T15:33:45Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.read_csv(f,sep='\t')
```
#### Problem description
Not so much a problem as a feature possibility - when reading without the encoding= parameter, the default can be made a bit smarter; since utf-16 seems to generally start with unique characters 0xFF 0xFE hex , if those chars start a file then it should be read as utf-16 (either after reading as utf-8 fails or as a first option)
I am not so familiar with the pandas code and will try to implement this if someone can point me to where these changes would go - the [pandas.io.parsers](https://github.com/pandas-dev/pandas/blob/master/pandas/io/parsers.py) is a bit opaque for me.
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https://api.github.com/repos/pandas-dev/pandas/issues/28819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28819/events | https://github.com/pandas-dev/pandas/issues/28819 | 503,441,086 | MDU6SXNzdWU1MDM0NDEwODY= | 28,819 | DOC: fix code-block in the reshaping docs | {
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} | 2 | 2019-10-07T13:07:42Z | 2019-10-16T16:24:58Z | 2019-10-16T16:24:58Z | MEMBER | null | The code block at this location:
https://dev.pandas.io/docs/user_guide/reshaping.html#pivoting-with-single-aggregations
is not rendered correctly, as we accidentally try to render it as a note. The `.. note::` should be replaced with `.. code-block::` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28820/events | https://github.com/pandas-dev/pandas/pull/28820 | 503,465,148 | MDExOlB1bGxSZXF1ZXN0MzI1MzExODg4 | 28,820 | DOC: fix code-block in the reshaping docs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28821/events | https://github.com/pandas-dev/pandas/issues/28821 | 503,481,496 | MDU6SXNzdWU1MDM0ODE0OTY= | 28,821 | Call .max method on two timezone datetime columns results in nan | {
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} | [] | closed | false | null | [] | null | 1 | 2019-10-07T14:16:23Z | 2019-10-07T15:12:31Z | 2019-10-07T15:12:20Z | NONE | null | #### Code Sample, a copy-pastable example if possible
This works when comparing two non-timezone datetime columns
```python
a = pd.DataFrame(
{
"a": [pd.Timestamp("20180101"), np.nan],
"b": [pd.Timestamp("20190101"), pd.Timestamp("20190101")],
}
)
a.max(axis=1)
```
Output:
0 2019-01-01
1 2019-01-01
dtype: datetime64[ns]
But it fails on time-zone datetime columns
```python
a = pd.DataFrame(
{
"a": [pd.Timestamp("20180101", tz="utc"), np.nan],
"b": [pd.Timestamp("20190101", tz="utc"), pd.Timestamp("20190101", tz="utc")],
}
)
a.max(axis=1)
```
Output:
0 NaN
1 NaN
dtype: float64
#### Problem description
I'm expecting the .max function to work consistently on both non-timezone and timezone datetime columns. But as the above results shows, somehow the comparison of timezone datetime is adjusted to float and the results are all nan.
#### Expected Output
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: 0.7.4
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
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
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https://api.github.com/repos/pandas-dev/pandas/issues/28822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28822/events | https://github.com/pandas-dev/pandas/issues/28822 | 503,527,135 | MDU6SXNzdWU1MDM1MjcxMzU= | 28,822 | validate_docstrings.py as a separate package? | {
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} | [] | closed | false | null | [] | null | 5 | 2019-10-07T15:28:43Z | 2019-10-25T16:39:01Z | 2019-10-25T16:39:01Z | CONTRIBUTOR | null | The docstring validation script is quite nice! I would like to use it for instance to validate docsting in scikit-learn, e.g.
```
python pandas/scripts/validate_docstrings.py sklearn.linear_model.LinearRegression
```
works, but then is a bit more difficult to customize being pandas specific.
In particular it would be nice to have the ability to specify which error to include / exclude in some config file. Also for instance scikit-learn has some default configuration for doctests that is currently ignored in `setup.cfg`,
```
[tool:pytest]
doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
```
Overall, it would be nice if this script was a separate project with a shared core functionality and possibly some config that that projects could personalize a bit if needed (although going with the 'black' approach with one set of rules for all is also a possibility).
Docstring validation is a problem that is relevant for a number of scientific python projects and it would be a shame if each one solves it independently. As far as I can tell, so far this script provides more relevant errors for scientific python projects that say `pydocstyle`. Using the same tool for validation, would also help making docstrings formatting more consistent across projects.
Also this script is bound to grow in scope (e.g. https://github.com/pandas-dev/pandas/issues/28701) and eventually sharing the maintenance with maintainers of other projects might be useful. Although I agree that it might add some overhead with respect to your CI integration. | {
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} | 4 | 2019-10-07T16:16:12Z | 2020-06-23T17:37:58Z | 2020-06-23T17:37:58Z | CONTRIBUTOR | null | I propose adding a `bias` parameter in [`DataFrame.cov()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cov.html). Currently, Pandas v0.25.1 does not have such a parameter.
The default would be `bias=False`, which would compute sample covariance with a `N-1` factor in the denominator. When `bias=True`, this would compute population covariance with `N` in the denominator.
This follows the same semantics as the [`numpy.cov()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.cov.html) function. Since `DataFrame.cov()` uses `numpy.cov()`, I hope this would not be too hard to implement. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28824/events | https://github.com/pandas-dev/pandas/pull/28824 | 503,609,272 | MDExOlB1bGxSZXF1ZXN0MzI1NDI4MjE1 | 28,824 | TST: Update test in #28798 to use a Categorical | {
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"d... | closed | false | null | [] | null | 1 | 2019-10-07T18:13:09Z | 2019-10-08T06:23:15Z | 2019-10-08T06:23:10Z | CONTRIBUTOR | null | - [x] xref #28798
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I merged #28798 too fast, as the column "Name" wasn't a Categorical, which was to be tested. I've made it a Categorical and for good measure I've added ``ordered_fixture`` to the test. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28825/events | https://github.com/pandas-dev/pandas/issues/28825 | 503,655,240 | MDU6SXNzdWU1MDM2NTUyNDA= | 28,825 | UnsupportedOperation 'seek' when loading excel files from urllib3.response.HTTPResponse object | {
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"color": ... | closed | false | null | [] | null | 1 | 2019-10-07T19:51:03Z | 2019-10-18T09:44:17Z | 2019-10-18T09:44:16Z | NONE | null | 0.25.1 seems to have reintroduced #20434
**Update:** not the case, but the same problem happens when feeding `excel_read()` with an [urllib3.response.HTTPResponse](https://urllib3.readthedocs.io/en/latest/reference/#urllib3.response.HTTPResponse) object. Code sample updated.
#### Code Sample, a copy-pastable example if possible
```
import pandas
import requests
url = "https://raw.github.com/pandas-dev/pandas/master/pandas/tests/io/data/test1.xls"
r = requests.get(url, stream=True)
df = pandas.read_excel(r.raw)
print(df)
```
#### Problem description
Using `0.25.1` I get the same problem and output as described in #20434 with the code above.
#### Expected Output
See #20434
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
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 : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28826/events | https://github.com/pandas-dev/pandas/pull/28826 | 503,655,638 | MDExOlB1bGxSZXF1ZXN0MzI1NDY1Njc5 | 28,826 | BUG: Patch for skipping seek() when loading Excel files from urllib3.response.HTTPResponse object | {
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} | 12 | 2019-10-07T19:51:44Z | 2019-10-11T14:38:25Z | 2019-10-11T14:38:25Z | NONE | null | - [ x] closes #28825
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Discovered that 0.25.x reintroduces #20437. Proposing the same fix here. | {
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"description": "Apply, Aggregate, Tra... | closed | false | null | [] | null | 4 | 2019-10-07T19:52:34Z | 2019-10-08T20:56:32Z | 2019-10-08T20:56:32Z | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'a':list('abcdef')})
L = []
def fcn(x):
global L
L.append(x)
df.a.apply(fcn)
print(L)
# ['a', 'b', 'c', 'd', 'e', 'f']
L=[]
df.apply(lambda x: fcn(x.a), axis=1)
print(L)
# ['a', 'b', 'c', 'd', 'e', 'f']
```
#### Problem description
According to the current `pd.DataFrame.apply()` docs: "In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row."
#### Expected Output
```
['a', 'a', 'b', 'c', 'd', 'e', 'f']
['a', 'a', 'b', 'c', 'd', 'e', 'f']
```
#### Output of ``pd.show_versions()``
<details>
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: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: 0.11.1
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: None
html5lib: None
sqlalchemy: 1.2.5
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 2 | 2019-10-07T22:12:23Z | 2019-10-08T12:58:09Z | 2019-10-08T12:44:05Z | MEMBER | null | mypy error: `pandas\core\generic.py:5902: error: "append" of "list" does not return a value`
This does not actually manifest as a bug with the current implementation since `pd.concat` ignores `None`.
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'0.26.0.dev0+498.gaf498fe8f'
>>>
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>>
>>> df = pd.DataFrame(data=d)
>>>
>>> df
col1 col2
0 1 3
1 2 4
>>>
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object
>>>
>>> results = []
>>> results.append(df['col1'].astype('int32'))
>>> results.append(results.append(df['col2']))
>>> results
[0 1
1 2
Name: col1, dtype: int32, 0 3
1 4
Name: col2, dtype: int64, None]
>>>
>>> pd.concat(results, axis=1)
col1 col2
0 1 3
1 2 4
>>>
```
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attrs 19.1 -> 19.2
https://github.com/pytest-dev/pytest/issues/5903
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] | closed | false | null | [] | null | 10 | 2019-10-07T23:31:53Z | 2019-10-08T23:31:32Z | 2019-10-08T23:31:32Z | NONE | null | Pandas has growing number of type annotations but these are not properly advertised to type checking tools. According to [PEP 561](https://www.python.org/dev/peps/pep-0561/) packages providing annotations should contain `py.typed` files in corresponding packages.
`py.typed` is applied recursively, if placed in the root, and can accept missing annotations, if contains `partial\n`.
This PR adds such file and includes it in `package_data`.
- [x] closes #28142
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-10-08T02:10:39Z | 2019-10-08T14:25:48Z | 2019-10-08T12:11:04Z | MEMBER | null | I think this finishes the porting of the tests commented "placeholder due to too many xfails". going to plow through existing (and newly added) xfails in separate branch(es) | {
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} | 3 | 2019-10-08T03:01:42Z | 2019-10-08T21:24:12Z | 2019-10-08T21:21:50Z | MEMBER | null | Vague gameplan: I think I've got a handle on the `if not boxed` xfail tests that are left, will incrementally fix them over a few more steps. Then the `if boxed` cases all correspond to passing `ndarray` for `fill_value`, which never happens in the tests except for dedicated tests designed to do exactly that. If we can rule that case out, we can remove most of the remaining xfails (and a lot of complexity in the test code) in one swoop. | {
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} | 8 | 2019-10-08T05:26:25Z | 2019-11-19T15:53:23Z | 2019-11-16T21:35:56Z | CONTRIBUTOR | null | - [x] closes #28769
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/28835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28835/events | https://github.com/pandas-dev/pandas/pull/28835 | 503,836,888 | MDExOlB1bGxSZXF1ZXN0MzI1NjA4NDIx | 28,835 | Remove NDFrameGroupBy Class | {
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} | 5 | 2019-10-08T05:35:22Z | 2020-01-16T00:33:51Z | 2019-10-08T12:33:47Z | MEMBER | null | Was going to do a larger refactor but this diff looks more confusing than it actually is, so figured I'd stop here for readability. All I've done in this PR is:
- Remove NDFrameGroupBy, consolidating it's methods into DataFrameGroupBy
- Merge NDFrameGroupBy.aggregate into DataFrameGroupBy.aggregate (the latter previously called the former)
After this I am looking to find a more logical home for the functions, as the current hierarchy isn't super clear. For example, `_iterate_slices()` should probably be abstract in the base `GroupBy` class, but right now the Series implementation is in the superclass while DataFrameGroupBy overrides that for itself. Series.pct_change is practically the same as GroupBy.pct_change, so is probably unnecessary. Will be a few more follow ups mixed in | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28836/events | https://github.com/pandas-dev/pandas/issues/28836 | 503,856,881 | MDU6SXNzdWU1MDM4NTY4ODE= | 28,836 | NameError: name 'tempita' is not defined when building extensions with old cython | {
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} | 10 | 2019-10-08T06:35:39Z | 2019-12-27T14:41:16Z | 2019-12-27T14:41:05Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
pietro@debiousci:~/nobackup/repo/pandas_temp$ python3 setup.py build_ext --inplace
Traceback (most recent call last):
File "setup.py", line 813, in <module>
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "setup.py", line 541, in maybe_cythonize
build_ext.render_templates(_pxifiles)
File "setup.py", line 127, in render_templates
pyxcontent = tempita.sub(tmpl)
NameError: name 'tempita' is not defined
```
#### Problem description
If cython is too old/missing, the nice error reporting of the setup.py do not work.
Until few minutes ago I had never heard about ``tempita``, but my understanding is that it is required (used in ``build_ext.render_templates``) while ``cython`` is not:
https://github.com/pandas-dev/pandas/blob/bee17d5f4c99e6d1e451cf9a7b6a1780aa25988d/setup.py#L74
Hence, whether the check for ``tempita`` is made should not depend on whether ``cython`` is found: instead, this is what currently happens, given that ``tempita`` is checked in the ``else`` clause of the ``try...except`` check of ``cython``:
https://github.com/pandas-dev/pandas/blob/bee17d5f4c99e6d1e451cf9a7b6a1780aa25988d/setup.py#L76
Notice that this means the above error pops out despite ``tempita`` being installed in my system.
#### Expected Output
None
#### Output of ``pd.show_versions()``
<details>
Commit: bee17d5f4c99e6d1e451cf9a7b6a1780aa25988d
</details>
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} | 5 | 2019-10-08T08:18:09Z | 2019-10-29T06:02:28Z | 2019-10-29T06:02:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
>>> df = pd.DataFrame.from_dict({"date": [1485264372711, 1485265925110, 1540215845888, 1540282121025]})
>>> df["date_dt"] = pd.to_datetime(df["date"], unit='ms', cache=True)
>>> df
date date_dt
0 1485264372711 2017-01-24 13:26:12.711
1 1485265925110 2017-01-24 13:52:05.110
2 1540215845888 2018-10-22 13:44:05.888
3 1540282121025 2018-10-23 08:08:41.025
>>> df.loc[:, "date_dt_cp"] = df.loc[:, "date_dt"]
>>> df
date date_dt date_dt_cp
0 1485264372711 2017-01-24 13:26:12.711 2017-01-24 13:26:12.711
1 1485265925110 2017-01-24 13:52:05.110 2017-01-24 13:52:05.110
2 1540215845888 2018-10-22 13:44:05.888 2018-10-22 13:44:05.888
3 1540282121025 2018-10-23 08:08:41.025 2018-10-23 08:08:41.025
>>> df.loc[[2,3], "date_dt_cp"] = df.loc[[2,3], "date_dt"]
>>> df
date date_dt date_dt_cp
0 1485264372711 2017-01-24 13:26:12.711 2017-01-24 13:26:12.711000064
1 1485265925110 2017-01-24 13:52:05.110 2017-01-24 13:52:05.110000128
2 1540215845888 2018-10-22 13:44:05.888 2018-10-22 13:44:05.888000000
3 1540282121025 2018-10-23 08:08:41.025 2018-10-23 08:08:41.024999936
```
#### Problem description
Using `.loc[]` on datetime columns to assign values modifies the dates.
When `.loc[]` is used on all the lines (`df.loc[:, "date_dt_cp"] = df.loc[:, "date_dt"]`) the dates are unchanged
```python
>>> df
date date_dt date_dt_cp
0 1485264372711 2017-01-24 13:26:12.711 2017-01-24 13:26:12.711
1 1485265925110 2017-01-24 13:52:05.110 2017-01-24 13:52:05.110
2 1540215845888 2018-10-22 13:44:05.888 2018-10-22 13:44:05.888
3 1540282121025 2018-10-23 08:08:41.025 2018-10-23 08:08:41.025
```
but when selecting only a subset of lines (`df.loc[[2,3], "date_dt_cp"] = df.loc[[2,3], "date_dt"]`), the values of the dates are changed:
```python
>>> df
date date_dt date_dt_cp
0 1485264372711 2017-01-24 13:26:12.711 2017-01-24 13:26:12.711000064
1 1485265925110 2017-01-24 13:52:05.110 2017-01-24 13:52:05.110000128
2 1540215845888 2018-10-22 13:44:05.888 2018-10-22 13:44:05.888000000
3 1540282121025 2018-10-23 08:08:41.025 2018-10-23 08:08:41.024999936
```
#### Expected Output
The last assignment in the example above shouldn't update the values:
```python
date date_dt date_dt_cp
0 1485264372711 2017-01-24 13:26:12.711 2017-01-24 13:26:12.711
1 1485265925110 2017-01-24 13:52:05.110 2017-01-24 13:52:05.110
2 1540215845888 2018-10-22 13:44:05.888 2018-10-22 13:44:05.888
3 1540282121025 2018-10-23 08:08:41.025 2018-10-23 08:08:41.025
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 3.7.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.0
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : 1.3.0
xlsxwriter : None
</details>
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} | 9 | 2019-10-08T08:38:48Z | 2020-01-31T07:39:17Z | 2019-10-16T16:24:58Z | CONTRIBUTOR | null | - [x] closes #28819
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https://api.github.com/repos/pandas-dev/pandas/issues/28839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28839/events | https://github.com/pandas-dev/pandas/issues/28839 | 503,956,960 | MDU6SXNzdWU1MDM5NTY5NjA= | 28,839 | BUG: DataFrame.equals() wrongly returns True in case of identical blocks with different locations | {
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} | 8 | 2019-10-08T10:18:37Z | 2019-11-19T13:31:13Z | 2019-11-19T13:31:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
version: 3.6.8
# Your code here
df3 = pd.DataFrame({'a': [1, 2], 'b': ['s', 'd']})
df4 = pd.DataFrame({'a': ['s', 'd'], 'b': [1, 2]})
df3.equals(df4)
```
#### Problem description
When I read the source code, I did a simple test on it, and then failed.
#### Expected Output
I expected it return False
#### Output of ``pd.show_versions()``
<details>
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 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.2
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.4
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28840 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28840/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28840/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28840/events | https://github.com/pandas-dev/pandas/issues/28840 | 504,056,494 | MDU6SXNzdWU1MDQwNTY0OTQ= | 28,840 | BUG: EAs with list-like values fail in to_csv (due to to_native_types) | {
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} | 0 | 2019-10-08T13:49:35Z | 2019-10-14T23:33:48Z | 2019-10-14T23:33:48Z | MEMBER | null | From a report at GeoPandas: https://github.com/geopandas/geopandas/issues/1145
Explanation: assume you have an ExtensionArray with list-like values (eg with an object numpy array with lists under the hood). When calling, `to_csv`, which goes through `Block.to_native_types`, we convert this to a numpy array and call `astype(str)`:
https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/core/internals/blocks.py#L699
But, in numpy, if you have a numpy array with list like values, doing `astype(str)` does not work:
```
In [2]: a = np.array([[1, 2], [3]], dtype=object)
In [3]: a.astype(str)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-db38302b5f9a> in <module>
----> 1 a.astype(str)
ValueError: setting an array element with a sequence
```
This could be considered a numpy issue, but, we should also avoid converting to a numpy array while we can directly call `astype` on the ExtensionArray. | {
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} | 17 | 2019-10-08T13:56:19Z | 2019-10-15T02:38:40Z | 2019-10-14T23:33:48Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/28840
This implements a `ExtensionBlock.to_native_types` to override the base class which converts to a numpy array.
To test this is a bit tricky. The current JSONArray we have in the extension tests does not have this issue. I would need to implement and add tests for a new ListArray to check this (I can ensure that this fixes the geopandas issue) | {
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} | 1 | 2019-10-08T14:02:46Z | 2019-10-08T15:13:24Z | null | CONTRIBUTOR | null | Right now our 32-bit build only happens on a Linux VM in azure pipelines. Anaconda no longer builds 32-bit linux packages, and conda-forge never built 32-bit packages. So that build is likely to cause problems over time.
We should migrate to either
1. Use a 32-bit windows build, which Anaconda still builds packages for.
2. Use a non-conda environment on 32-bit linux VM.
1 is probably preferable since it aligns with the rest of our CI infra. I don't know if Azure provides 32-bit images (last I checked the didn't). IIRC, you can trick conda into installing 32-bit packages on a 64-bit machine, but I'm not sure how robust that is. | {
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} | 13 | 2019-10-08T14:20:16Z | 2019-10-11T12:41:20Z | 2019-10-11T12:41:20Z | NONE | null | #### Problem description
`pd.io.formats.format._binify` defines `line_width` as `Union[np.int32, int]`:
https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/io/formats/format.py#L1893
however it is inconsistent with `DataFrameFormatter.__init__`
https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/io/formats/format.py#L518
and `DataFrameFormatter._join_multiline`
https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/io/formats/format.py#L871
which take and pass as-is `Optional[int]`.
This leads to Mypy error:
```
pandas/io/formats/format.py:871: error: Argument 2 to "_binify" has incompatible type "Optional[int]"; expected "Union[int32, int]"
```
Providing default in [`_join_multiline`](https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/io/formats/format.py#L860), i.e.
```python
lwidth = self.line_width or DEFAULT_LINE_WIDTH
```
could be reasonable fix. One could also ignore [the call](https://github.com/pandas-dev/pandas/blob/39602e7d7c5b663696f5a6eca9e66e65483bc868/pandas/io/formats/format.py#L871):
```python
col_bins = _binify(col_widths, lwidth) # type: ignore
```
but that leaves possible correctness problem.
#### Expected Output
Passing Mypy type check
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : bee17d5f4c99e6d1e451cf9a7b6a1780aa25988d
python : 3.7.3.final.0
...
```
Additionally
```
mypy==0.730
```
CC @WillAyd | {
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} | 7 | 2019-10-08T14:54:12Z | 2019-10-08T21:00:23Z | 2019-10-08T21:00:23Z | CONTRIBUTOR | null | This fixes a bunch of spurious errors when running the test suite on pypy3.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] closes [#28738](https://github.com/pandas-dev/pandas/issues/28738)
- [x] passes `black pandas`
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1) Are there other common usages besides "XXX" that I'm not aware of?
2) Should we standardize on some usage patterns?
<b>Update</b> Also: comments with "?" but no TODO/FIXME label that might merit TODO | {
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} | 4 | 2019-10-08T16:03:25Z | 2019-10-08T21:25:18Z | 2019-10-08T21:24:17Z | MEMBER | null | Summary of changes:
- Made `_iterate_slices` abstract in base class, moved pre-existing definition from Base -> Series where it was actually being called (DataFrame already overrides)
- Made `_wrap_transformed_output` Abstract in base class, as both Series and DataFrame override this
- Redefined `_wrap_applied_output`; this was Abstract in base class before but its definition was not in sync with how the subclasses actually defined
- Renamed `_wrap_output` to `_wrap_series_output` as it is only valid for SeriesGroupBy
- Renamed `_wrap_generic_output` to `_wrap_frame_output` as it is only valid for DataFrameGroupBy
- Renamed `_aggregate_generic` to `_aggregate_frame` as it is only valid for DataFrameGroupBy
More to come in separate PRs
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} | 2 | 2019-10-08T16:26:03Z | 2019-10-08T21:26:05Z | 2019-10-08T21:23:31Z | MEMBER | null | By far the largest component of the diff is removing `from numpy import nan` imports and enabling the corresponding code-check.
Other than that there are a handful of cleanups, typings, docstring, and error message improvements collected from orphan branches locally. | {
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If `name` is not found, it attempts to raise a `ValueError` with formatted string that expects keyword argument `name`, however `names` is passed as a positional argument. In effect it will throw unexpected `KeyError`.
Steps to reproduce the problem:
```python
from odf.opendocument import OpenDocumentSpreadsheet
import tempfile
path = tempfile.mktemp()
OpenDocumentSpreadsheet().save(path)
pd.read_excel(path, engine="odf", sheet_name="foo")
# Traceback (most recent call last):
...
# KeyError: 'name'
```
Expected behavior
```python
# Traceback (most recent call last):
# ...
# ValueError: sheet foo not found
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-10-08T17:13:40Z | 2020-01-01T16:02:32Z | 2020-01-01T15:59:36Z | CONTRIBUTOR | null | #### Problem description
Sometimes I encounter html tables that I need to parse where cells contain full-tables themselves. When using `pd.read_html` itself, this does not work quite that well. With a simple call to pd.read_html, each of these nested tables are parsed and then "inserted"/"concatenated" as rows.
```python
import pandas as pd
df_up = pd.read_html("up_pf00344.test.html", attrs = {'id': 'results'})
```
Screenshot of output as parsed:
#### Expected Output
I think a more natural parsing would be to parse each nested tables into their own pd.DataFrames and be inserted as objects as the value of the corresponding column.
Screenshot of table as rendered in html:
Link to file: https://gist.github.com/smsaladi/6adb30efbe70f9fed0306b226e8ad0d8
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.14.4.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.utf8
LANG : en_US.utf8
LOCALE : en_US.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 : 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 : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
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
</details>
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} | 2 | 2019-10-08T17:56:04Z | 2019-10-08T21:33:31Z | 2019-10-08T21:21:13Z | MEMBER | null | Along with #28833 this gets close to finishing off the not-boxed cases | {
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} | 2 | 2019-10-08T18:20:00Z | 2019-10-11T15:54:38Z | 2019-10-11T15:50:18Z | MEMBER | null | @toobaz any guesses what this might have been intended to catch? | {
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} | 1 | 2019-10-08T18:35:16Z | 2019-10-08T20:56:40Z | 2019-10-08T20:56:32Z | CONTRIBUTOR | null | …twice
- [x] closes #28827
- [ ] 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 | 2 | 2019-10-08T18:40:19Z | 2019-10-11T09:58:11Z | 2019-10-10T20:26:34Z | NONE | null | Would it be possible to have a proper table of contents and index added to the pdf version of the documentation? The pdf doc for verion 0.25.1 has a section labeled contents; however, the TOC is missing. Also, consider relagating the previous version documentation to an appendix this would allow a inclusion of an index for the current version documentation only.
The reason this change is requested is because I do not have full access to a computer system and/or its environment. The only documentation I have access to must be physical. Having such a large document necessitates being able to navigate it efficiently.
Thank you.
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} | 3 | 2019-10-08T20:40:03Z | 2019-10-11T18:35:24Z | 2019-10-11T18:15:35Z | MEMBER | null | Mostly fixing extra " " introduced by black (will take a look at the issue tracker there to see if that can be fixed once and for all).
Also change `except Foo as e:` to `except Foo as err` and remove one `except Exception` in a docs file. Clarified a whatsnew note that @jorisvandenbossche asked for a while back. | {
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} | 2 | 2019-10-08T21:26:02Z | 2019-10-09T07:38:05Z | 2019-10-09T07:37:50Z | CONTRIBUTOR | null | - [ ] closes #27977
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fixed for the following functions
`pandas.io.formats.style.Styler.set_caption
pandas.plotting.table
pandas.plotting.register_matplotlib_converters
pandas.plotting.deregister_matplotlib_converters
pandas.arrays.IntervalArray.mid
pandas.Timedelta.ceil
pandas.Timedelta.floor
pandas.Period
pandas.Period.asfreq
pandas.Period.to_timestamp`
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Index gets lost when DataFrame melt method is used #17440
ENH: Add optional argument keep_index to dataframe melt method #17459
Melt enhance #17677
Please let me know if additional things need to be done to complete this PR
----
Setting keep_index to True will reuse the original DataFrame index +
names of melted columns as additional level. closes issue #17440
(cherry picked from commit 0c64bf0e5f145781c0f74fb93ffcd63a6d964bd9)
- [x] closes #xxxx
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 8 | 2019-10-08T22:42:56Z | 2019-10-09T15:24:20Z | 2019-10-09T04:31:19Z | MEMBER | null | ```python
import pandas as pd
left = pd.DataFrame({"a": [1.0, 2.0]})
right = pd.DataFrame({"a": [1.0]})
pd.merge(left, right, how="left", on="a", suffixes=("_left", "_right"))
# a
# 0 1.0
# 1 2.0
```
#### Problem description
The documentation for `suffixes` in `pd.merge` reads:
```
suffixes : tuple of (str, str), default (‘_x’, ‘_y’)
Suffix to apply to overlapping column names in the left and right side, respectively. To raise an exception on overlapping columns use (False, False).
```
but it seems that the join columns themselves are ignored. I'm guessing this is by design, but if so should the docstring be updated to reflect this?
#### Expected Output
```python
# a_left a_right
# 0 1.0 1.0
# 1 2.0 NaN
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} | 3 | 2019-10-08T22:57:01Z | 2019-10-11T15:30:07Z | 2019-10-11T12:24:27Z | MEMBER | null | @jreback less trivial than some of the others. These changes are based on my understanding that maybe_promote should _never_ be returning np.bytes_ dtype; those cases all become np.object_. That is what maybe_promote currently does, and this updates the tests to expect that behavior. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28862/events | https://github.com/pandas-dev/pandas/pull/28862 | 504,327,870 | MDExOlB1bGxSZXF1ZXN0MzI2MDAzMTgy | 28,862 | CLN: catch less in pd.io | {
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} | 2 | 2019-10-08T23:22:12Z | 2019-11-21T23:07:48Z | 2019-11-12T18:00:24Z | MEMBER | null | @bashtage (only tangentially related to this PR) there is an `except Exception` in pandas.io.stata that I'd like to make more specific. Any suggestions? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28863/events | https://github.com/pandas-dev/pandas/pull/28863 | 504,337,526 | MDExOlB1bGxSZXF1ZXN0MzI2MDExMTA3 | 28,863 | Vectorized ISO Format | {
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"description": "Memory or... | closed | false | null | [] | null | 4 | 2019-10-09T00:00:43Z | 2020-01-16T00:33:48Z | 2019-10-11T12:53:48Z | MEMBER | null | - [ ] closes #28180
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Needs work but could certainly use some guidance from @jbrockmendel
This uses the dt -> ISO string formatting that is deeply nested in the JSON code. It doesn't handle time zones properly (see #12997), doesn't match what you would get by default from `Timestamp.isoformat` (different precision) and doesn't support Timedeltas yet. When Timedeltas are supported this could ease some of the performance issues @cbertinato is seeing in #28595
In any case looking for guidance and thoughts on how to properly implement this, if this is even in the right direction
Here's a rough benchmark on performance:
```ipython
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} | 2 | 2019-10-09T03:35:04Z | 2019-10-10T13:34:20Z | 2019-10-10T12:51:18Z | MEMBER | null | Orthogonal to other outstanding maybe_promote PRs.
This one required pretty significant changes to the code. Using `np.min_scalar_type` and `np.can_cast` cleans this up a bit, but it is still pretty verbose. AFAICT there is no clear way to make it shorter without significantly sacrificing clarity.
In a follow-up I think L410-459 can be refactored out to a helper function. Waiting on that until I figure out the boxed=True cases, which are still troublesome. | {
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} | 3 | 2019-10-09T04:50:28Z | 2019-12-20T01:03:29Z | 2019-10-11T15:14:07Z | MEMBER | null | The common index tests use `setup_method` to create a `dict` of indexes to test against:
https://github.com/pandas-dev/pandas/blob/df2e0813e053cc5bc924b2292ea8918a6b27f0e2/pandas/tests/indexes/test_range.py#L25-L30
This `dict` of indexes is then iterated over within the tests:
https://github.com/pandas-dev/pandas/blob/df2e0813e053cc5bc924b2292ea8918a6b27f0e2/pandas/tests/indexes/common.py#L359-L363
The bulk of this PR involves converting `self.indices` into a parametrized fixture of indexes, and adjusting the tests to support this (largely just unindenting). I had to do this conversion for all indexes at once since common test code for all index classes utilizes this pattern, so the diff is fairly large, but it should be relatively simple changes.
I also had to make some changes to references to specific indexes as well (e.g. `self.index`, `self.strIndex`, etc.) since the `setup_method` code also directly set each index in the `dict` as a class attribute. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28866 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28866/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28866/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28866/events | https://github.com/pandas-dev/pandas/issues/28866 | 504,419,926 | MDU6SXNzdWU1MDQ0MTk5MjY= | 28,866 | DataFrame.replace with dict does not work with Duplicate Column Labels | {
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"id... | open | false | null | [] | null | 1 | 2019-10-09T05:14:35Z | 2020-09-17T19:46:48Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({
'name':'a a b b c d'.split(),
'value1':[1,None,2,None,3,4],
'value2':[1,None,2,None,3,4]
})
df.columns='name v v'.split()
df.fillna({'v':0})
```
#### Problem description
display the Warning message:
```
D:\Program Files\Anaconda3\lib\site-packages\pandas\core\frame.py:4239: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
**kwargs
```
and Data has not been modified
#### Output of ``pd.show_versions()``
<details>
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28867 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28867/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28867/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28867/events | https://github.com/pandas-dev/pandas/issues/28867 | 504,489,086 | MDU6SXNzdWU1MDQ0ODkwODY= | 28,867 | DEPR: deprecate Index.to_native_types() ? | {
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} | 2 | 2019-10-09T08:17:19Z | 2020-09-19T02:13:31Z | 2020-09-19T02:13:31Z | MEMBER | null | We currently have a public `Index.to_native_types()` method (https://dev.pandas.io/docs/reference/api/pandas.Index.to_native_types.html) that somehow formats the values of the index.
Example:
```
In [3]: pd.date_range("2012", periods=3).to_native_types()
Out[3]: array(['2012-01-01', '2012-01-02', '2012-01-03'], dtype=object)
```
I don't think this needs to be in the public API? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28868/events | https://github.com/pandas-dev/pandas/issues/28868 | 504,524,284 | MDU6SXNzdWU1MDQ1MjQyODQ= | 28,868 | Maximum of time series with NaT returns a dtype float64 with only NaN's | {
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} | [] | closed | false | null | [] | null | 2 | 2019-10-09T09:24:30Z | 2019-10-09T15:57:15Z | 2019-10-09T15:57:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
show_bug = True
a = pd.date_range("2019-1-1", periods=10, freq="1D", tz="Europe/Paris")
b = pd.date_range("2019-1-3", periods=10, freq="1H", tz="Europe/Paris")
if show_bug:
a = pd.Index([pd.NaT] + a.to_list()[1:])
multi = pd.MultiIndex.from_arrays([a, b])
m = multi.to_frame(index=False).max(axis=1)
print(m)
```
#### Problem description
When show_bug is False, no NaT's are present. This gives this output :
```
0 2019-01-03 00:00:00+01:00
1 2019-01-03 01:00:00+01:00
2 2019-01-03 02:00:00+01:00
3 2019-01-04 00:00:00+01:00
4 2019-01-05 00:00:00+01:00
5 2019-01-06 00:00:00+01:00
6 2019-01-07 00:00:00+01:00
7 2019-01-08 00:00:00+01:00
8 2019-01-09 00:00:00+01:00
9 2019-01-10 00:00:00+01:00
dtype: datetime64[ns, Europe/Paris]
```
When show_bug is True, all it does is replacing the first value in a with a NaT.
I expect the output to be the same, except for the first row being a NaT.
Here is what I get for show_bug=True :
```
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
dtype: float64
```
#### Expected Output
```
0 NaT
1 2019-01-03 01:00:00+01:00
2 2019-01-03 02:00:00+01:00
3 2019-01-04 00:00:00+01:00
4 2019-01-05 00:00:00+01:00
5 2019-01-06 00:00:00+01:00
6 2019-01-07 00:00:00+01:00
7 2019-01-08 00:00:00+01:00
8 2019-01-09 00:00:00+01:00
9 2019-01-10 00:00:00+01:00
dtype: datetime64[ns, Europe/Paris]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 4.6.5
hypothesis : 4.33.1
sphinx : 2.2.0
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 : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28869 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28869/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28869/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28869/events | https://github.com/pandas-dev/pandas/issues/28869 | 504,582,681 | MDU6SXNzdWU1MDQ1ODI2ODE= | 28,869 | Cannot `style.background_gradient` on an `Int64` column | {
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"de... | closed | false | null | [] | null | 5 | 2019-10-09T11:16:33Z | 2021-02-11T10:04:04Z | 2021-02-11T10:04:04Z | NONE | null | ```python
old = pd.Series(range(9))
new = pd.Series(range(9), dtype='Int64')
print(old.min(), old.values.min(), new.min()) # =0 OK
print(new.values.min()) # KO
old.to_frame().style.background_gradient() # OK
new.to_frame().style.background_gradient() # KO
```
#### Problem description
Similarly to https://github.com/pandas-dev/pandas/issues/25580, the newer Int64 backend is missing some basic functionality.
Expected: values.min() is available, gradients are displayed
Actual:
```python
----> 1 print(new.values.min())
AttributeError: 'IntegerArray' object has no attribute 'min'
```
and full stack of the `background_gradient` error:
<details>
```
AttributeError Traceback (most recent call last)
~/virtualenvs/jupyter/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/io/formats/style.py in _repr_html_(self)
161 Hooks into Jupyter notebook rich display system.
162 """
--> 163 return self.render()
164
165 @Appender(_shared_docs['to_excel'] % dict(
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/io/formats/style.py in render(self, **kwargs)
457 * table_attributes
458 """
--> 459 self._compute()
460 # TODO: namespace all the pandas keys
461 d = self._translate()
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/io/formats/style.py in _compute(self)
527 r = self
528 for func, args, kwargs in self._todo:
--> 529 r = func(self)(*args, **kwargs)
530 return r
531
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/io/formats/style.py in _apply(self, func, axis, subset, **kwargs)
536 if axis is not None:
537 result = data.apply(func, axis=axis,
--> 538 result_type='expand', **kwargs)
539 result.columns = data.columns
540 else:
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)
6485 args=args,
6486 kwds=kwds)
-> 6487 return op.get_result()
6488
6489 def applymap(self, func):
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/core/apply.py in get_result(self)
149 return self.apply_raw()
150
--> 151 return self.apply_standard()
152
153 def apply_empty_result(self):
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/core/apply.py in apply_standard(self)
255
256 # compute the result using the series generator
--> 257 self.apply_series_generator()
258
259 # wrap results
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/core/apply.py in apply_series_generator(self)
284 try:
285 for i, v in enumerate(series_gen):
--> 286 results[i] = self.f(v)
287 keys.append(v.name)
288 except Exception as e:
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/core/apply.py in f(x)
76
77 def f(x):
---> 78 return func(x, *args, **kwds)
79 else:
80 f = func
~/virtualenvs/jupyter/lib/python3.6/site-packages/pandas/io/formats/style.py in _background_gradient(s, cmap, low, high, text_color_threshold)
939
940 with _mpl(Styler.background_gradient) as (plt, colors):
--> 941 smin = s.values.min()
942 smax = s.values.max()
943 rng = smax - smin
AttributeError: ("'IntegerArray' object has no attribute 'min'", 'occurred at index 0')
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-1062.1.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 19.2.3
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: None
html5lib: None
sqlalchemy: 1.3.3
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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} | 4 | 2019-10-09T11:29:18Z | 2019-10-11T12:09:34Z | 2019-10-11T12:09:34Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
left = pd.DataFrame({'ts_int': [0, 100, 200], 'left_val': [1, 2, 3]})
right = pd.DataFrame({'ts_int': [50, 150, 250], 'right_val': [1, 2, 3]})
left['ts_int'] = left['ts_int'].astype(int)
right['ts_int'] = right['ts_int'].astype(int)
pd.merge_asof(left, right, on='ts_int', tolerance=100)
> pandas.errors.MergeError: key must be integer, timestamp or float
print(left['ts_int'].dtype)
>>> int32
print(right['ts_int'].dtype)
>>> int32
```
#### Problem description
merge_asof() throws the MergeError `key must be integer, timestamp or float` even when the data used to merge on is a valid integer type.
This happens with all types of int with the exception of int64, as a result of the check performed when a tolerance is passed _(pandas/core/reshape/merge.py:1641)_. This check uses the `is_int64_dtype(lt)`, which will ofcourse return `False` for every type of int that isn't `int64`. This requirement of the `on` key being int64 is however not documented in the docs, nor is it reflected from the raised error.
#### Expected Output
The expected output can go either of two ways. If it should be possible to perform the merge on every integer type, the expected output would be:
```
ts_int left_val right_val
0 0 1 NaN
1 100 2 1.0
2 200 3 2.0
```
If it shouldn't be possible, I'd expect the returned MergeError and/or the docs to reflect that the integer type that should be used as key to merge on has to be of int64 type.
#### 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 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
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 : 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
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28871/events | https://github.com/pandas-dev/pandas/issues/28871 | 504,705,227 | MDU6SXNzdWU1MDQ3MDUyMjc= | 28,871 | columns sometimes not added to empty dataframe | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 4 | 2019-10-09T14:53:06Z | 2019-10-10T04:05:35Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import datetime
import pandas as pd
test = pd.DataFrame({'date':[datetime.datetime(2000,1,1)]}).set_index('date')
test = test[0:0].copy()
test['3010'] = None
test['2010'] = None
print(test)
```
#### Problem description
The column '3010' is added to the dataframe as expected, but column 2010 does not get added.
#### Expected Output
Empty DataFrame
Columns: [3010, 2010]
Index: []
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.5.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 9.0.1
setuptools : 36.4.0
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
openpyxl : None
odfpy : None
gcsfs : None
bottleneck : None
pandas_gbq : None
xarray : None
tables : None
scipy : None
pyarrow : None
xlsxwriter : None
matplotlib : None
sqlalchemy : None
pytables : None
xlwt : None
lxml.etree : None
s3fs : None
xlrd : None
numexpr : None
bs4 : None
fastparquet : None
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"description": "Interval... | open | false | null | [] | null | 0 | 2019-10-09T15:27:25Z | 2020-04-27T00:19:08Z | null | CONTRIBUTOR | null | #### The following ipython session demonstrates the issue
```python
import numpy as np
import pandas as pd
# a simple population of integer events
population = np.array([1, 2, 2, 3, 3, 3])
# let's make a histogram of the population
hist, edges = np.histogram(population, 4)
# lets turn the histogram into a dataframe using the edges as IntervalIndex
df = pd.DataFrame(data=hist, index=pd.IntervalIndex.from_breaks(edges))
# The 3 events are indexable properly
print(df.loc[3]) # Works
# To see the event at 1 you need to apply some offset
print(df.loc[1+1e-6]) # Works but ugly
print(df.loc[1]) # fails
```
#### Problem description
The current behavior of `IntervalIndex` is that all the intervals have the same closing. It is however desireable to have an `IntervalIndex` like `[[0, 1], (1, 2], (2, 3]]` including the lower and the upper limit.
`cut()` solves this by adding an arbitrary offset to the lower limit to make it loo like `[(-0.002, 1], (1, 2], (2, 3]]`. Then the lower interval is left open but the value 0.0 is still contained by the interval.
This however results in the fact that the intervals contains also values like -0.001 which might be undesired as due to semantics negative values might be impossible. Histograms that allow events in the closed range `[0,1]`, forbidding negative values are impossible to model with a `pandas.IntervalIndex`.
#### Expected behavior.
I'd like to be able to do something like
```
idx = pd.IntervalIndex.from_breaks(np.arange(10))
idx[0].set_closed('both')
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... | closed | false | null | [] | null | 10 | 2019-10-09T18:31:52Z | 2020-02-06T23:59:52Z | 2020-02-06T23:59:52Z | NONE | null | closes #16716
closes #28825
closes #28826
solves https://github.com/pandas-dev/pandas/pull/16910
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Continuation of https://github.com/pandas-dev/pandas/pull/21504 and https://github.com/pandas-dev/pandas/pull/17087
This PR is not ready but I'd appreciate any early comment/feedback. | {
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] | closed | false | null | [] | null | 2 | 2019-10-09T19:41:53Z | 2019-10-10T15:48:29Z | 2019-10-10T15:48:29Z | NONE | null | #### Problem description
Many web service return data divided in subsets. Common conventrion is return data as dict:
```
{"count":78,
"next":"link to next part of data or null",
"previous":null,
"results":[
{"name":"John"surname":"Smith"},
{"name":"Bob"surname":"Black"},
]}
```
Could you consider to add to read_json flag paggination=False, if flag is set to True then json shoulde be compose by sum of results:
```
url = "..."
results = []
while url is not None:
_json = json.load(requests.get(url).content)
results += _json['results']
url = _json['next']
```
There is same question on StackOverflow about this, so I think this feature will be helpfull for many users. | {
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} | 1 | 2019-10-09T19:58:12Z | 2019-10-10T01:51:09Z | 2019-10-10T01:27:38Z | MEMBER | null | The new casting in cython_agg_blocks is specific to a single IntegerArray test case. We could pretty reasonably move that into maybe_downcast_numeric. For the moment id rather hold off since I expect other EA cases to show up here.
cc @WillAyd | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28880/events | https://github.com/pandas-dev/pandas/pull/28880 | 504,884,404 | MDExOlB1bGxSZXF1ZXN0MzI2NDQwMDMx | 28,880 | TST: Fix maybe_promote floating non-boxed tests | {
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} | 1 | 2019-10-09T20:43:50Z | 2019-10-10T13:33:32Z | 2019-10-10T12:50:18Z | MEMBER | null | Like #28864, this involves changing the underlying function. Some of this can be de-duplicated once these are in.
I think that this is the last of the xfails for non-boxed test cases. Really looking forward to having this done with,. | {
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"default"... | open | false | null | [] | null | 2 | 2019-10-09T22:07:16Z | 2021-07-21T05:35:23Z | null | NONE | null | OS: Windows 10
Python: 3.7.3
Pandas: 0.25.1
The DataFrame object has functions such as mean() and sum() which return a Series that contains the appropriate aggregation for each column of the DataFrame. Not all data types are appropriate for all aggregations. For example, strings are not suitable for mean or sum.
Current Behavior:
When mean, sum, or other numerical aggregation is called on a DataFrame that has both numeric and string columns, it attempts to perform the aggregation on all of the columns. When it tries to perform the numeric aggregation on the string column it encounters an exception (TypeError). That exception is silenced and the affected column is excluded from the results.
Why this is a bug:
As the number of rows in the DataFrame increases, the time it takes to attempt and fail to perform a numerical aggregation on the string column increases at (at least) an exponential rate. I doubled the number of rows and saw a x80 increase in runtime. Better detection of this issue could allow these aggregation functions to scale linearly.
Proposed Solution:
Before attempting to perform an aggregation on a column, the function should first check the defined data type. If that data type is not compatible then the column should be skipped immediately without attempting the aggregation.
Alternative Solution:
Some might argue that if a column contains all numeric data it should still be allowed to have the aggregation performed regardless of how that column is defined. I disagree, I believe clean code dictates that a column should behave according to the declared data type. Still, if you wanted to support this kind of implicit type cast, it should be done one row at a time, failing at the first non-numeric value. In my test dataset, all values were non-numeric, so the function could have failed after examining only one value, but instead the number of values available bloats the runtime before the error is encountered.
Note on fix implementation:
It appears to me that the DataFrame aggregations are actually looping over the Series in the DataFrame and calling the Series aggregations in turn, then silencing any exceptions. An elegant solution would be to make the change to the Series aggregation functions. The Series should check its own type when the aggregation function is called, and throw an exception if the defined type is not compatible with the aggregation. The DataFrame aggregation function could then silence the exception as usual. This would increase performance without altering functionality in any way. | {
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} | 11 | 2019-10-09T22:31:43Z | 2019-10-30T18:29:08Z | 2019-10-30T18:29:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
dtf = dtf.groupby(cut(dtf[X], rng)).quantile(.5)
```
#### Problem description
Process: Python [24642]
Path: /Library/Frameworks/Python.framework/Versions/3.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 3.7.4 (3.7.4)
Code Type: X86-64 (Native)
Parent Process: Python [24593]
Responsible: iTerm2 [1703]
User ID: 501
Date/Time: 2019-10-09 17:11:04.949 -0500
OS Version: Mac OS X 10.15 (19A583)
Report Version: 12
Bridge OS Version: 3.0 (14Y904)
Anonymous UUID: F986CCB3-5DD1-9587-8492-6D8B8A43979D
Sleep/Wake UUID: 42F77302-9822-4979-89CB-7C39F3C0556A
Time Awake Since Boot: 67000 seconds
Time Since Wake: 1900 seconds
System Integrity Protection: enabled
Crashed Thread: 7
Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x000000013a6dcff8
Exception Note: EXC_CORPSE_NOTIFY
Termination Signal: Segmentation fault: 11
Termination Reason: Namespace SIGNAL, Code 0xb
Terminating Process: exc handler [24642]
VM Regions Near 0x13a6dcff8:
MALLOC_LARGE 000000013a5ee000-000000013a633000 [ 276K] rw-/rwx SM=PRV
-->
MALLOC_LARGE 000000013a6dd000-000000013a74d000 [ 448K] rw-/rwx SM=PRV
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<details>
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3 org.python.python 0x000000010d874d42 _PyMethodDescr_FastCallKeywords + 82
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17 org.python.python 0x000000010d92e413 _PyEval_EvalCodeWithName + 2467
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23 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
24 org.python.python 0x000000010d92d8c2 call_function + 738
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26 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
27 org.python.python 0x000000010d86e004 _PyFunction_FastCallDict + 148
28 org.python.python 0x000000010d86f4bf _PyObject_Call_Prepend + 143
29 org.python.python 0x000000010d86e707 PyObject_Call + 135
30 org.python.python 0x000000010d9b5b77 t_bootstrap + 71
31 org.python.python 0x000000010d96c939 pythread_wrapper + 25
32 libsystem_pthread.dylib 0x00007fff733eed76 _pthread_start + 125
33 libsystem_pthread.dylib 0x00007fff733eb5d7 thread_start + 15
Thread 3:
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1 libsystem_pthread.dylib 0x00007fff733ef082 _pthread_cond_wait + 701
2 org.python.python 0x000000010d96ce01 PyThread_acquire_lock_timed + 673
3 org.python.python 0x000000010d9b620f acquire_timed + 111
4 org.python.python 0x000000010d9b6320 lock_PyThread_acquire_lock + 48
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6 org.python.python 0x000000010d874d42 _PyMethodDescr_FastCallKeywords + 82
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9 org.python.python 0x000000010d92e413 _PyEval_EvalCodeWithName + 2467
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12 org.python.python 0x000000010d92a873 _PyEval_EvalFrameDefault + 25155
13 org.python.python 0x000000010d92e413 _PyEval_EvalCodeWithName + 2467
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27 org.python.python 0x000000010d86e004 _PyFunction_FastCallDict + 148
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30 org.python.python 0x000000010d9b5b77 t_bootstrap + 71
31 org.python.python 0x000000010d96c939 pythread_wrapper + 25
32 libsystem_pthread.dylib 0x00007fff733eed76 _pthread_start + 125
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Thread 4:
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1 libsystem_kernel.dylib 0x00007fff7332b6ac mach_msg + 60
2 com.apple.CoreFoundation 0x00007fff3bee419b __CFRunLoopServiceMachPort + 322
3 com.apple.CoreFoundation 0x00007fff3bee3737 __CFRunLoopRun + 1695
4 com.apple.CoreFoundation 0x00007fff3bee2e13 CFRunLoopRunSpecific + 499
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7 org.python.python 0x000000010d86f1cc _PyMethodDef_RawFastCallKeywords + 668
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Thread 5:
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1 libsystem_kernel.dylib 0x00007fff7332b6ac mach_msg + 60
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Thread 6:
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Thread 7 Crashed:
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1 algos.cpython-37m-darwin.so 0x0000000119e8937c __pyx_FusedFunction_call + 812
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Thread 8:
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81 org.python.python 0x000000010d92d8c2 call_function + 738
82 org.python.python 0x000000010d92a873 _PyEval_EvalFrameDefault + 25155
83 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
84 org.python.python 0x000000010d86e004 _PyFunction_FastCallDict + 148
85 org.python.python 0x000000010d86f4bf _PyObject_Call_Prepend + 143
86 org.python.python 0x000000010d86e707 PyObject_Call + 135
87 org.python.python 0x000000010d92ab9e _PyEval_EvalFrameDefault + 25966
88 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
89 org.python.python 0x000000010d92d8c2 call_function + 738
90 org.python.python 0x000000010d92a873 _PyEval_EvalFrameDefault + 25155
91 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
92 org.python.python 0x000000010d92d8c2 call_function + 738
93 org.python.python 0x000000010d92a873 _PyEval_EvalFrameDefault + 25155
94 org.python.python 0x000000010d86ea10 function_code_fastcall + 128
95 org.python.python 0x000000010d86e004 _PyFunction_FastCallDict + 148
96 org.python.python 0x000000010d86f4bf _PyObject_Call_Prepend + 143
97 org.python.python 0x000000010d86e707 PyObject_Call + 135
98 org.python.python 0x000000010d9b5b77 t_bootstrap + 71
99 org.python.python 0x000000010d96c939 pythread_wrapper + 25
100 libsystem_pthread.dylib 0x00007fff733eed76 _pthread_start + 125
101 libsystem_pthread.dylib 0x00007fff733eb5d7 thread_start + 15
Thread 7 crashed with X86 Thread State (64-bit):
rax: 0x00007fc23e50d830 rbx: 0xffffffffffffffff rcx: 0x0000000148926000 rdx: 0x0000000131c66000
rdi: 0x000000000013dc10 rsi: 0xfffffffffffffff8 rbp: 0x0000700006b28490 rsp: 0x0000700006b27df0
r8: 0x0000000000000008 r9: 0x0000000000000008 r10: 0x0000000000000008 r11: 0x0000000000000001
r12: 0x000000013a6dd000 r13: 0x0000700006b282f8 r14: 0x00000001352d0760 r15: 0x000000000000880b
rip: 0x000000011b1596cf rfl: 0x0000000000010282 cr2: 0x000000013a6dcff8
Logical CPU: 4
Error Code: 0x00000006 (no mapping for user data read)
Trap Number: 14
Binary Images:
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0x7fff7116b000 - 0x7fff71183ffb liblzma.5.dylib (16) <D416FC97-76EC-38B5-A134-85DDFEA9D297> /usr/lib/liblzma.5.dylib
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0x7fff71f71000 - 0x7fff71f89ff7 libresolv.9.dylib (67) <06480BFC-6372-3225-B77A-F5AC9969DB64> /usr/lib/libresolv.9.dylib
0x7fff71fcf000 - 0x7fff71fe1fff libsasl2.2.dylib (213) <277129F1-29AE-34EB-BBAB-FF6DF4B43FAB> /usr/lib/libsasl2.2.dylib
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0x7fff722c5000 - 0x7fff722f2ffb libssl.46.dylib (47.11.1) <CA81BD73-E5BF-3F88-A70E-49BA7C6B2781> /usr/lib/libssl.46.dylib
0x7fff723c4000 - 0x7fff7241fff0 libusrtcp.dylib (1880.11.2) <B821F69B-2E28-36C7-8F11-6990F8D4E26B> /usr/lib/libusrtcp.dylib
0x7fff72420000 - 0x7fff72423ffb libutil.dylib (57) <844B7887-09B3-3D12-ACDE-C4EB8F53DC62> /usr/lib/libutil.dylib
0x7fff72424000 - 0x7fff72431fff libxar.1.dylib (420) <E0D7C0A6-76EC-3682-A393-6596D4986269> /usr/lib/libxar.1.dylib
0x7fff72437000 - 0x7fff72519ff7 libxml2.2.dylib (32.12) <C0A87484-D334-3B50-8F8A-A9C63295F49E> /usr/lib/libxml2.2.dylib
0x7fff7251d000 - 0x7fff72545fff libxslt.1.dylib (16.6) <CD9E79B0-159A-3055-B62A-57AB2B445912> /usr/lib/libxslt.1.dylib
0x7fff72546000 - 0x7fff72558fff libz.1.dylib (76) <3FC3FC3E-ABF3-3167-9078-B54C952608B4> /usr/lib/libz.1.dylib
0x7fff72fbd000 - 0x7fff72fc2ff7 libcache.dylib (83) <8EC69760-6DAA-3068-9372-F1D554C548E5> /usr/lib/system/libcache.dylib
0x7fff72fc3000 - 0x7fff72fceff7 libcommonCrypto.dylib (60165) <698BE754-137D-361D-B826-57B8DD969E4A> /usr/lib/system/libcommonCrypto.dylib
0x7fff72fcf000 - 0x7fff72fd6fff libcompiler_rt.dylib (101.2) <0BE7F119-C9C2-3612-A3F4-2336D4444476> /usr/lib/system/libcompiler_rt.dylib
0x7fff72fd7000 - 0x7fff72fe0ff7 libcopyfile.dylib (166) <B5E73B1C-0BCF-33FE-80A1-D9E3BA3033D4> /usr/lib/system/libcopyfile.dylib
0x7fff72fe1000 - 0x7fff73078fc3 libcorecrypto.dylib (866.0.10) <58344B13-CD10-3697-A516-6F5B06DD1EB7> /usr/lib/system/libcorecrypto.dylib
0x7fff7318f000 - 0x7fff731d0ff0 libdispatch.dylib (1173.0.3) <F4260D89-F67D-30CB-AF61-7ED25CB687DB> /usr/lib/system/libdispatch.dylib
0x7fff731d1000 - 0x7fff73206fff libdyld.dylib (732.8) <98960E27-A08B-36DA-A5CB-8538B2D6757E> /usr/lib/system/libdyld.dylib
0x7fff73207000 - 0x7fff73207ffb libkeymgr.dylib (30) <682B41BC-BDC2-38D5-8820-87099606FA12> /usr/lib/system/libkeymgr.dylib
0x7fff73208000 - 0x7fff73214ff7 libkxld.dylib (6153.11.26) <53BE9630-BDC8-3649-8709-7A4F86777B1A> /usr/lib/system/libkxld.dylib
0x7fff73215000 - 0x7fff73215ff7 liblaunch.dylib (1738.11.1) <7FE11F0D-65BC-3726-B1DD-E84F329193E0> /usr/lib/system/liblaunch.dylib
0x7fff73216000 - 0x7fff7321bff7 libmacho.dylib (949.0.1) <163DFE06-2FAD-3CBC-80EF-C38EED6AEA52> /usr/lib/system/libmacho.dylib
0x7fff7321c000 - 0x7fff7321eff3 libquarantine.dylib (110.0.4) <C8F39330-8CB5-30B0-8564-96453DCEFAD7> /usr/lib/system/libquarantine.dylib
0x7fff7321f000 - 0x7fff73220ff7 libremovefile.dylib (48) <FB280185-B5ED-3F08-B08A-A378865C1398> /usr/lib/system/libremovefile.dylib
0x7fff73221000 - 0x7fff73238fff libsystem_asl.dylib (377.0.1) <30CE9DAF-B1FA-3510-832B-F1CE19933ED7> /usr/lib/system/libsystem_asl.dylib
0x7fff73239000 - 0x7fff73239fff libsystem_blocks.dylib (74) <E0B8C825-E62B-357E-A2A0-13776F0A0F8C> /usr/lib/system/libsystem_blocks.dylib
0x7fff7323a000 - 0x7fff732c1ff7 libsystem_c.dylib (1353.11.2) <2A5BFAFE-8214-3B35-AD46-C07A1A8B8941> /usr/lib/system/libsystem_c.dylib
0x7fff732c2000 - 0x7fff732c5fff libsystem_configuration.dylib (1061.0.2) <56174463-22ED-337F-B0D4-94995DCDB9B7> /usr/lib/system/libsystem_configuration.dylib
0x7fff732c6000 - 0x7fff732c9ff7 libsystem_coreservices.dylib (114) <01695913-028E-3AE1-8D4E-2B2769109811> /usr/lib/system/libsystem_coreservices.dylib
0x7fff732ca000 - 0x7fff732d1fff libsystem_darwin.dylib (1353.11.2) <4CE52C63-87AA-3C6D-899F-8C983E5FC061> /usr/lib/system/libsystem_darwin.dylib
0x7fff732d2000 - 0x7fff732d9ffb libsystem_dnssd.dylib (1096.0.2) <DC7381E8-F09F-3441-8267-9B8F50A0EBA9> /usr/lib/system/libsystem_dnssd.dylib
0x7fff732da000 - 0x7fff732dbffb libsystem_featureflags.dylib (17) <DBCA4AA2-CA05-38D5-AB4B-BE0F3E09BB8B> /usr/lib/system/libsystem_featureflags.dylib
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0x7fff7332a000 - 0x7fff73356fff libsystem_kernel.dylib (6153.11.26) <4CE9D54A-A975-348E-B878-EE74EDFC956B> /usr/lib/system/libsystem_kernel.dylib
0x7fff73357000 - 0x7fff7339eff7 libsystem_m.dylib (3178) <4F516261-0C0E-3105-AF35-EF39D6347B50> /usr/lib/system/libsystem_m.dylib
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0x7fff733df000 - 0x7fff733e8fe7 libsystem_platform.dylib (220) <90E508E4-46D8-33FF-8552-DDAA079977A0> /usr/lib/system/libsystem_platform.dylib
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0x7fff733f4000 - 0x7fff733f8ffb libsystem_sandbox.dylib (1217.11.16) <51129CBB-BC46-37F1-A1B5-ECFA9530704D> /usr/lib/system/libsystem_sandbox.dylib
0x7fff733f9000 - 0x7fff733fbfff libsystem_secinit.dylib (62.0.4) <A48D9AF3-75F2-3331-A0C8-0A23771F4AC7> /usr/lib/system/libsystem_secinit.dylib
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0x7fff7341c000 - 0x7fff73421ffb libunwind.dylib (35.4) <F5AE1D43-7C5F-3BA2-94D3-E769F82C0F61> /usr/lib/system/libunwind.dylib
0x7fff73422000 - 0x7fff73456ff6 libxpc.dylib (1738.11.1) <2E9076CD-6C0E-38B6-8403-B2DDCE125FBF> /usr/lib/system/libxpc.dylib
External Modification Summary:
Calls made by other processes targeting this process:
task_for_pid: 32
thread_create: 0
thread_set_state: 0
Calls made by this process:
task_for_pid: 0
thread_create: 0
thread_set_state: 0
Calls made by all processes on this machine:
task_for_pid: 7803078
thread_create: 0
thread_set_state: 0
VM Region Summary:
ReadOnly portion of Libraries: Total=660.2M resident=0K(0%) swapped_out_or_unallocated=660.2M(100%)
Writable regions: Total=1.5G written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=1.5G(100%)
VIRTUAL REGION
REGION TYPE SIZE COUNT (non-coalesced)
=========== ======= =======
Activity Tracing 256K 1
Dispatch continuations 16.0M 1
Kernel Alloc Once 8K 1
MALLOC 1.1G 724
MALLOC guard page 16K 3
MALLOC_LARGE (reserved) 256K 2 reserved VM address space (unallocated)
STACK GUARD 36K 9
Stack 51.6M 9
VM_ALLOCATE 4K 1
VM_ALLOCATE (reserved) 192.0M 2 reserved VM address space (unallocated)
__DATA 62.5M 464
__DATA_CONST 20K 1
__LINKEDIT 380.1M 248
__OBJC_RO 31.8M 1
__OBJC_RW 1764K 2
__TEXT 280.2M 382
__UNICODE 564K 1
mapped file 28K 1
shared memory 44K 5
=========== ======= =======
TOTAL 2.1G 1858
TOTAL, minus reserved VM space 2.0G 1858
Model: MacBookPro13,3, BootROM 262.0.0.0.0, 4 processors, Quad-Core Intel Core i7, 2.9 GHz, 16 GB, SMC 2.38f8
Graphics: kHW_IntelHDGraphics530Item, Intel HD Graphics 530, spdisplays_builtin
Graphics: kHW_AMDRadeonPro460Item, AMD Radeon Pro 460, spdisplays_pcie_device, 4 GB
Memory Module: BANK 0/DIMM0, 8 GB, LPDDR3, 2133 MHz, 0x802C, 0x4D5435324C31473332443450472D30393320
Memory Module: BANK 1/DIMM0, 8 GB, LPDDR3, 2133 MHz, 0x802C, 0x4D5435324C31473332443450472D30393320
AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x15A), Broadcom BCM43xx 1.0 (7.77.105.1 AirPortDriverBrcmNIC-1429)
Bluetooth: Version 7.0.0f8, 3 services, 27 devices, 1 incoming serial ports
Network Service: Wi-Fi, AirPort, en0
USB Device: USB 3.0 Bus
USB Device: Apple T1 Controller
Thunderbolt Bus: MacBook Pro, Apple Inc., 41.2
Thunderbolt Bus: MacBook Pro, Apple Inc., 41.2
</details>
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
NSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : 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 : 19.2.3
setuptools : 40.8.0
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 : None
pandas_datareader: 0.8.1
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28883 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28883/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28883/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28883/events | https://github.com/pandas-dev/pandas/pull/28883 | 504,933,371 | MDExOlB1bGxSZXF1ZXN0MzI2NDc3MDIw | 28,883 | CLN: dont catch Exception in groupby var | {
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} | 1 | 2019-10-09T22:40:33Z | 2019-10-10T01:55:47Z | 2019-10-10T01:25:29Z | MEMBER | null | Between this, #28878, and #28873, I'm finding that the outside-in approach to cleaning this up is proving easier the earlier inside-out approach. | {
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First part of removal od ``NDFrame.ix``. There's still alot of tests for this, so I'll do this removal in 2-3 steps. This PR removes pandas/tests/indexing/test_ix.py and tests for ix in a few more places. | {
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} | 1 | 2019-10-10T03:45:40Z | 2019-10-10T16:04:28Z | 2019-10-10T16:00:24Z | CONTRIBUTOR | null | - [x] xref #28724
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-10-10T03:47:10Z | 2019-10-11T15:25:48Z | 2019-10-11T12:01:31Z | MEMBER | null | There will be some nice cleanups we can do after we bump cython to 0.30 (which hasnt come out yet).
Also I think there is some na-checking code that we can share between the various fused-types functions after this. | {
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} | 1 | 2019-10-10T03:54:24Z | 2019-10-10T16:03:57Z | 2019-10-10T15:53:58Z | CONTRIBUTOR | null | - [x] xref #28724
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 1 | 2019-10-10T04:38:34Z | 2019-10-10T05:34:57Z | 2019-10-10T05:34:50Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# code
df_sam = pd.DataFrame([["a", 'b'], ["", "c"], ["c","d"], ["", "s"]], columns=["col1", "col2"])
```
df_sam dataframe:
| col1 | col2 |
| --- | --- |
| a | b |
| | c|
| c | d |
| | s |
#### the expected result is
```python
df_sam.col1.replace("", None)
```
0 a
1 None
2 c
3 None
Name: col1, dtype: object
#### the actual result is
```python
df_sam.col1.replace("", None)
```
0 a
1 a
2 c
3 c
Name: col1, dtype: object
any idea why the actual result is not what i expected, wondering this isuue is a bug or not?
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} | 7 | 2019-10-10T07:04:29Z | 2019-11-09T01:00:05Z | 2019-11-09T00:59:55Z | CONTRIBUTOR | null | xref https://github.com/python-sprints/pandas-mentoring/issues/163
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} | 0 | 2019-10-10T07:23:42Z | 2019-10-10T12:43:43Z | 2019-10-10T09:20:23Z | CONTRIBUTOR | null | Corrects the spelling of compiler in contributing.rst.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"descript... | closed | false | null | [] | null | 2 | 2019-10-10T07:48:14Z | 2020-04-03T05:47:04Z | 2020-04-03T05:46:57Z | NONE | null | code:
t = [1]*4782+[2]*1030+[3]*251+[4]*122+[5]*177
t = pd.Series(t)
result = pd.qcut(t,50,duplicates = 'drop').value_counts()
problem:
print(result), we get
(0.999, 2.0] 5812
(2.0, 3.0] 251
(4.0, 5.0] 177
(3.0, 4.0] 122
t.value_counts(), we get
1 4782
2 1030
3 251
5 177
4 122
why 1 and 2 combines in the same bin? | {
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```python
>>> import pandas as pd
>>> d = pd.DataFrame(([1, 2], [3, 4]), columns=["Col 1", "Col 2"])
>>> d.query("`Col 1` < `Col 2`") # This works as expected.
Col 1 Col 2
0 1 2
1 3 4
>>> d.query("`Col 1` < `Col 2`") # 2 whitespaces between "Col" and "1". Also works, but not expected?
Col 1 Col 2
0 1 2
1 3 4
>>> d = pd.DataFrame(([1, 2], [3, 4]), columns=["Col 1", "Col 2"]) # 2 whitespaces between "Col" and "1" in the column name.
>>> d.query("`Col 1` < `Col 2`") # 2 whitespaces between "Col" and "1" in the query.
pandas.core.computation.ops.UndefinedVariableError: name 'Col_1_BACKTICK_QUOTED_STRING' is not defined
>>> d.query("`Col 1` < `Col 2`") # 10 whitespaces between "Col" and "1".
pandas.core.computation.ops.UndefinedVariableError: name 'Col_1_BACKTICK_QUOTED_STRING' is not defined
>>> d.eval("`Col 1` < `Col 2`") # 2 whitespaces between "Col" and "1".
pandas.core.computation.ops.UndefinedVariableError: name 'Col_1_BACKTICK_QUOTED_STRING' is not defined
```
#### Problem description
When using backticks in the input to `DataFrame.query` or `DataFrame.eval`, it seems that the number of whitespaces between `Col` and `1`, is mapped into: `Col_1_BACKTICK_QUOTED_STRING`, regardless of how many whitespaces there are between `Col` and `1`.
Having 2 whitespaces when defining the column name in the DataFrame, it seems that the column name is defined as: `Col__1_BACKTICK_QUOTED_STRING`.
With 10 whitespaces: `Col__________1_BACKTICK_QUOTED_STRING`, and so forth.
This makes the number of whitespaces in the column name important, as column name will be based on the amount of them. However, the number of whitespaces in the input to `DataFrame.query` or `DataFrame.eval` does not seem to have any affect, and will always have one underscore, and not match the column name if it has more than one whitespace.
#### Expected Output
The expected output would be to have the column name and the input to `query` or `eval` transformed with the same function, having a one to one mapping between whitespaces and underscores when definding the variable. i.e.
2 whitespaces: `Col__1_BACKTICK_QUOTED_STRING`.
10 whitespaces: `Col__________1_BACKTICK_QUOTED_STRING`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.21.3.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
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 : 0.15.0
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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https://api.github.com/repos/pandas-dev/pandas/issues/28894 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28894/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28894/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28894/events | https://github.com/pandas-dev/pandas/pull/28894 | 505,157,874 | MDExOlB1bGxSZXF1ZXN0MzI2NjQ0NjUw | 28,894 | def item( was broken | {
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} | [] | closed | false | null | [] | null | 3 | 2019-10-10T09:45:53Z | 2019-10-22T01:04:18Z | 2019-10-22T01:04:18Z | NONE | null | `pd.DataFrame({"a":[1,2,3]}).a.item() ` -- breaks!
`pd.DataFrame({"a":[1,2,3]}).a.values.item(0)` --- this is actual expected behaviour
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28895/events | https://github.com/pandas-dev/pandas/issues/28895 | 505,292,480 | MDU6SXNzdWU1MDUyOTI0ODA= | 28,895 | Create to_elastic method | {
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} | [] | closed | false | null | [] | null | 1 | 2019-10-10T14:01:11Z | 2019-10-10T15:12:27Z | 2019-10-10T15:12:27Z | NONE | null | ### Code Sample, a copy-pastable example if possible
### Problem description
Create method to export a pandas dataframe data to elasticsearch json format like to_excel, to_csv, to_sql methods.
### Input
df : pandas dataframe
index : str : elasticsearch index
type : str : elasticsearch type
EX :
df.head()
Name Age Weight
0 Kevin 26 94
1 Marc 32 70
2 Louis 35 80
index = 'personnes'
type = 'homme'
### Usage
df.to_elastic(file_path, 'index'=index, 'type'=type)
### Expected Output
{"index":{"_index": "personnes","_type":"homme","_id":1}}
{"fields" : {"Name" : "Kevin","Age" : 26,"Weight" : 94}
{"index" :{"_index": "personnes","_type":"homme","_id":2}}
{"fields" : {"Name" : "Marx","Age" : 32,"Weight" : 70}
{"index" :{"_index": "personnes","_type":"homme","_id":3}}
{"fields" : {"Name" : "Louis","Age" : 35,"Weight" : 80}
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https://api.github.com/repos/pandas-dev/pandas/issues/28896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28896/events | https://github.com/pandas-dev/pandas/issues/28896 | 505,328,202 | MDU6SXNzdWU1MDUzMjgyMDI= | 28,896 | Failing multiplication if both dataframes contain the (seemingly) identical categorical column | {
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"c... | closed | false | null | [] | null | 2 | 2019-10-10T14:57:25Z | 2020-08-19T12:30:10Z | 2020-08-19T12:30:10Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pop = pd.read_pickle('pop.pcl')
a = pd.read_pickle('rates_all.pcl')
b = pd.read_pickle('rates_main.pcl')
print('####### Dataframes #######')
print(pop)
print(a)
print(b)
print('\n####### Indices #######')
print(b.index.get_level_values(1))
print(a.index.get_level_values(1))
print(pop.index.get_level_values(0))
print('\n####### Output #######')
# the following works
print(pop.mul(b))
# the following fails
pop.mul(a)
```
Output:
```python
####### Dataframes #######
Age group 5-yrs to 90 PROVINCIE
0-4 Drenthe 801402
Flevoland 740024
Friesland 1118421
Gelderland 3443051
Groningen 907168
Name: Population, dtype: int32
All tumors Age group 5-yrs to 90
-1 0-4 0.000198
5-9 0.000112
10-14 0.000123
15-19 0.000211
20-24 0.000329
dtype: float64
Main tumor group Age group 5-yrs to 90
0 0-4 3.727047e-07
5-9 2.410106e-07
10-14 3.438238e-07
15-19 6.399917e-08
20-24 1.843772e-07
dtype: float64
####### Indices #######
b: Index([' 0-4', ' 5-9', '10-14', '15-19', '20-24'], dtype='object', name='Age group 5-yrs to 90')
a: CategoricalIndex([' 0-4', ' 5-9', '10-14', '15-19', '20-24'], categories=[' 0-4', ' 5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', ...], ordered=True, name='Age group 5-yrs to 90', dtype='category')
pop: CategoricalIndex([' 0-4', ' 0-4', ' 0-4', ' 0-4', ' 0-4'], categories=[' 0-4', ' 5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', ...], ordered=True, name='Age group 5-yrs to 90', dtype='category')
####### Output #######
Age group 5-yrs to 90 PROVINCIE Main tumor group
0-4 Drenthe 0 0.298686
Flevoland 0 0.275810
Friesland 0 0.416841
Gelderland 0 1.283241
Groningen 0 0.338106
5-9 NaN 0 NaN
10-14 NaN 0 NaN
15-19 NaN 0 NaN
20-24 NaN 0 NaN
dtype: float64
```
#### Problem description
The above code needs the following files:
[Analysis.zip](https://github.com/pandas-dev/pandas/files/3712866/Analysis.zip)
Running it leads to an error "TypeError: No matching signature found" in the last call.
The pop dataframe has a category column 'Age group 5-yrs to 90' in the multi-index. In dataframe b the index column 'Age group 5-yrs to 90' has type object. Multiplying both dataframes succeeds. Dataframe a also has a category column 'Age group 5-yrs to 90', but multiplying it with pop fails because it cannot find a matching signature.
#### Expected Output
I would expect the output of pop.mul(a) to be similar to the pop.mul(b) call, without an error.
#### 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 142 Stepping 10, 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 : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
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 : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 3 | 2019-10-10T15:48:17Z | 2019-10-12T18:27:57Z | 2019-10-12T17:14:22Z | MEMBER | null | There is some casting we currently do in many places in this function. Instead this PR implements `_ensure_dtype_type` and calls it once at the end of the function.
Removes _check_lossless_cast which can be replaced with np.can_cast | {
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} | 3 | 2019-10-10T16:15:23Z | 2019-11-07T21:21:43Z | 2019-11-07T21:21:43Z | MEMBER | null | mypy errors
```
pandas\core\frame.py:2742:23: error: Module has no attribute "_unpickle_array" [attr-defined]
pandas\core\frame.py:2744:17: error: Module has no attribute "_unpickle_array" [attr-defined]
pandas\core\frame.py:2751:17: error: Module has no attribute "_unpickle_array" [attr-defined]
pandas\core\frame.py:2752:51: error: Module has no attribute "_unpickle_array" [attr-defined]
pandas\core\frame.py:2757:45: error: Module has no attribute "_unpickle_array" [attr-defined]
```
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'0.26.0.dev0+521.g93183bab1'
>>>
>>> df = pd.DataFrame()
>>>
>>> state = df.__getstate__()
>>>
>>> state
{'_data': BlockManager
Items: Index([], dtype='object')
Axis 1: Index([], dtype='object'), '_typ': 'dataframe', '_metadata': []}
>>>
>>> df._unpickle_frame_compat(state)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas-simonjayhawkins\pandas\core\frame.py", line 2717, in _unpickle_frame_compat
columns = com._unpickle_array(cols)
AttributeError: module 'pandas.core.common' has no attribute '_unpickle_array'
>>>
```
we don't have any tests hitting DataFrame._unpickle_frame_compat or DataFrame._unpickle_matrix_compat
https://github.com/pandas-dev/pandas/blob/f625730eb1e729a3f58b213cb31ccf625307b198/pandas/core/frame.py#L2707-L2738
can this code be removed?
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https://api.github.com/repos/pandas-dev/pandas/issues/28899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28899/events | https://github.com/pandas-dev/pandas/pull/28899 | 505,389,847 | MDExOlB1bGxSZXF1ZXN0MzI2ODMzMTA4 | 28,899 | REF: simplify maybe_promote integer cases | {
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} | 2 | 2019-10-10T16:47:28Z | 2019-10-10T22:58:04Z | 2019-10-10T22:53:02Z | MEMBER | null | @jreback its a little absurd how much this can be simplified after all the trouble I went through to on the last round
Let's hope the float cases simplify as nicely (separate branch/PR) | {
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"default": f... | closed | false | null | [] | null | 17 | 2019-10-10T16:56:57Z | 2021-10-06T18:08:36Z | 2021-10-06T18:08:36Z | MEMBER | null | A lot of complexity in DataFrame._reduce/apply/groupby ops is driven by the numeric_only=None. In these cases we try to apply the reduction to non-numeric dtypes and if it fails, exclude them. This hugely complicated our exception handling.
We should consider removing that option and require users to subset the appropriate columns before doing their operations. | {
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} | 1 | 2019-10-10T16:57:20Z | 2021-03-25T01:55:01Z | 2021-02-20T21:07:11Z | NONE | null |
```python
import pandas as pd
import seaborn as sns
data = [['thing', 'T01', 0], ['thing2', 'T02', 5], ['thing3', 'T03', 25], ['thing', 'T01', 2]]
df = pd.DataFrame(data, columns=['Access', 'Access ID', 'Freq'])
cm = sns.light_palette("green", as_cmap=True)
df.style.set_caption('Access').background_gradient(cmap=cm, axis=0)
```
Currently I'm only able to apply background gradient to the 'freq' column however it would be ideal if it would colour the entire row.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### 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: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28902/events | https://github.com/pandas-dev/pandas/issues/28902 | 505,401,491 | MDU6SXNzdWU1MDU0MDE0OTE= | 28,902 | No metadata preventing installs of other packages | {
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} | [] | closed | false | null | [] | null | 6 | 2019-10-10T17:12:50Z | 2019-10-11T14:02:44Z | 2019-10-11T14:02:44Z | NONE | null | I'm trying to install a few packages (spacy, scattertext, etc) and keep getting this pandas error
```
No metadata found in ./anaconda3/lib/python3.7/site-packages
Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: '/Users/lmcquillan/anaconda3/lib/python3.7/site-packages/pandas-0.25.1.dist-info/METADATA'
```
Haven't been able to find a solution yet. I've tried uninstalling/reinstalling to no avail.
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} | 5 | 2019-10-10T19:35:31Z | 2020-07-07T11:09:10Z | 2020-07-07T11:09:10Z | NONE | null | #### Problem description
Code found here:
https://github.com/pandas-dev/pandas/blob/v0.25.1/pandas/plotting/_core.py#L424
Example from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.boxplot.html does not work properly.
#### Code Sample
```python
import itertools
tuples = [t for t in itertools.product(range(1000), range(4))]
index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
data = np.random.randn(len(index),4)
df = pd.DataFrame(data, columns=list('ABCD'), index=index)
grouped = df.groupby(level='lvl1')
boxplot_frame_groupby(grouped)
grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
boxplot_frame_groupby(grouped, subplots=False)
```
#### Resulting Error
```python
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-49-1a28c2f7125d> in <module>
5 df = pd.DataFrame(data, columns=list('ABCD'), index=index)
6 grouped = df.groupby(level='lvl1')
----> 7 boxplot_frame_groupby(grouped)
8 grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
9 boxplot_frame_groupby(grouped, subplots=False)
NameError: name 'boxplot_frame_groupby' is not defined
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https://api.github.com/repos/pandas-dev/pandas/issues/28904 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28904/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28904/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28904/events | https://github.com/pandas-dev/pandas/pull/28904 | 505,471,094 | MDExOlB1bGxSZXF1ZXN0MzI2ODk4MTk3 | 28,904 | clean tests/indexing/common.py | {
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} | 1 | 2019-10-10T19:40:09Z | 2019-10-11T16:07:25Z | 2019-10-11T12:00:15Z | CONTRIBUTOR | null | clean-up of tests/indexing/common.py. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28905 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28905/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28905/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28905/events | https://github.com/pandas-dev/pandas/issues/28905 | 505,471,489 | MDU6SXNzdWU1MDU0NzE0ODk= | 28,905 | Warning when plotting a DataFrame with a single row | {
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] | closed | false | null | [] | null | 4 | 2019-10-10T19:41:05Z | 2020-02-17T15:27:36Z | 2020-02-17T15:27:35Z | NONE | null | Closely related to #18306, item 9 in the listing, but probably not an exact duplicate. Based on StackOverflow question https://stackoverflow.com/q/58322744/2988730.
Simple code to reproduce:
```
import pandas as pd
df = pd.DataFrame([[1, 2, 3]], columns=['A', 'B', 'C'])
df.set_index('A', inplace=True)
df.plot()
```
This generates
```
UserWarning: Attempting to set identical left == right == 1.0 results in singular transformations; automatically expanding.
ax.set_xlim(left, right)
```
Expected behavior is that the default xlims of `[None, None]` will let matplotlib compute its own limits. Instead, it appears that pandas is computing the limits based on the min and max of the data. This seems redundant at best, and incorrect in this particular case. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28906/events | https://github.com/pandas-dev/pandas/issues/28906 | 505,483,922 | MDU6SXNzdWU1MDU0ODM5MjI= | 28,906 | read_json doesn't work on binary files with lines=True and chunksize | {
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"description": "read_jso... | open | false | null | [] | null | 6 | 2019-10-10T20:08:23Z | 2020-06-15T06:21:06Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
Using `test.jsonl` as the following:
{"test1":10}
{"test2":"hi"}
```python
with open('test.jsonl', 'rb') as f:
df = pd.read_json(f, lines=True, chunksize=1)
for chunk in df:
print(chunk)
```
#### Problem description
When I try to use `read_json` on a binary file with `lines=True` and `chunksize` set, I get the following error:
`TypeError: sequence item 0: expected str instance, bytes found`
It works when I remove the chunksize parameter. I'm guessing that the chunksize isn't taking into account the fact that the file is being read as a binary file.
#### Expected Output
I expect to be able to read a file using `lines=True` and `chunksize`
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.15.2
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
s3fs : 0.3.5
scipy : 1.2.0
sqlalchemy : 1.3.9
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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} | 2 | 2019-10-10T20:28:37Z | 2019-10-12T17:10:03Z | 2019-10-12T17:09:57Z | CONTRIBUTOR | null | … Number to new tests.
- [ X ] closes #28703
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} | 1 | 2019-10-10T21:33:51Z | 2019-10-11T12:41:24Z | 2019-10-11T12:41:20Z | MEMBER | null | - [ ] closes #28843
- [n/a ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [n/a ] whatsnew entry
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} | 0 | 2019-10-10T21:49:32Z | 2020-06-01T10:32:23Z | 2020-06-01T10:32:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> s = pd.Series([1, 0], dtype=np.uint8)
>>> s.diff()
0 NaN
1 255.0
dtype: float64
```
#### Problem description
The current version of the docs don't reflect on what happens when a Series or DataFrame containing unsigned ints are used with the `diff()` function, especially for cases where integer overflow occurs.
NumPy handles this in their docs for `numpy.diff()`, stating:
> For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly:
```python
>>> u8_arr = np.array([1, 0], dtype=np.uint8)
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
255
```
The behaviour of `pandas.Series.diff()` and `pandas.DataFrame.diff()` during calculation is the same as the NumPy equivalent, but the actual return type is no longer an unsigned integer; it will always return with a `float64` type.
#### Expected Output
The current output _can_ be expected when the user is perfectly aware that integer overflow might possibly occur during the calculation of the `diff()` output, even though it returns as a `float64` dtype. I do think it is desired to mention this in docs similarly to the NumPy equivalent though.
However, since the output dtype of `diff()` will always be `float64`, this also allows to handle the case differently: Instead of storing the output of the calculation:
https://github.com/pandas-dev/pandas/blob/0c4404b5b85c085e5b6cd26be42e81e5a197fd8d/pandas/core/algorithms.py#L1964
into a float64 dtyped array, it would also be possible to process the values as float instead of integers? This would generate the output:
```
0 NaN
1 -1.0
dtype: float64
```
I understand that this might go against the absolute difference of the elements as they are defined inside of the Series or DataFrame, but it would better fit the output type? Maybe just a warning for the overflow is the best middle ground instead? Would love to hear your thoughts on the case...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
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 : 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
</details>
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} | 1 | 2019-10-10T21:51:14Z | 2019-10-11T13:44:44Z | 2019-10-11T11:58:25Z | MEMBER | null | errors reported by mypy 0.730 on master.
```
pandas\compat\pickle_compat.py:71: error: Incompatible return type for "__new__" (returns "Series", but must return a subtype of "_LoadSparseSeries")
pandas\compat\pickle_compat.py:85: error: Incompatible return type for "__new__" (returns "DataFrame", but must return a subtype of "_LoadSparseFrame")
```
probably makes sense to apply these changes now to avoid ci breakage on mypy bump.
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"de... | closed | false | null | [] | null | 3 | 2019-10-10T22:00:27Z | 2019-11-05T22:10:26Z | 2019-11-05T22:10:26Z | MEMBER | null | AFAICT the only reason we need register_dtype is so that we can have a list of extent EA Dtypes to iterate over when doing is_extension_array_dtype. Is that inaccurate?
If so, let's just use `ExtensionDtype.__subclasses__()` and avoid some globals and decorators. | {
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"default": fa... | closed | false | null | [] | null | 2 | 2019-10-10T22:11:53Z | 2020-01-16T00:33:47Z | 2019-10-30T17:09:37Z | MEMBER | null | - [X] closes #12997
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This vendors updates from numpy that allow for tz-aware ISO date formatting. Note this does slightly change the behavior of UTC dates. Previously they would write out as `2013-01-01T05:00:00.000Z` but now are `2013-01-01T05:00:00.000+00:00`. Both are valid ISO 8601
There is a follow up that needs to be addressed with reading these dates | {
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