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https://api.github.com/repos/pandas-dev/pandas/issues/28313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28313/events | https://github.com/pandas-dev/pandas/issues/28313 | 490,269,206 | MDU6SXNzdWU0OTAyNjkyMDY= | 28,313 | Problem with MultiIndex upsampling / resampling | {
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... | 8 | 2019-09-06T11:08:45Z | 2021-07-11T17:39:08Z | null | NONE | null | #### Summary
Thanks for making Pandas I have used it in a lot of projects! But now I have a problem.
I have spent nearly 3 days trying to figure out how to **resample / upsample a Pandas MultiIndex** elegantly and correctly. I have read and tried numerous posts on StackOverflow and GitHub. My conclusion is that *... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28314/events | https://github.com/pandas-dev/pandas/pull/28314 | 490,311,350 | MDExOlB1bGxSZXF1ZXN0MzE0OTQxNTcz | 28,314 | ENH: add way to check for sparse data (#26706) | {
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- [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/28315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28315/events | https://github.com/pandas-dev/pandas/issues/28315 | 490,334,046 | MDU6SXNzdWU0OTAzMzQwNDY= | 28,315 | Add documentation section on Scaling | {
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While we continue to do work within pandas to improve scaling (fewer copies, native string dtype, etc.), we can document a few strategies that are availab... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28316/events | https://github.com/pandas-dev/pandas/pull/28316 | 490,357,295 | MDExOlB1bGxSZXF1ZXN0MzE0OTc5MTgx | 28,316 | DOC: Improve compiler installation instructions | {
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Note:
- For Linux, we have recommended to install a group of packages, along with GCC:
``sudo apt-get install build-esse... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28317/events | https://github.com/pandas-dev/pandas/issues/28317 | 490,387,994 | MDU6SXNzdWU0OTAzODc5OTQ= | 28,317 | Improve DataFrame.select_dtypes scaling to wide data frames | {
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```python
import numpy as np
import pandas as pd
from timeit import default_timer as tic
ns = [0, 10, 100, 1_000, 10_000]
times = []
for n in ns:
df = pd.DataFrame(np.random.randn(10, n))
t0 = tic()
df.select_dtypes(include='int')
t1 = tic... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28318/events | https://github.com/pandas-dev/pandas/issues/28318 | 490,407,559 | MDU6SXNzdWU0OTA0MDc1NTk= | 28,318 | .infer_objects() behavior is unintuitive when used with schemaless inputs | {
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```python
import pandas as pd
from io import StringIO
df = pd.concat([
pd.DataFrame({'a': ['a', 'b']}),
pd.DataFrame({'a': [1, 'c']}),
pd.DataFrame({'a': [2, 3]})
]).reset_index(drop=True)
#int64
print(df.loc[[2,4,5]].infer_objects().dtyp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28319/events | https://github.com/pandas-dev/pandas/pull/28319 | 490,407,579 | MDExOlB1bGxSZXF1ZXN0MzE1MDIwMzA1 | 28,319 | FIX: add additional condition to check invalid type(#28277) | {
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- [ ] 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/28320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28320/events | https://github.com/pandas-dev/pandas/pull/28320 | 490,411,461 | MDExOlB1bGxSZXF1ZXN0MzE1MDIzNDU0 | 28,320 | Added cpp files to build clean | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28321/events | https://github.com/pandas-dev/pandas/issues/28321 | 490,416,619 | MDU6SXNzdWU0OTA0MTY2MTk= | 28,321 | Remove customizations to isort formatting | {
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] | closed | false | null | [] | null | 6 | 2019-09-06T16:30:09Z | 2019-10-29T19:11:19Z | 2019-10-29T19:11:19Z | MEMBER | null | We customize the display of pandas imports in setup.cfg for isort. This is documented in the contributing guide:
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#import-formatting
To provide an example, isort would naturally order imports to be something like:
```python
from pandas.a... | {
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https://docs.python.org/3/howto/cporting.html | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28323/events | https://github.com/pandas-dev/pandas/issues/28323 | 490,448,318 | MDU6SXNzdWU0OTA0NDgzMTg= | 28,323 | `pivot_table` with `margins=True` shows `NaN` with `Period` columns | {
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```python
import pandas as pd
df = pd.DataFrame(
{
"a": [1, 1, 2, 2],
"b": [
pd.Period("2019Q1"),
pd.Period("2019Q2"),
pd.Period("2019Q1"),
pd.Period("2019Q2"),
],
"x": 1.0,
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28324 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28324/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28324/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28324/events | https://github.com/pandas-dev/pandas/pull/28324 | 490,539,805 | MDExOlB1bGxSZXF1ZXN0MzE1MTI3NDM5 | 28,324 | Clean groupby error message | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2019-09-06T22:01:37Z | 2019-09-12T01:10:16Z | 2019-09-07T17:18:54Z | MEMBER | null | Cleans an error message from `groupby.ops`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28325/events | https://github.com/pandas-dev/pandas/issues/28325 | 490,542,194 | MDU6SXNzdWU0OTA1NDIxOTQ= | 28,325 | read_json default date parsing behavior is unexpected | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28326/events | https://github.com/pandas-dev/pandas/pull/28326 | 490,547,499 | MDExOlB1bGxSZXF1ZXN0MzE1MTMzNzE0 | 28,326 | BUG: Fix FastParquetImpl.write for non-existent file | {
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"followers_url": "https://api.github.com/users/wesm... | 11 | 2019-09-06T22:32:14Z | 2019-10-09T21:24:02Z | 2019-09-19T15:46:14Z | CONTRIBUTOR | null | `PyArrowImpl` already correctly opens a non-existent file for writing (https://github.com/pandas-dev/pandas/blob/master/pandas/io/parquet.py#L95), with `engine='fastparquet'` this fails for e.g. a GCS URL (though it looks like S3 is already correct):
```
[nav] In [1]: pd.DataFrame().to_parquet('gs://city_data/test/bl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28327 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28327/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28327/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28327/events | https://github.com/pandas-dev/pandas/pull/28327 | 490,549,007 | MDExOlB1bGxSZXF1ZXN0MzE1MTM0OTQw | 28,327 | CLN: split_and_operate | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2019-09-06T22:39:05Z | 2019-09-07T17:28:23Z | 2019-09-07T17:17:32Z | MEMBER | null | In the branch I've got going that implements block-wise ops I found a bug in Block.split_and_operate. This doesn't fix that bug (since I don't have a test case that is independent of the rest of ops branch), just upstreams the cleanups that go along along with it. Small optimizations. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28328/events | https://github.com/pandas-dev/pandas/pull/28328 | 490,562,197 | MDExOlB1bGxSZXF1ZXN0MzE1MTQ1Mjk2 | 28,328 | CLN: eval_kwargs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28329/events | https://github.com/pandas-dev/pandas/issues/28329 | 490,562,789 | MDU6SXNzdWU0OTA1NjI3ODk= | 28,329 | DataFrame.corr(method="kendall") calculation is slow | {
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import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000, 300))
df.corr(method="kendall")
# 21.6 s ± 686 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
`DataFrame.corr(method="kendall")` doesn't scale particularly well, perhaps because it's the only named correlati... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28330/events | https://github.com/pandas-dev/pandas/issues/28330 | 490,587,647 | MDU6SXNzdWU0OTA1ODc2NDc= | 28,330 | groupby and resample methods do not preserve subclassed data structures | {
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```python
class MyDataFrame(pd.DataFrame):
@property
def _constructor(self):
return MyDataFrame
dates = pd.date_range('2019', freq='H', periods=1000)
my_df = MyDataFrame(np.arange(len(dates)), index=dates)
print(type(my_df))
# __main__.MyDataFrame (✓)
print(type(my_df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28331/events | https://github.com/pandas-dev/pandas/issues/28331 | 490,602,943 | MDU6SXNzdWU0OTA2MDI5NDM= | 28,331 | DataFrame resample and Grouper ,Why are the results different? | {
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... | 1 | 2019-09-07T07:18:41Z | 2021-07-11T17:40:34Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
data:
```python
idx = pd.date_range('2017-1-1','2018-12-31',freq='M')
df = pd.DataFrame(np.arange(len(idx)),idx)
```
resample:
```python
df.resample('3m',kind='period').sum()
```
use grouper:
```python
df.groupby(pd.Grouper(level=0,freq='3m',kind='... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28332/events | https://github.com/pandas-dev/pandas/issues/28332 | 490,637,091 | MDU6SXNzdWU0OTA2MzcwOTE= | 28,332 | Feature request: extend `pandas.DataFrame.replace` | {
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The following code works:
```python
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df.rename(columns=str.lower)
```
however the following ones do not work:
```python
df.rename(columns=str.upper.str.lower)
```
```python
df.rename(columns=str.upp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28333/events | https://github.com/pandas-dev/pandas/issues/28333 | 490,644,022 | MDU6SXNzdWU0OTA2NDQwMjI= | 28,333 | Unexpected TypeError using `pd.NamedAgg` dict with `pd.rolling` | {
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... | 4 | 2019-09-07T14:51:53Z | 2021-09-12T13:20:14Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
# (1) - doesn't work
animals.groupby("kind").rolling(1).agg(**{'total weight': pd.... | {
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] | closed | false | null | [] | null | 15 | 2019-09-07T19:01:13Z | 2019-09-13T18:35:12Z | 2019-09-13T18:35:12Z | CONTRIBUTOR | null | In preperation for https://github.com/pandas-dev/pandas/issues/27108
(disallowing duplicates), we need to enhance our metadata propagation.
*We need a way for a particiular attribute to deterimine how it's
propagated for a particular method*. Our current method of metadata
propagation lacked two features
1. It... | {
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```python
no code to show it's just the behavior of the csv_read method.
```
#### Problem description
If there's a dataframe that has a bunch of ints but a blank in between for example
> 1
> 2
> 3
>
> 4
pandas will turn it into
> 1.0
> 2.0
... | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2019-09-07T21:07:02Z | 2019-09-10T14:16:01Z | 2019-09-10T12:05:09Z | CONTRIBUTOR | null | - [x] closes #28301
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The old implementation used `arange` to generate codes for a non-multiindex, which ended up creating bad codes if the index was not unique. The fix... | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2019-09-08T03:25:31Z | 2019-09-10T12:06:25Z | 2019-09-10T12:06:25Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
idx1 = pd.MultiIndex.from_tuples(
[
("x", Period("2019Q1", "Q-DEC")),
("x", Period("2019Q2", "Q-DEC")),
]
)
idx2 = pd.MultiIndex.from_tuples(
[
("x", Period("2019Q1", "Q-DEC")),
("x", Period("201... | {
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"followers_url": "https://api.github.com/users/wesm... | 5 | 2019-09-08T11:26:51Z | 2019-09-11T17:17:09Z | 2019-09-11T15:51:34Z | CONTRIBUTOR | null | [pyjanitor](https://github.com/ericmjl/pyjanitor) is a quite nice pandas data cleaning library and gives its users some useful method on data frames. It's pipe oriented, so it can be helpful in data cleaning/transformation work.
Adding pyjanitor to the ecosystem page gives it visibility to our users and makes it eas... | {
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] | closed | false | null | [] | null | 19 | 2019-09-08T12:04:36Z | 2020-10-12T17:32:43Z | 2020-04-10T11:20:26Z | MEMBER | null | **tl;dr No need for review!**
no intention of merging this so using patterns that probably won't be accepted.
PR is for visibility and reference only.
using http://blog.zulip.org/2016/10/13/static-types-in-python-oh-mypy/ as a suitable template for a project plan. resolution of #27568 would come before too muc... | {
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- Unknown parameters {kwargs}
- Parameter "errors" description should finish with "."
- flake8 error: E126 continuation line over-indented for hanging indent
- flake8 error: E231 missing whitespace after ','
pandas.Series.astype: Unknown parameters {kwargs} in #27976 | {
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The tldr is that as long as we ship the cythonized code, our older sdists have no hope of working with newer Pythons. The have to be compiled with a modern enough Cython.
I think this makes even mor... | {
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] | closed | false | null | [] | null | 5 | 2019-09-08T21:06:59Z | 2019-10-15T12:08:52Z | 2019-09-14T23:15:54Z | NONE | null | Ndarray was converted to DataFrame times incorrectly, python3.7 was used, python3.6 was not reported until rolled back, and centos7 system was used
将ndarray转为DataFrame时报错了"No such keys(s): 'display.html.border'",使用的是python3.7,
回滚python3.6才没报错,用的centos7 | {
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(obviously the TODO comment will be removed/resolved before merge) | {
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closes #28337
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds a workaround to allow period indexes to be reindexed with non-period indexes. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28355 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28355/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28355/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28355/events | https://github.com/pandas-dev/pandas/issues/28355 | 490,869,822 | MDU6SXNzdWU0OTA4Njk4MjI= | 28,355 | pandas.compat StringIO return unexpected 'nan' under Python3 | {
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<details>
```python
# -*- coding: utf-8 -*-
import pandas as pd
from pandas.compat import StringIO
html_file_content = """#<table><tr>
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one",... | {
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[root@rc14e00h003 recon]# python
Python 2.7.5 (default, Jun 20 2019, 20:27:34)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-36)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
Traceback (most recent call last):
File... | {
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```python
import time
import pandas as pd
a = pd.Series([1, 1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T01:00:00+01:00'),
pd.Timestamp('2019-04-07T01:00:00+01:00')]))
a = a.resample('1s').sum() # ~5... | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"description": "hash_pandas_object",
... | open | false | null | [] | null | 4 | 2019-09-09T18:45:52Z | 2021-01-02T23:51:00Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from pandas.util import hash_pandas_object
x = pd.Series([np.nan])
print(x.data.hex())
expected = hash_pandas_object(x)
x1 = np.array([-np.sqrt(-1.)])
print(x1.data.hex())
hashed_x1 = hash_pandas... | {
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**pandas=0.24.2**
I uploaded the jupyter notebook file on [my GitHub account](https://github.com/yuriikoval1997/pandas0.24.2-has-a-problem-with-cyrillic/blob/master/pandas%20bugs.ipynb), so here I will be short.
For some reason, pandas.DataFrame.boxplot(by="class") method doesn't work correc... | {
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```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000, 300))
#Using Spearman method: On my computer about 12.19 seconds
df.corr(method = 'spearman')
#Using detour: On my computer about 0.72 seconds
rank_df = df.rank()
rank_df.corr(method = 'pearson')
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- [x] closes #28299
- [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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Currently it is only one way, for read_parquet it will come back as int or float (depending on presence of missing values), but fixing this is also being discussed (https://issues.apache.org/jira/browse/A... | {
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... | 5 | 2019-09-10T14:38:09Z | 2020-05-08T19:50:22Z | 2020-05-08T19:50:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': range(1000)})
df.rolling(5, center=True, win_type='triang').apply(lambda x: np.nanstd(x), raw=False)
```
#### Problem description
The above produces an error, stating that the `Window... | {
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Still needs some more tests and fixing corner cases.
We probably also want to consolidate the pyarrow import checking somewhat.
I think a main que... | {
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... | 10 | 2019-09-10T16:35:01Z | 2021-07-11T17:42:28Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
cat_df = pd.DataFrame()
cat_df['letters'] = ['a', 'b', 'c']
cat_df['letters'] = cat_df['letters'].astype('category')
cat_df['nums'] = [0, 1, 2]
cat_df['nums'] = cat_df['nums'].astype('category')
cat_df['count'] = 1
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- [x] xref: #11053, #28177
- [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/28374 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28374/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28374/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28374/events | https://github.com/pandas-dev/pandas/pull/28374 | 491,836,346 | MDExOlB1bGxSZXF1ZXN0MzE2MTI2OTY4 | 28,374 | BLD: Add pyproject.toml | {
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* Bumps minimum Cython version to the version supporting Python 3.8.
Closes https://github.com/pandas-dev/pandas/issues/20775
xref https://github.com/pandas-dev/pandas/issues/27435 (check if closes)
xref https://github.com/pandas-dev/pandas/pull/25227, https://github.com/p... | {
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S3 paths work for reading and writing CSV. However, I am not able to write json files using the to_json method. Reading json from an S3 path seems to work just fine.
```python
df.to_json(s3uri, orient='values')
```
#### Problem description
```python
-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28376 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28376/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28376/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28376/events | https://github.com/pandas-dev/pandas/issues/28376 | 491,892,748 | MDU6SXNzdWU0OTE4OTI3NDg= | 28,376 | How to fix your data returning Nan instead of float values in Python 3 for categorical data | {
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After grouping by, I tried to see what the data looks like and this is what I get. what can I do?
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
pd.set_option('display.max_rows', 500)
pd.set_option('display.ma... | {
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"description": "IO issue... | open | false | null | [] | null | 2 | 2019-09-10T21:04:36Z | 2020-05-02T04:33:59Z | null | CONTRIBUTOR | null | Split from https://github.com/pandas-dev/pandas/pull/27899. How should we handle options to be passed to the `open` call (like `encoding`, `errors`).
A few options
* pass through `**kwargs`
* a "storage_options" kwarg that's a dict with some specification
Want to thing about things like S3, etc. too. | {
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>>> import pandas.util.testing as tm
>>> tm.makeObjectSeries()
...
t1Q4jcW3nW 2000-02-09
ElZCr3WTz2 2000-02-10
p43Nqg7u4D 2000-02-11
dtype: datetime64[ns]
```
@jbrockmendel and I were both surprised that calling `makeObjectSeries` returns a Series with a datetime dtype. I think it w... | {
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] | open | false | null | [] | null | 2 | 2019-09-11T01:24:19Z | 2019-09-11T16:28:40Z | null | CONTRIBUTOR | null | Related to #28333 and #26513
There are several helper methods to help with named aggregations, that are found in [`pandas.core.groupby.generic`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py), namely:
- [`_is_multi_agg_with_relabel`](https://github.com/pandas-dev/pandas/blob/mas... | {
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] | closed | false | null | [] | null | 2 | 2019-09-11T01:41:10Z | 2019-09-11T17:31:59Z | 2019-09-11T17:30:23Z | NONE | null | #### This is probably some configuration issue on my end.
```python
Traceback (most recent call last):
File ".\utils.py", line 70, in <module>
sql_to_hdf5(chunked_dfs)
File ".\utils.py", line 27, in sql_to_hdf5
store = pd.HDFStore(file_path)
File "C:\Users\J68531\AppData\Local\Continuum\anaconda... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28382/events | https://github.com/pandas-dev/pandas/issues/28382 | 491,980,987 | MDU6SXNzdWU0OTE5ODA5ODc= | 28,382 | Update cpython imports in Cython Code | {
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... | 0 | 2019-09-11T02:15:40Z | 2019-09-13T00:56:41Z | 2019-09-13T00:56:41Z | MEMBER | null | Looking at the Cython source there is a big deprecation warning about doing `from cpython cimport ...`
https://github.com/cython/cython/blob/cd031618700bf8293818729ee07bfc855fb424a2/Cython/Includes/cpython/__init__.pxd#L125
We tend to import things that way, so I think would be nice to clean up our imports to be ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28383/events | https://github.com/pandas-dev/pandas/issues/28383 | 492,026,269 | MDU6SXNzdWU0OTIwMjYyNjk= | 28,383 | Pandas get_dummies validate "columns" input | {
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... | 13 | 2019-09-11T05:35:00Z | 2019-10-22T01:31:31Z | 2019-10-22T01:31:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import string, pandas
p_csv = pandas.read_csv(my_dir/myFile), index_col=0)
sepanames = sorted(p_csv["SEPARATOR"].unique())
for i in range(0, 14):
print(i)
col = p_csv.columns.get_loc("SEPARATOR") + 1 + i
p_csv.insert(col, "SEPARATOR_" + se... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28384 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28384/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28384/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28384/events | https://github.com/pandas-dev/pandas/issues/28384 | 492,134,439 | MDU6SXNzdWU0OTIxMzQ0Mzk= | 28,384 | Categorical equality with NaN behaves unexpectedly | {
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```python
import pandas as pd
cat_type = pd.CategoricalDtype(categories=["a", "b", "c"])
s1_cat = pd.Series(["a", "b", "c"], dtype=cat_type)
s2_cat = pd.Series([np.nan, "a", "b"], dtype=cat_type)
s1_cat != s2_cat # expected all True
```
Output:
```
0 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28385/events | https://github.com/pandas-dev/pandas/issues/28385 | 492,146,792 | MDU6SXNzdWU0OTIxNDY3OTI= | 28,385 | Nullable Int64 column changes type after some (cumsum) operations | {
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... | 6 | 2019-09-11T10:19:00Z | 2021-07-12T03:16:43Z | null | CONTRIBUTOR | null | #### Code
```python
import pandas as pd
s1 = pd.Series([1, 2, 3, 4], dtype='Int64')
s2 = pd.Series([1, 2, float('nan'), 3, 4], dtype='Int64')
cs1 = s1.cumsum()
cs2 = s1.cumsum()
print(str(s1.dtype), str(cs1.dtype))
print(str(s2.dtype), str(cs2.dtype))
```
#### Output
```
Int64 object
Int64 object
... | {
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pd.to_datetime with out-of-bounds date strings
As discussed briefly in #28367
- [x] closes #9107 ? the issue is no longer truly reproducible, this PR adds a test around the new behaviour
- [x] tests added / passed
- [x] passes `black pandas`... | {
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] | closed | false | null | [] | null | 14 | 2019-09-11T16:00:47Z | 2021-11-20T23:21:59Z | 2019-11-02T03:09:57Z | MEMBER | null | Discussed [here](https://github.com/jbrockmendel/pandas/pull/3)
Change the EA interface to require `size` instead of `__len__`.
Doing this separately from #27142 makes it much simpler to explain to downstream authors what is changing for them. After this, the rest of the changes necessary for (limited) 2D suppor... | {
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```python
import pandas as pd
d = {('East',1):{'Home':'MIL','Away':'BOS','Winner':'MIL'},
('East',2):{'Home':'PHI','Away':'IND','Winner':'PHI'},
('East',3):{'Home':'MIL','Away':'PHI','Winner':'PHI'},
('West',1):{'Home':'HOU','Away':'UTA','Winner':'HOU'},
('West',2):{'Home... | {
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with new enough versions of Cython. This is the latest current release.
xref https://github.com/pandas-dev/pandas/issues/28341 | {
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When set (via the construction with `allow_d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28395 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28395/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28395/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28395/events | https://github.com/pandas-dev/pandas/pull/28395 | 492,492,241 | MDExOlB1bGxSZXF1ZXN0MzE2NjU1ODE3 | 28,395 | REF: prepare Series logical op to be refactored to array op | {
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Change to `fill_bool` makes it work on `ndarray` instead of `Series`.
`other = lib.item_from_zerodim` is added for consisten... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28396/events | https://github.com/pandas-dev/pandas/pull/28396 | 492,494,176 | MDExOlB1bGxSZXF1ZXN0MzE2NjU3MzEy | 28,396 | REF: prepare Series comparison op to be refactored to array op | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28397/events | https://github.com/pandas-dev/pandas/issues/28397 | 492,551,448 | MDU6SXNzdWU0OTI1NTE0NDg= | 28,397 | Alignment of CategoricalIndex will convert the index to int type? | {
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```python
import pandas as pd
idx1 = pd.Categorical(['A1', 'A2', 'A2'], categories=['A1', 'A2'])
idx2 = pd.Categorical(['A2', 'A1', 'A1'], categories=['A1', 'A2'])
tmp1 = pd.DataFrame(np.random.randn(6).reshape(3, 2), index=idx1)
tmp2 = pd.DataFrame(np.random.randn(6).reshape(3, 2), index=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28398 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28398/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28398/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28398/events | https://github.com/pandas-dev/pandas/pull/28398 | 492,563,925 | MDExOlB1bGxSZXF1ZXN0MzE2NzEyMjEz | 28,398 | CLN: Update cpython imports in Cython Code GH28382 | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Apologies if anything is not ideal, as this is my first pull request here. Any suggestions or comments are much appreciated! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28399/events | https://github.com/pandas-dev/pandas/pull/28399 | 492,585,718 | MDExOlB1bGxSZXF1ZXN0MzE2NzI5NzY5 | 28,399 | BUG: Fix Series(List[Interval]) to infer interval dtype | {
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- [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/28400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28400/events | https://github.com/pandas-dev/pandas/pull/28400 | 492,609,598 | MDExOlB1bGxSZXF1ZXN0MzE2NzQ4OTI1 | 28,400 | Fix a `np.random.np.random` typo in "computation.rst" in document. | {
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... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28401/events | https://github.com/pandas-dev/pandas/issues/28401 | 492,622,799 | MDU6SXNzdWU0OTI2MjI3OTk= | 28,401 | BUG: Constructing a Series from a scalar generally doesn't work for extension types | {
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Construction without specifying a dtype results in `object` dtype for `Interval` and `Period`:
```python-traceback
In [2]: pd.Series(pd.Interval(0, 1), index=range(3))
Out[2]:
0 (0, 1]
1 (0, 1]
2 (0, 1]
dtype: object
In [3]: pd.Series(pd.Period(... | {
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This is probably an issue with the latest openssl from Anaconda, see https://github.com/ContinuumIO/anaconda-issues/issues/11274
But opening just to have an issue to refer to. We can certai... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28403/events | https://github.com/pandas-dev/pandas/issues/28403 | 492,658,527 | MDU6SXNzdWU0OTI2NTg1Mjc= | 28,403 | NaN is converted to strings when reassigning a column with .loc | {
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... | 2 | 2019-09-12T08:24:42Z | 2019-10-21T19:34:22Z | null | NONE | null | Hello team,
Here is a simple working exemple of value assignment on a dataframe:
```
df = pd.DataFrame({'A': [np.nan, np.nan, 'b']})
df['A'].loc[[0, 1]] = ['a', np.nan]
df.notna()
Out[126]:
A
0 True
1 False
2 True
```
However, if we use loc to assign the entire column with an array includin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28404 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28404/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28404/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28404/events | https://github.com/pandas-dev/pandas/pull/28404 | 492,663,504 | MDExOlB1bGxSZXF1ZXN0MzE2NzkxNTYy | 28,404 | TMP: pin openssl for doc build | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28405 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28405/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28405/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28405/events | https://github.com/pandas-dev/pandas/pull/28405 | 492,704,418 | MDExOlB1bGxSZXF1ZXN0MzE2ODI0ODY2 | 28,405 | Revert "TMP: pin openssl for doc build" | {
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Closes https://github.com/pandas-dev/pandas/issues/28402 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28406 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28406/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28406/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28406/events | https://github.com/pandas-dev/pandas/issues/28406 | 492,718,874 | MDU6SXNzdWU0OTI3MTg4NzQ= | 28,406 | Converting from categorical to int ignores NaNs | {
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"followers_url": "https://api.github.com/users/wesm... | 10 | 2019-09-12T10:25:07Z | 2019-09-18T12:35:04Z | 2019-09-18T12:35:04Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [6]: s = pd.Series([1, 0, None], dtype='category')
In [7]: s... | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2019-09-12T11:37:33Z | 2019-09-18T14:02:12Z | 2019-09-18T14:02:12Z | MEMBER | null | mypy error: `pandas\core\sparse\frame.py:94:25: error: Item "SparseArray" of "Union[SparseSeries, SparseArray]" has no attribute "index"`
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'0.25.0+332.g261c3a667'
>>> sa = pd.SparseArray([1,2,3])
>>> sa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28408/events | https://github.com/pandas-dev/pandas/issues/28408 | 492,751,169 | MDU6SXNzdWU0OTI3NTExNjk= | 28,408 | Series.dt.round fails if fraction is exactly 0.5 | {
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```python
rng = pd.date_range('1/1/2018 11:59:00.050', periods=10, freq='100ms')
rng
>>>DatetimeIndex(['2018-01-01 11:59:00.050000', '2018-01-01 11:59:00.150000',
'2018-01-01 11:59:00.250000', '2018-01-01 11:59:00.350000',
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28409 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28409/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28409/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28409/events | https://github.com/pandas-dev/pandas/issues/28409 | 492,753,930 | MDU6SXNzdWU0OTI3NTM5MzA= | 28,409 | DEPR: Move rarely used I/O connectors to third party modules | {
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"... | closed | false | null | [] | null | 15 | 2019-09-12T11:45:43Z | 2020-09-24T09:56:13Z | 2020-09-24T09:56:13Z | MEMBER | null | Related to #26804 (and indirectly related to #15862, #15008 if we standardize I/O connectors as part of this)
The recent survey shows that several of our I/O connectors (Stata, SPSS, SAS and GBQ) are rarely used (from the chart I'd say around 3% of respondents said they used them):
![pandas_survey_io](https://us... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28410 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28410/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28410/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28410/events | https://github.com/pandas-dev/pandas/issues/28410 | 492,771,318 | MDU6SXNzdWU0OTI3NzEzMTg= | 28,410 | Series.append raises TypeError with tuple of Series | {
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```
pandas\core\series.py:2733:25: error: Unsupported operand types for + ("List[Any]" and "Tuple[Any, ...]")
pandas\core\series.py:2733:25: note: Right operand is of type "Union[List[Any], Tuple[Any, ...]]"
```
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as... | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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