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https://api.github.com/repos/pandas-dev/pandas/issues/10703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10703/events | https://github.com/pandas-dev/pandas/pull/10703 | 98,157,572 | MDExOlB1bGxSZXF1ZXN0NDEyMjc3Mjk= | 10,703 | BUG: Bug in Index construction with a mixed list of tuples #10697) | {
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``` python
In [8]: import pandas.util.testing as tm
In [9]: s = pd.Series(tm.makeCategoricalIndex(k=100))
In [10]: s.value_counts()
Out[10]:
vcKH 40
vXR9 31
Zn8J 29
dtype: int64
In [11]: s.value_counts().index
Out[11]: Index(['vcKH', 'vXR9', 'Zn8J'], dtype='object')
```
Now that we have ... | {
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"descript... | closed | false | null | [] | null | 6 | 2015-07-30T18:58:10Z | 2015-07-30T20:56:50Z | 2015-07-30T19:04:36Z | NONE | null | Hello,
I have another issue with tuples. If you export and import a DataFrame to csv the tuples headers are transformed to string.
Here is an example of test:
``` python
import pandas as pd
from StringIO import StringIO
df_out = pd.DataFrame()
df_out['A'] = ['foo']
df_out['B', 1] = ['bar']
sio_out = StringIO()
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... | 3 | 2015-07-30T20:49:22Z | 2017-09-10T14:08:40Z | 2017-09-10T14:08:32Z | NONE | null | The output columns from `get_dummies()` are binary by nature, changing this default from float64 to int8 would save 7 bytes per dummy.
```
df=pd.DataFrame({ 'x':[1,2,3] })
pd.get_dummies(df.x)
```
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'black' color was hard-coded for vertical lines, I suggest to make it an option
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In [40]: cols = pd.MultiIndex.from_product([['A', 'B', 'C'],[1,2]])
In [41]: df = pd.DataFrame(np.random.randn(5,6), columns=cols)
In [42]: df.loc[:, ['B', 'A']]
Out[42]:
A B
1 2 1 2
0 -0.990868 1.577803 1.023247 -0.423165
1 -1.180170 1.236143 -1.48... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10712/events | https://github.com/pandas-dev/pandas/issues/10712 | 98,417,470 | MDU6SXNzdWU5ODQxNzQ3MA== | 10,712 | BUG: Series.dt ops reset name | {
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```
import pandas as pd
idx = pd.date_range('2011-01-01', periods=5, freq='M', name='x')
idx
# DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30', '2011-05-31'],
# dtype='datetime64[ns]', name=u'x', freq='M', tz=None)
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https://api.github.com/repos/pandas-dev/pandas/issues/10713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10713/events | https://github.com/pandas-dev/pandas/issues/10713 | 98,418,455 | MDU6SXNzdWU5ODQxODQ1NQ== | 10,713 | BUG/API: Categorical doesn't support categories with tz | {
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```
import pandas as pd
idx = pd.date_range('2011-01-01', periods=5, freq='M', tz='US/Eastern')
idx
# DatetimeIndex(['2011-01-31 00:00:00-05:00', '2011-02-28 00:00:00-05:00',
# '2011-03-31 00:00:00-04:00', '2011-04-30 00:00:00-04:00',
#... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10714/events | https://github.com/pandas-dev/pandas/pull/10714 | 98,489,815 | MDExOlB1bGxSZXF1ZXN0NDEzOTA5MDQ= | 10,714 | updating account info | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10715/events | https://github.com/pandas-dev/pandas/issues/10715 | 98,492,684 | MDU6SXNzdWU5ODQ5MjY4NA== | 10,715 | DOC: limit error traceback to one line for expected exceptions | {
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... | 3 | 2015-08-01T00:25:57Z | 2019-10-21T19:52:15Z | null | MEMBER | null | xref https://github.com/pydata/pandas/pull/10674#issuecomment-126837519
When using `:okexcept:`, limit the traceback to one line (or as a separate option + try to push to ipython upstream afterwards)
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https://api.github.com/repos/pandas-dev/pandas/issues/10716 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10716/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10716/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10716/events | https://github.com/pandas-dev/pandas/pull/10716 | 98,498,248 | MDExOlB1bGxSZXF1ZXN0NDEzOTQzMjM= | 10,716 | BUG: Series.align with MultiIndex may be inverted | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-08-01T01:56:20Z | 2015-08-18T11:22:55Z | 2015-08-18T10:51:57Z | MEMBER | null | Closes #10665. Closes #10741.
I'm adding some more tests for `Index.join`, and found a skeptic behavior.
For `right` join, all levels / labels of right side remains (OK).
```
import pandas as pd
midx = pd.MultiIndex.from_product([[0, 1], [0, 1, 2]], names=['a', 'b'])
idx = pd.Index([1, 2 ,3], name='b')
idx.join(mid... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10718/events | https://github.com/pandas-dev/pandas/pull/10718 | 98,541,976 | MDExOlB1bGxSZXF1ZXN0NDE0MDI2NjA= | 10,718 | BUG: Categorical doesn't show tzinfo properly | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-08-01T14:48:50Z | 2015-08-08T19:44:00Z | 2015-08-08T19:43:55Z | MEMBER | null | Closes #10713.
- [x] Check timedelta
- [x] Refactor not to import `DatetimeIndex` and `PeriodIndex` if possible.
- [x] Fix `categories` line break location
- [x] Check `Categorical` `Series` formatting
- [x] Check `Categorical` `DataFrame` formatting
- [x] Fix `CategoricalIndex` formatting
## CategoricalIndex problem... | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-08-01T15:57:29Z | 2015-08-02T21:17:47Z | 2015-08-02T17:07:09Z | CONTRIBUTOR | null | closes #10711
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... | 14 | 2015-08-01T16:01:55Z | 2020-07-02T09:26:32Z | null | CONTRIBUTOR | null | exceptions that are raised on unsuccessful datetime/timedelta parsing should add this:
`you can coerce to NaT by passing errors='coerce'`
comment at the end: https://github.com/pydata/pandas/pull/10674
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https://api.github.com/repos/pandas-dev/pandas/issues/10721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10721/events | https://github.com/pandas-dev/pandas/pull/10721 | 98,560,437 | MDExOlB1bGxSZXF1ZXN0NDE0MDc1OTk= | 10,721 | Clarify docstring: Series.sort_index() creates new instance (rather than sorting in-place) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10722/events | https://github.com/pandas-dev/pandas/issues/10722 | 98,560,781 | MDU6SXNzdWU5ODU2MDc4MQ== | 10,722 | "value is trying to be set on a copy of a slice from a DataFrame" errors? | {
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"color": "00... | closed | false | null | [] | null | 7 | 2015-08-01T20:49:57Z | 2015-08-01T21:38:33Z | 2015-08-01T21:12:42Z | CONTRIBUTOR | null | I'm (consistently) getting a `value is trying to be set on a copy of a slice...` error on the following bit of code, and can't figure out why:
```
i['gender'] = i.gender.replace({'male':'m', 'female':'f'})
```
The full error:
```
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10723/events | https://github.com/pandas-dev/pandas/pull/10723 | 98,604,288 | MDExOlB1bGxSZXF1ZXN0NDE0MTY1MzI= | 10,723 | BUG: concat of Series w/o names #10698 | {
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Let the result of 'concat' to inherit the parent Series' names. The Series' name (if present) will be used as the resulting DataFrame column name. When only one of the Series has a valid name, the resulting DataFrame will inherit the name only, and use a column name for the other columns the column inde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10724/events | https://github.com/pandas-dev/pandas/pull/10724 | 98,605,407 | MDExOlB1bGxSZXF1ZXN0NDE0MTY4MjI= | 10,724 | BUG: pd.unique should respect datetime64 and timedelta64 dtypes (GH9431) | {
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Previously `pd.unique` would return an array of `object` dtype when passed a 1D array, Series or Index with a `datetime64` or `timedelta64` dtype. This PR should remedy that behaviour.
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https://api.github.com/repos/pandas-dev/pandas/issues/10725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10725/events | https://github.com/pandas-dev/pandas/issues/10725 | 98,612,692 | MDU6SXNzdWU5ODYxMjY5Mg== | 10,725 | COMPAT: see if we can use EnumCol to store Categoricals in HDFStore | {
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... | 1 | 2015-08-02T14:25:47Z | 2021-04-18T23:08:00Z | null | CONTRIBUTOR | null | http://www.pytables.org/usersguide/libref/declarative_classes.html#the-atom-class-and-its-descendants
- would have to ensure back-compat reading.
- `order` property would have to be retained in column meta data (IIRC this is how its done now)
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https://api.github.com/repos/pandas-dev/pandas/issues/10726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10726/events | https://github.com/pandas-dev/pandas/pull/10726 | 98,622,048 | MDExOlB1bGxSZXF1ZXN0NDE0MjA4MTQ= | 10,726 | API/WIP: .sorted | {
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closes #8239
```
Changes to sorting API
^^^^^^^^^^^^^^^^^^^^^^
The sorting API has had some longtime inconsistencies. (:issue:`9816`,:issue:`8239`).
Here is a summary of the **prior** to 0.17.0 API
- ``Series.sort`` is **INPLACE** while ``DataFrame.sort`` returns a new object.
- ``Series.order`` retur... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10727/events | https://github.com/pandas-dev/pandas/pull/10727 | 98,640,300 | MDExOlB1bGxSZXF1ZXN0NDE0MjUzNjA= | 10,727 | DOC: limit error traceback to one line for expected exceptions (GH10715) | {
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This gives you an output like
```
In [4]: s = pd.Series([1,3,5,np.nan,6,8])
In [5]: s.iloc[11]
---------------------------------------------------------------------------
IndexError: single positional indexer is out-of-bounds
```
for input
```
.. ipython:: python
:okexcept:
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data = "1,2,3\n4,5,6\n "
print(pandas.read_csv(pandas.compat.StringIO(data), skip_blank_lines=True))
1 2 3
0 4 5 6
1 NaN NaN NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10729/events | https://github.com/pandas-dev/pandas/pull/10729 | 98,657,836 | MDExOlB1bGxSZXF1ZXN0NDE0MzA3MDc= | 10,729 | API: CategoricalIndex for value_counts | {
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Changes `Categorical.value_counts` to return
a Series with a CategoricalIndex. Previously
the Series and an Index.
Just some random observations on the convenience of working with categoricals:
The code here could be simplified if
1. `Categorical.categories` was al... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10730/events | https://github.com/pandas-dev/pandas/issues/10730 | 98,687,322 | MDU6SXNzdWU5ODY4NzMyMg== | 10,730 | Add inplace option to DataFrame.update() | {
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... | 7 | 2015-08-03T07:28:59Z | 2021-10-29T02:17:14Z | null | NONE | null | Currently `DataFrame.update()` modifies the dataframe in place. I feel this is not symmetric to how most other methods work, e.g. `set_index()`, that returns a new `DataFrame`. It is probably too late to change this default behavior (isi it?), but if at the very least an `inplace` option was added (with default value `... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10731/events | https://github.com/pandas-dev/pandas/pull/10731 | 98,720,618 | MDExOlB1bGxSZXF1ZXN0NDE0NTY0MDg= | 10,731 | TST: better testing for io/data | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10732/events | https://github.com/pandas-dev/pandas/issues/10732 | 98,726,501 | MDU6SXNzdWU5ODcyNjUwMQ== | 10,732 | DataFrame msgpack objects written from linux cannot be read on windows | {
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```
df2 = pandas.DataFrame([['100']], index=[1], columns=['v'])
df2.to_msgpack(r"/mnt/C/temp/p2.msgpack")
```
then on windows:
```
df = pandas.read_msgpack(r"C:\temp\p2.msgpack")
ValueError: Shape of passed values is (1, 1), indices imply (1, 2)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10733/events | https://github.com/pandas-dev/pandas/pull/10733 | 98,737,602 | MDExOlB1bGxSZXF1ZXN0NDE0NjQxMTI= | 10,733 | DOC: to_datetime outdated example | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-08-03T12:58:13Z | 2015-08-05T08:12:35Z | 2015-08-05T08:12:32Z | MEMBER | null | With the new default of `errors='raise'`, the following section in the docs is not fully correctly phrased anymore: http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#invalid-data.
So I just removed that example of `to_datetime([1, '1'])` (as it now raises, which is less unexpected, and the new example a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10734/events | https://github.com/pandas-dev/pandas/issues/10734 | 98,743,681 | MDU6SXNzdWU5ODc0MzY4MQ== | 10,734 | DEPR: DataFrame.combine propagates nan values | {
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... | 17 | 2015-08-03T13:33:28Z | 2021-04-18T23:09:12Z | null | NONE | null | The documentation for DataFrame.combine claims that the method "do[es] not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame’s value". However, this does not seem to correspond to the actual behaviour of DataFrame.combine.
Sample code:
``` python
>>> imp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10735/events | https://github.com/pandas-dev/pandas/issues/10735 | 98,751,783 | MDU6SXNzdWU5ODc1MTc4Mw== | 10,735 | API: deprecate combineAdd and combineMult methods? | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-08-03T14:04:59Z | 2015-08-16T08:28:53Z | 2015-08-16T08:28:53Z | MEMBER | null | On a DataFrame, we have the [`combineAdd`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.combineAdd.html) and [`combineMult`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.combineMult.html) methods:
- they are a redundant, as they are defined as `self.add(other, fill_va... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10736/events | https://github.com/pandas-dev/pandas/issues/10736 | 98,806,711 | MDU6SXNzdWU5ODgwNjcxMQ== | 10,736 | BUG?: .sample() sometimes returning a view not a copy. | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-08-03T18:06:24Z | 2015-08-19T20:52:33Z | 2015-08-19T20:52:33Z | CONTRIBUTOR | null | I've realized the `.sample()` function is sometimes returning a view rather than a copy. This will cause the "setting on a view/copy" error:
```
df = pd.DataFrame({'col1':[5,6,7], 'col2':['a','b','c']})
a_sample = df.sample(1)
a_sample['col1'] = 'z'
```
I believe the behavior is the result of it using the `.take()` f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10737/events | https://github.com/pandas-dev/pandas/issues/10737 | 98,815,894 | MDU6SXNzdWU5ODgxNTg5NA== | 10,737 | pandas.date_range documentation does not have link to acceptable 'freq' values | {
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There is a highly-upvoted Stack Overflow question entitled... | {
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- Makes sure to return copies, not views.
- makes sure if a separate series is passed as weights that the axis are aligned before arguments get converted into numpy arrays and handled positionally
closes #10736
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The dtypes of the DataFrame returned by `read_stata` have the same order as the original stata file, even if `columns` are specified with a different order.
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https://api.github.com/repos/pandas-dev/pandas/issues/10740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10740/events | https://github.com/pandas-dev/pandas/pull/10740 | 98,828,576 | MDExOlB1bGxSZXF1ZXN0NDE1MDQyMTU= | 10,740 | ENH: Fixed DF.apply for functions returning a dict, #8735 | {
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Previously, when the function argument to DataFrame.apply returned a dict, the reduction code would mistake its "values" property for the values of a Pandas Series, and return a Series of "values" instance methods. The new check ensures that the "values" property is an np.ndarray.
Previous behavior:
``... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10741 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10741/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10741/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10741/events | https://github.com/pandas-dev/pandas/issues/10741 | 98,834,450 | MDU6SXNzdWU5ODgzNDQ1MA== | 10,741 | BUG: Joining single-index DataFrame to multiindex DF incorrect for how=left and how=right | {
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```
df1 = pd.DataFrame([... | {
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closes #10732
I wasn't able to reproduce the odd behavior. Let's see if this passes on Travis.
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Previously only non-relative offsets that could be mapped to a time_delta (`Tick`, `Day`, etc) worked.
Some performance numbers are below - `Tick` based offsets should b... | {
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```
writing build\temp.win-amd64-2.7\Release\pandas\index.def
C:\Program Files\mingw-builds\x64-4.8.1-posix-seh-rev5\mingw64\bin\dllwrap.exe -DMS_WIN64 -mdll -static --entry _DllMain@12 --output-lib build\temp.win-amd64-2... | {
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```
import pandas as pd
df = pd.DataFrame([1036713600000], dtype='float64')
print(df[0].astype('datetime64[ms]'))
```
Output:
```
0 2002-11-08
Name: 0,... | {
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```
In [1]: data = '''
...: [{"id": 1, "timestamp": 1036713600000},
...: {"id": 2}]
...: '''
```
after #10776 this is ok
```
In [2]: pd.read_json(StringIO(data), orient='records', dtype={'id': 'int32', 'timestamp': 'datetime64[ms]'})
Out[2]:
id tim... | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-08-04T17:50:43Z | 2015-09-01T19:18:15Z | 2015-09-01T19:18:15Z | CONTRIBUTOR | null | This came out of work on https://github.com/pydata/pandas/pull/10729
In the [documentation](http://pandas.pydata.org/pandas-docs/version/0.16.2/categorical.html#missing-data), we mention that
> There are two ways a np.nan can be represented in categorical data: either the value is not available (“missing value”) or n... | {
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... | 2 | 2015-08-04T19:07:30Z | 2015-08-12T09:31:26Z | 2015-08-12T09:31:26Z | CONTRIBUTOR | null | `pd.read_sql_table(tablename, engine)` won't be able to find `tablename` if it's actually a view.
This should be a one line change to https://github.com/pydata/pandas/blob/b281e6570bc3bb3773520eff0b6965d5691e3ba2/pandas/io/sql.py#L340
``` python
meta = MetaData(con, schema=schema)
try:
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In [1]: import pandas as pd
In [2]: i = pd.Index(range(10))
In [3]: pd.Series.count(i)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-759ac5e97e30> in <module>()
----> 1 pd.Series.count... | {
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] | closed | false | null | [] | null | 6 | 2015-08-05T17:43:32Z | 2016-09-10T13:42:34Z | 2016-09-10T13:42:34Z | NONE | null | https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L102
Why is this in the gbq.py? can we set this as an environmental Variable?
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"i... | open | false | null | [] | null | 2 | 2015-08-05T22:31:10Z | 2020-04-14T05:11:24Z | null | NONE | null | Writing a DataFrame with utf-16 encoding adds garbage characters to the file:
``` python
import codecs
import pandas as pd
#This works
enc = 'utf-8'
print '\n\n',enc,'\n\n'
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.D... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10756/events | https://github.com/pandas-dev/pandas/issues/10756 | 99,393,659 | MDU6SXNzdWU5OTM5MzY1OQ== | 10,756 | Pandas groupby histogram doesn't do transparant bars (alpha kwarg) | {
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] | closed | false | null | [] | null | 16 | 2015-08-06T09:17:49Z | 2017-10-30T16:38:09Z | 2017-08-22T14:18:55Z | NONE | null | I would expect the following code to produce a plot with transparent bars, but it doesn't.
``` python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('ggplot')
N = 1000
df = pd.DataFrame({
'a': np.random.randint(0, 2, N),
'b': np.random.random(N)
})
#df.groupby('a').b.hist(a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10757/events | https://github.com/pandas-dev/pandas/pull/10757 | 99,424,029 | MDExOlB1bGxSZXF1ZXN0NDE3ODE5ODU= | 10,757 | BUG: Fix dtypes order when ordering is different from original file in pandas.io.stata.read_stata | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-08-06T12:27:07Z | 2015-08-06T15:17:07Z | 2015-08-06T15:16:59Z | CONTRIBUTOR | null | This fixes http://stackoverflow.com/questions/31783392/pandas-adding-columns-to-filter-will-mess-with-data-structure
The dtypes of the DataFrame returned by read_stata have the same order as the original stata file, even if columns are specified with a different order.
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https://api.github.com/repos/pandas-dev/pandas/issues/10758 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10758/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10758/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10758/events | https://github.com/pandas-dev/pandas/issues/10758 | 99,424,240 | MDU6SXNzdWU5OTQyNDI0MA== | 10,758 | API/COMPAT: add kw args to Timestamp constructor | {
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`Timestamp(year, month, day, second, millisecond, microsecond, nanosecond)`
as optional keyword args might be nice for compat with `datetime.datetime`. We do something similar for `Timedelta` construction.
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https://api.github.com/repos/pandas-dev/pandas/issues/10759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10759/events | https://github.com/pandas-dev/pandas/issues/10759 | 99,492,784 | MDU6SXNzdWU5OTQ5Mjc4NA== | 10,759 | Rolling qcut | {
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"f... | 1 | 2015-08-06T18:03:52Z | 2020-04-05T01:52:31Z | null | NONE | null | I believe qcut would be much more useful if there was a convenient way to apply it to a rolling window instead of the whole Series object. Of course it's possible with rolling_apply, but that does window_size times more calculations that are necessary for the output. For something as trivial as checking how the current... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10760/events | https://github.com/pandas-dev/pandas/issues/10760 | 99,578,239 | MDU6SXNzdWU5OTU3ODIzOQ== | 10,760 | DataError when aggregating Decimal values using pivot_table | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-08-07T04:46:12Z | 2015-08-10T11:05:43Z | 2015-08-10T11:05:43Z | NONE | null | ```
import pandas as pd
from decimal import Decimal
df = pd.DataFrame()
df['a'] = pd.Series(['a', 'b', 'a'])
df['b'] = pd.Series([Decimal(x) for x in [1, 2, 3]])
df.pivot_table(columns='a', values='b')
```
Results in:
```
DataError Traceback (most recent call last)
<ipython-input-8-15... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10761/events | https://github.com/pandas-dev/pandas/issues/10761 | 99,618,677 | MDU6SXNzdWU5OTYxODY3Nw== | 10,761 | DataFrame.plot overrides previous plots if the frame's index is DatetimeIndex | {
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... | open | false | null | [] | null | 5 | 2015-08-07T09:58:14Z | 2021-04-18T23:10:48Z | null | NONE | null | Environment is Anaconda 2.3, using python 2.7.10. Pandas version 0.16.2.
Here's the code to reproduce this problem in IPython Notebook 3.2, only `df['col3']` is plotted.
When sharing an axis the last plot() seems to override whatever was plotted before. Only the last plotted item is shown in figure.
```
import pand... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10762/events | https://github.com/pandas-dev/pandas/issues/10762 | 99,639,535 | MDU6SXNzdWU5OTYzOTUzNQ== | 10,762 | reset_index loses type information on time indices with time zone information | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-08-07T12:36:14Z | 2015-08-07T13:00:41Z | 2015-08-07T12:39:11Z | NONE | null | ```
import pandas as pd
ts1 = pd.Timestamp('2015-07-01 10:00:00')
ts2 = pd.Timestamp('2015-07-01 11:00:00')
df1 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns=['a'])
print(df1.index.dtype)
df1 = df1.reset_index(drop=False)
print(df1['index'].dtype)
df2 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10763 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10763/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10763/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10763/events | https://github.com/pandas-dev/pandas/issues/10763 | 99,643,027 | MDU6SXNzdWU5OTY0MzAyNw== | 10,763 | pandas.Series does not allow adding timedeltas to series of timestamps with datetime information | {
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It is possible with numpy, but not with pandas:
```
import numpy as np
import pandas as pd
ts1 = pd.Timestamp('2015-07-01 10:00:00').tz_localize('Europe/London')
ts2 = pd.Timestamp('2015-07-01 11:00:00').tz_localize('Europe/London')
dt = ts2 - ts1
x = np.array([ts1, ts2])
print(x)
dx = np.array([dt, dt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10764/events | https://github.com/pandas-dev/pandas/issues/10764 | 99,644,593 | MDU6SXNzdWU5OTY0NDU5Mw== | 10,764 | reindex() from string index to DatetimeIndex index returns nan values on Pandas 0.16.2 and 0.16.1 | {
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This test code:
```
indexInit = ["19990101","19990102","19990103"]
values = [1.0,2.5,2.1]
outSeries = Series(values,index=indexInit)
outSeries.reindex(index=DatetimeIndex(outSeries.index))
```
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1999-0... | {
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"followers_url": "https://api.github.com/users/jorisva... | 7 | 2015-08-07T15:56:20Z | 2016-11-11T11:46:51Z | 2016-11-11T11:46:43Z | NONE | null | Adding cassandra support should be very similar to `to_sql()` but is different enough where I think it needs it's own call, as it wouldn't use SQLAlchemy or behave even remotely close.
Python driver is here: https://github.com/datastax/python-driver
Willing to help on this if it'll get pulled in.
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https://api.github.com/repos/pandas-dev/pandas/issues/10766 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10766/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10766/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10766/events | https://github.com/pandas-dev/pandas/issues/10766 | 99,680,336 | MDU6SXNzdWU5OTY4MDMzNg== | 10,766 | Inconsistent results for np.isreal with strings | {
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Just uncovered a clear bug in the interaction between pandas series and numpy isreal(). The behavior is simple: isreal() produces incorrect results on panda's series that include string types. It's not 100% clear the underlying cause but it appears to be related to np.isreal operating over the pd.Series o... | {
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As you probably know, the [Truck (or Bus) Factor](https://en.wikipedia.org/wiki/Bus_factor) designates the minimal number of developers that have to be hit by a truck (or quit) before a project is in... | {
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I've stumbled onto a behavior that causes performance problems on large datasets: When creating a multiindex with second level being a DateTime64 col, and then doing a `.loc[('val1', TimeStamp('val2'),)]` to get at particular value, pandas coerces (via _box_func, called from __iter__) all values in that particu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10770/events | https://github.com/pandas-dev/pandas/issues/10770 | 99,829,223 | MDU6SXNzdWU5OTgyOTIyMw== | 10,770 | DOC: add days_in_month to time/date component accessors | {
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document http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#time-date-components
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https://api.github.com/repos/pandas-dev/pandas/issues/10771 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10771/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10771/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10771/events | https://github.com/pandas-dev/pandas/pull/10771 | 99,834,519 | MDExOlB1bGxSZXF1ZXN0NDE5NjA4MzU= | 10,771 | BUG: `read_sql_table` can't find views | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-08-08T21:31:09Z | 2015-08-12T09:30:03Z | 2015-08-12T06:46:14Z | CONTRIBUTOR | null | closes #10750
Add ability to 'read_sql_table' to read views and implement an unit test to check its behaviour
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https://api.github.com/repos/pandas-dev/pandas/issues/10772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10772/events | https://github.com/pandas-dev/pandas/issues/10772 | 99,838,956 | MDU6SXNzdWU5OTgzODk1Ng== | 10,772 | BUG: crosstab(dropna=False) does not work like value_counts(dropna=False) | {
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... | 15 | 2015-08-08T22:20:16Z | 2021-04-18T23:12:17Z | null | CONTRIBUTOR | null | If one uses `series.value_counts(dropna=False)` it includes `NaN` values, but `crosstab(series1, series2, dropna=False)` does not include `NaN` values. I think of `cross_tab` kind of like 2-dimensional `value_counts()`, so this is surprising.
``` python
In [31]: x = pd.Series(['a', 'b', 'a', None, 'a'])
In [32]: y = ... | {
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```
>>> datetime(2300,1,1)
datetime.datetime(2300, 1, 1, 0, 0)
>>> numpy.datetime64('2300-01-01')
numpy.datetime64('2300-01-01')
>>> Timestamp('2300-01-01') # pandas.tslib.OutOfBoundsDatetime
```
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... | 6 | 2015-08-09T01:16:09Z | 2021-04-18T23:16:23Z | 2021-04-18T23:16:23Z | MEMBER | null | Following slices basically result in the same `Index`.
```
import pandas as pd
pd.Index([1, 2, 3])[:, None]
# Int64Index([[1], [2], [3]], dtype='int64')
pd.MultiIndex.from_tuples([('a', 1), ('b', 2)])[:, None]
# MultiIndex(levels=[[u'a', u'b'], [1, 2]],
# labels=[[[0], [1]], [[0], [1]]])
pd.period_range('2... | {
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"descript... | closed | false | null | [] | null | 1 | 2015-08-09T01:35:59Z | 2015-08-09T01:56:38Z | 2015-08-09T01:56:20Z | CONTRIBUTOR | null | Here's what's going on:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.16.2+308.g529288e'
In [3]: dt = pd.Timedelta(seconds=-2)
In [4]: dt
Out[4]: Timedelta('-1 days +23:59:58')
In [5]: dt.seconds
Out[5]: 86398
```
I would expect `dt.seconds == -2`.
It seems like the fix may be something like:
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https://api.github.com/repos/pandas-dev/pandas/issues/10776 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10776/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10776/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10776/events | https://github.com/pandas-dev/pandas/pull/10776 | 99,871,274 | MDExOlB1bGxSZXF1ZXN0NDE5NjYzNDM= | 10,776 | BUG: GH10747 where cast_to_nanoseconds from NaT fails | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10777 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10777/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10777/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10777/events | https://github.com/pandas-dev/pandas/issues/10777 | 99,871,469 | MDU6SXNzdWU5OTg3MTQ2OQ== | 10,777 | Precision of floats with to_csv - difference between Python 2 and Python 3 | {
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I noticed that size of a CSV file generated with Pandas and Python are differents depending of which version of Python was used.
I was expecting same behaviour for same code.
With Python 3
```
pc:~ scls$ cat big.py
import pandas as pd
import numpy as np
(rows, cols) = (400, 10)
a = np.random.random((rows, co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10778/events | https://github.com/pandas-dev/pandas/issues/10778 | 99,907,374 | MDU6SXNzdWU5OTkwNzM3NA== | 10,778 | df.to_json segfaults with categorical column types | {
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``` python
df = pd.DataFrame({"A":["a","b","c","a"]})
df["B"] = df["A"].astype('category')
```
Will segfault when `df.to_json` is called. Tested on:
``` bash
Python 3.4.3 |Anaconda 2.3.0 (x86_64)| (default, Mar 6 2015, 12:07:41)
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10779/events | https://github.com/pandas-dev/pandas/issues/10779 | 99,917,716 | MDU6SXNzdWU5OTkxNzcxNg== | 10,779 | Series.iloc[ negative number ] can access memory it doesn't own or cause Segmentation Fault | {
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It does appear to check on write, just not on read.
Python 2.7.10 |Anaconda 2.1.0 (64-bit)| (default, May 28 2015, 17:02:03)
[GCC 4.4.7 20120313 (Red Hat 4... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10780/events | https://github.com/pandas-dev/pandas/issues/10780 | 99,983,042 | MDU6SXNzdWU5OTk4MzA0Mg== | 10,780 | grouped.filter with dropna option | {
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```
grouped = a.groupby(idx)
grouped.filter(lambda x: x.mean()>0.0, dropna=False)
```
in case there was no group whose `x.mean()` was larger than zero, an `AttributeError: 'list' object has no attribute 'astype'` would be raised (rather than returning an all `NaN` dataframe/serie in previous version... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10781/events | https://github.com/pandas-dev/pandas/issues/10781 | 100,005,765 | MDU6SXNzdWUxMDAwMDU3NjU= | 10,781 | ix/loc using indices rather than labels when passed a list for integer MultiIndex DataFrame? | {
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``` python
import numpy as np
import pandas as pd
index =... | {
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They are listed as _sudo apt-get install python-pandas_.
It would be a good idea to mention, that this will install pandas for python2 and you need to specificly write _python... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10783 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10783/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10783/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10783/events | https://github.com/pandas-dev/pandas/issues/10783 | 100,051,715 | MDU6SXNzdWUxMDAwNTE3MTU= | 10,783 | Undesired behaviour using to_csv with QUOTE_NONE | {
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My data frame contains unicode strings. Now when I use 'to_csv' without any quoting parameter, I get a csv file where certain fields (2 out of 11 to be exact) are in double quotes. For example:
`28,"May 8, 2013 10:22 AM","76th St, Yates Blvd",41.75717,-87.56633,3,-,1,4,-`
Now when I use csv.QUOTE_NONE i get the f... | {
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- Added hint regarding pip install on low memory machines.
- Added hint to python 3 version of pandas from distributon repos.
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I would have thought skipna=TRUE, which is ... | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-08-10T14:19:14Z | 2015-08-10T14:30:57Z | 2015-08-10T14:30:33Z | CONTRIBUTOR | null | In pandas 0.15, a DataFrame's index no longer supports the modulo operator. I believe the following code used to work under pandas 0.14, but under pandas 0.15, a TypeError indicating "unsupported operand types" is raised:
```
In [1]: import pandas ... | {
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] | closed | false | null | [] | null | 2 | 2015-08-10T15:42:30Z | 2015-08-11T11:39:16Z | 2015-08-11T10:36:50Z | NONE | null | I'm using windows server 2003, and python2.7 with pandas 0.16.2. It takes my computer 4-5 minutes to execute the following program:
---
from pandas.io.data import Options
aapl = Options('uso', 'yahoo')
data = aapl.get_all_data()
print data.iloc[0:5, 0:5]
---
Is it ok or not?
If anyone wants to know more details, ... | {
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... | 3 | 2015-08-10T16:25:49Z | 2020-05-22T03:09:52Z | null | MEMBER | null | Derived from #10507. Consider better API for testing functions.
### 1. How to specify comparison tolerance
Current assert functions have `check_less_precise` and `check_exact` flags to specify how to compare values:
- `check_exact=True`: Use `assert_equal`, comparison using `==`.
- `check_exact=False` and `check_less_... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-08-10T16:43:54Z | 2016-03-06T14:57:59Z | 2016-03-06T14:57:59Z | CONTRIBUTOR | null | Currently the exponential functions take one of three arguments to specify the length of backhistory. Each of these numerically converts to alpha. Outlined here: http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions
Is there a reason we don't allow users to just pass in `... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10791 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10791/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10791/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10791/events | https://github.com/pandas-dev/pandas/pull/10791 | 100,190,894 | MDExOlB1bGxSZXF1ZXN0NDIwNzgwMDA= | 10,791 | BUG: Index.take may add unnecessary freq attribute | {
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```
import pandas as pd
idx = pd.Index([1, 2, 3])
idx.freq
# AttributeError: 'Int64Index' object has no attribute 'freq'
idx.take([True, False, True]).freq
# None
```
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-08-10T23:45:43Z | 2015-08-29T00:03:43Z | 2015-08-29T00:03:43Z | CONTRIBUTOR | null | `nlargest/nsmallest` on frame should have `take_last` -> `keep`, in-line with #10236
[here](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#3187)
no deprecation is necessary as this is new in 0.17.0
cc @sinhrks
cc @cpcloud
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According to <a href="http://www.utf8-chartable.de/unicode-utf8-table.pl?start=6... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10795/events | https://github.com/pandas-dev/pandas/pull/10795 | 100,267,462 | MDExOlB1bGxSZXF1ZXN0NDIxMDg0MTg= | 10,795 | TST: Suppress warnings of drop_duplicates tests | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10796/events | https://github.com/pandas-dev/pandas/issues/10796 | 100,357,890 | MDU6SXNzdWUxMDAzNTc4OTA= | 10,796 | Incorrect groupby displayed in IPython Notebook with 64-bit floats and multi-index | {
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... | 5 | 2015-08-11T16:55:37Z | 2016-04-04T21:33:52Z | null | NONE | null | I ran into this bug when attempting some groupby-aggregates on data that had 64-bit floats.
```
_ids = np.array(range(201500000100,201500000110)*3).astype(float)
_df = pd.DataFrame({'id':_ids,
'value_1':np.random.randint(0,10,30),
'value_2':list('ABC')*10})
```
A simple groupby-aggregation produce... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10797/events | https://github.com/pandas-dev/pandas/issues/10797 | 100,379,543 | MDU6SXNzdWUxMDAzNzk1NDM= | 10,797 | Accept index in addition to list for set_index | {
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... | 5 | 2015-08-11T18:46:23Z | 2021-04-18T23:20:29Z | null | NONE | null | Running this code generates an error, since columns are now considered an Index and not a list. I'm not sure if the error that gets raised (set_index ends up asking for an index that is the entire length of the data frame) is actually by design. In this case, it was a bit unintuitive to figure out how to catch, since I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10798/events | https://github.com/pandas-dev/pandas/issues/10798 | 100,395,485 | MDU6SXNzdWUxMDAzOTU0ODU= | 10,798 | ENH: Support datetime.date in Period Index | {
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... | 3 | 2015-08-11T19:56:02Z | 2021-06-05T18:59:49Z | null | CONTRIBUTOR | null | `PeriodIndex`s should resolve when `datetime.date` is passed into `in`, but doesn't. Example below.
Tracing this through, it looks like `parse_time_string` is expected to return a tuple in `get_loc` (which is called by `__contains__`): https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py#L610
But if ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10799/events | https://github.com/pandas-dev/pandas/pull/10799 | 100,398,171 | MDExOlB1bGxSZXF1ZXN0NDIxNzIxNjg= | 10,799 | Allows set_index() to take an Index as an argument (#10797) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10800/events | https://github.com/pandas-dev/pandas/pull/10800 | 100,398,640 | MDExOlB1bGxSZXF1ZXN0NDIxNzI0NTY= | 10,800 | BUG: pd.Series.interpolate(method='spline') Errort Msg, #10633 | {
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This a first attempt at the error messages mentioned. Not ready to merge yet but I wanted to start the conversation...
I think I might have the error message wrong - anyone got any ideas.
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https://api.github.com/repos/pandas-dev/pandas/issues/10801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10801/events | https://github.com/pandas-dev/pandas/pull/10801 | 100,428,782 | MDExOlB1bGxSZXF1ZXN0NDIxODc2MjY= | 10,801 | PeriodIndex test keys that aren't strings | {
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Not sure if the testing is done in a good way - I just copy & pasted code to test `PeriodIndex` & `DatetimeIndex` (it wouldn't work for `LikeDatetimeIndex` because of `TimeDeltaIndex`) - very open to feedback on better ways to do this.
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https://api.github.com/repos/pandas-dev/pandas/issues/10802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10802/events | https://github.com/pandas-dev/pandas/issues/10802 | 100,438,790 | MDU6SXNzdWUxMDA0Mzg3OTA= | 10,802 | Should empty strings be null for notnull() and isnull()? | {
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"color": "AD7FA8"... | closed | false | null | [] | null | 6 | 2015-08-12T00:10:03Z | 2020-05-03T20:09:24Z | 2020-05-03T20:09:24Z | CONTRIBUTOR | null | Is there a reason that `notnull()` and `isnull()` consider an empty string to not be a missing value?
```
pd.isnull('')
False
```
Seems like in string data, people usually think of the empty string as "missing".
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