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https://api.github.com/repos/pandas-dev/pandas/issues/5601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5601/events | https://github.com/pandas-dev/pandas/pull/5601 | 23,409,300 | MDExOlB1bGxSZXF1ZXN0MTAzNDk5MjM= | 5,601 | TST/API: test the list of NA values in the csv parser. add N/A, #NA as independent default values (GH5521) | {
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Also added a few helper functions. These functions could be applied to refactor some of the other methods.
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closes #5480.
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Version: d057fc9 ('0.12.0-1178-gd057fc9')
I tried a sequence of "python setup.py clean && python setup.py install" without success.
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https://api.github.com/repos/pandas-dev/pandas/issues/5607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5607/events | https://github.com/pandas-dev/pandas/issues/5607 | 23,436,767 | MDU6SXNzdWUyMzQzNjc2Nw== | 5,607 | set_index drop inconsistency | {
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See what I mean:
``` python
print df
a b c
0 ... | {
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`DataFrameGroupBy.cumcount()` returns a Series while most others (the ones I tested, like count, cumsum, first, sum ) return a DataFrame. They are maybe not all comparable, but at least with `cumsum` it could be I think:
``` python
In [1]: data = pd.DataFrame({'name' : ['a', 'a', 'b', 'd'], 'counts' : [3,4,3,2... | {
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```
def quick_shift(df, N=1):
'''Quick implementation of dataframe shift'''
shift_value = np.roll(df.values,N,axis=0)
shift_value[0:N] = np.NaN
return DataFrame(shift_value, index=df.index, columns=df.columns)
DataFrame.quick_shift = quick_shift
```
Perf benchm... | {
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"description": "2014-02-3: previous version released",
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"labels_url": "htt... | 17 | 2013-11-28T11:52:46Z | 2016-08-26T21:13:28Z | 2014-04-29T20:12:46Z | MEMBER | null | `GroupBy.count()` (with the default `as_index=True`) return the grouping column both as index and as column, while other methods as `first` and `sum` keep it only as the index (which is most logical I think). This seems a minor inconsistency to me:
```
In [41]: data = pd.DataFrame({'name' : ['a', 'a', 'b', 'd'], 'coun... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5611/events | https://github.com/pandas-dev/pandas/issues/5611 | 23,449,508 | MDU6SXNzdWUyMzQ0OTUwOA== | 5,611 | DataFrame inspector GUI, df.inspect() | {
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"followers_url": "https://api.github.com/users/jorisva... | 8 | 2013-11-28T13:25:02Z | 2016-11-11T10:09:36Z | 2016-11-11T10:09:32Z | NONE | null | #5013 is in the sandbox, the functionality would be useful as `df.inspect()`,
popping up a gui grid view of the frame.
In the future, it's possible to detect ipnb and render a js grid instead, if the coming ipython
js functionality is adequate, to cover the remote kernel use-case (less common).
cc @alefnula
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Maybe it could be made more similar to `pd.datetime`? (or even just use it under the hood?)
Code: https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L278
Docs:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5613/events | https://github.com/pandas-dev/pandas/issues/5613 | 23,458,643 | MDU6SXNzdWUyMzQ1ODY0Mw== | 5,613 | asfreq / resample do not set freq with regular timeseries | {
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```
In [1]: df = pd.DataFrame({'date':["2012-01-01", "2012-01-02", "2012-01-03"], 'col':[1,2,3]})
In [2]: df2 = df.set_index(pd.to_datetime(... | {
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"labels_url": "htt... | 16 | 2013-11-28T20:37:33Z | 2014-05-01T22:23:50Z | 2014-05-01T15:13:57Z | CONTRIBUTOR | null | It shouldn't sum the groupby'd col (in fact index col should be the index, if groupby as_index).
```
In [13]: df = pd.DataFrame([[1, 2, np.nan], [1, np.nan, 9], [3, 4, 9]], columns=['A', 'B', 'C'])
In [14]: g = df.groupby('A')
In [16]: g.cumsum()
Out[16]:
A B C
0 1 2 NaN
1 2 NaN 9
2 3 4 9
[3 rows... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5615/events | https://github.com/pandas-dev/pandas/pull/5615 | 23,476,781 | MDExOlB1bGxSZXF1ZXN0MTAzODY1ODQ= | 5,615 | DOC: SQL to pandas comparison (#4524) | {
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``` python
>>> df = pd.DataFrame({'a': [10, 20], 'b': [30, 40], 'c': [50, 60]},
... index=pd.MultiIndex.from_tuples([(70, 80), (90, 100)]))
>>> writer = pd.ExcelWriter('test.xls')
>>> df.to_excel(writer, sheet_name='1', index=False)
>>> writer.save()
>>> pd.read_excel('test.xls', '1')
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https://api.github.com/repos/pandas-dev/pandas/issues/5617 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5617/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5617/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5617/events | https://github.com/pandas-dev/pandas/pull/5617 | 23,504,458 | MDExOlB1bGxSZXF1ZXN0MTA0MDIyNzU= | 5,617 | RLS: first release candidate for v0.13 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5618/events | https://github.com/pandas-dev/pandas/issues/5618 | 23,504,498 | MDU6SXNzdWUyMzUwNDQ5OA== | 5,618 | set_option as a context manager | {
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"followers_url": "https://api.github.com/users/... | 11 | 2013-11-29T18:11:57Z | 2017-02-23T17:29:57Z | 2013-12-31T00:49:22Z | CONTRIBUTOR | null | It doesn't look like it's available yet as such, but it might be nice to have set_option usable as a context manager. E.g., I often find myself wanting to do something like
```
with pd.set_option('max_rows', 200):
print df
```
As simple as adding `__enter__` and `__exit__` methods and preserving state?
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https://api.github.com/repos/pandas-dev/pandas/issues/5619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5619/events | https://github.com/pandas-dev/pandas/issues/5619 | 23,513,990 | MDU6SXNzdWUyMzUxMzk5MA== | 5,619 | ENH: Allow passing a list of regular expressions to the usecols keyword argument in read_csv | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5620/events | https://github.com/pandas-dev/pandas/issues/5620 | 23,519,585 | MDU6SXNzdWUyMzUxOTU4NQ== | 5,620 | GroupBy on a MultiIndex with the index causes python to stop working | {
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```
df = pd.DataFrame({'A':[1,1,2], 'B':[1,2,1], 'C':[1,2,3]})
df = df.set_index(['A', 'B'])
df.index.groupby(df.index)
```
I know this code is as such probably bullshit (I also did it by accident, the first index shouldn't have been there :-)), but still... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5621/events | https://github.com/pandas-dev/pandas/pull/5621 | 23,522,497 | MDExOlB1bGxSZXF1ZXN0MTA0MTAxNTM= | 5,621 | DOC: Remove quasi-non-public parts of Index API from API docs | {
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shouldn't be there at all, since they're not meant for public
consumption or not documented. I've slimmed down the methods to set
ops, copy, metadata (i.e. names) and a few of the DatetimeIndex methods.
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https://api.github.com/repos/pandas-dev/pandas/issues/5622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5622/events | https://github.com/pandas-dev/pandas/issues/5622 | 23,524,129 | MDU6SXNzdWUyMzUyNDEyOQ== | 5,622 | BUG: Indexes and datetime objects | {
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```
import pandas as pd, numpy, datetime
#idx = [datetime.date.fromordinal(n) for n in range(1,3)]
idx = [datetime.datetime.fromordinal(n) for n in range(1,3)]
df = pd.DataFrame(data=numpy.zeros(2),index=idx)
```
This code does not work. It seems that the lin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5623/events | https://github.com/pandas-dev/pandas/pull/5623 | 23,525,880 | MDExOlB1bGxSZXF1ZXN0MTA0MTE1MjA= | 5,623 | BUG/API: autocorrelation_plot should accept kwargs | {
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"followers_url": "https://api.github.com/users/... | 4 | 2013-11-30T20:15:08Z | 2014-06-15T07:05:39Z | 2014-01-15T15:28:37Z | CONTRIBUTOR | null | Unless there's a specific reason not to for this plot? All the others seem to accept them.
``` python
autocorrelation_plot(data, label='ACF')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5624/events | https://github.com/pandas-dev/pandas/pull/5624 | 23,526,844 | MDExOlB1bGxSZXF1ZXN0MTA0MTE4OTY= | 5,624 | CLN: Trim includes of absent files | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5625/events | https://github.com/pandas-dev/pandas/pull/5625 | 23,529,412 | MDExOlB1bGxSZXF1ZXN0MTA0MTI4MTE= | 5,625 | ENH: Make set_option into a contextmanager that undos itself on __exit__ | {
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cc @y-p and @jseabold
I went ahead and simplifed all the prefixing stuff to just change a single `prefix_key` function instead and handled the dynamic docstring by setting the `__doc__` attribute of functions/classes. set_option is now callable _and_ a contextmanager (whee!) so no need to add anything els... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5626/events | https://github.com/pandas-dev/pandas/pull/5626 | 23,529,757 | MDExOlB1bGxSZXF1ZXN0MTA0MTI5Mzg= | 5,626 | TST: Always try to close file descriptors of tempfiles | {
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segmentation faults for me. This should fix the issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/5627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5627/events | https://github.com/pandas-dev/pandas/pull/5627 | 23,536,316 | MDExOlB1bGxSZXF1ZXN0MTA0MTU0MDg= | 5,627 | BUG/TST: importing util.clipboard shouldnt print to stdout (nosetests) | {
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$ nosetests
/usr/bin/xclip <<<< raise your hand if you've been ignoring this for 6 months
...........
```
`os.system` nastiness. The vbench package has similar problems.
https://github.com/pydata/pandas/pull/3848
:raised_hand:
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https://api.github.com/repos/pandas-dev/pandas/issues/5628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5628/events | https://github.com/pandas-dev/pandas/issues/5628 | 23,550,720 | MDU6SXNzdWUyMzU1MDcyMA== | 5,628 | Dropping rows inplace, weird bug with 0.13.0rc1 | {
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Please have a look at the MWE I figured out after some hacki... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5629/events | https://github.com/pandas-dev/pandas/issues/5629 | 23,554,161 | MDU6SXNzdWUyMzU1NDE2MQ== | 5,629 | PERF: Too long MultiIndex repr freezes notebook | {
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``` python
print len(counts.index)
print type(counts.index)
3982319
<class 'pandas.core.index.MultiIndex'>
```
whereupon my Macbook started to boil and the notebooks became unresponsive and I had to hard-kill the notebook server ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5630/events | https://github.com/pandas-dev/pandas/issues/5630 | 23,563,755 | MDU6SXNzdWUyMzU2Mzc1NQ== | 5,630 | Documentation / Remote Data Access - 3rd pary libraries | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2013-12-02T10:33:25Z | 2015-01-06T13:14:19Z | 2015-01-06T13:14:19Z | CONTRIBUTOR | null | Does it make sense to extend documentation with links to a 3rd party libraries that download financial data from different sources in the pandas format? E.g. interfaces for Quandl (http://www.quandl.com/help/packages/python) or for Thomson Reuters Datastream (https://github.com/vfilimonov/pydatastream)?
Also personal... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5632/events | https://github.com/pandas-dev/pandas/issues/5632 | 23,603,011 | MDU6SXNzdWUyMzYwMzAxMQ== | 5,632 | Setting empty column on empty DataFrame | {
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"followers_url": "https://api.github.com/users/wesm... | 6 | 2013-12-02T21:26:59Z | 2013-12-03T14:53:32Z | 2013-12-03T01:59:04Z | CONTRIBUTOR | null | This issue appears in 0.13.0-rc1 and did not appear in 0.12.0-854-gde63e00
Let us say we have an empty DataFrame. Then the following calls (which worked in pandas 0.12) no longer work:
``` python
df = pd.DataFrame()
df['foo'] = []
df = pd.DataFrame()
df['foo'] = df.index
df = pd.DataFrame()
df['foo'] = range(len(df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5633/events | https://github.com/pandas-dev/pandas/pull/5633 | 23,606,249 | MDExOlB1bGxSZXF1ZXN0MTA0NTE0NTE= | 5,633 | BUG: make sure partial setting with a Series like works with a completly empty frame (GH5632) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5634/events | https://github.com/pandas-dev/pandas/pull/5634 | 23,617,707 | MDExOlB1bGxSZXF1ZXN0MTA0NTc5ODk= | 5,634 | BUG: Validate levels in a multi-index before storing in a HDFStore (GH5527) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5635/events | https://github.com/pandas-dev/pandas/issues/5635 | 23,620,886 | MDU6SXNzdWUyMzYyMDg4Ng== | 5,635 | BUG: empty result for HDFStore where with leading NaNs | {
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"labels_url": "htt... | 6 | 2013-12-03T04:36:41Z | 2014-02-15T21:00:15Z | 2014-02-15T21:00:15Z | CONTRIBUTOR | null | https://github.com/PyTables/PyTables/issues/282 is the culprit
I may be missing something here, but I think this is an odd result. When reading from a `table` HDFStore, if the first value is NaN then any `where`s based on that column will return an empty selection.
``` python
In [58]: sm
Out[58]:
HRHHID... | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2013-12-03T20:03:36Z | 2016-04-19T18:36:05Z | 2016-04-19T18:36:05Z | CONTRIBUTOR | null | With:
```
csv="""A,B,C
1,2,2003-11-1
"""
```
These all work as expected:
```
In [40]: pd.read_csv(StringIO(csv), parse_dates="C",index_col="C").index[0]
Out[40]: Timestamp('2003-11-01 00:00:00', tz=None)
In [41]: pd.read_csv(StringIO(csv), parse_dates=["C"],index_col="C").index[0]
Out[41]: Timestamp('2003-11-01 0... | {
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"labels_url": "htt... | 1 | 2013-12-03T20:33:11Z | 2014-04-06T15:03:05Z | 2014-04-06T15:03:01Z | NONE | null | With a daily frequency, when you specify a range from a Friday morning UTC to Saturday morning UTC, you get back both days as expected:
```
(Pdb) pd.DatetimeIndex(start=datetime.datetime(2013,11,22,5,0,0,tzinfo=pytz.utc), end=datetime.datetime(2013,11,23,4,59,59,tzinfo=pytz.utc), freq=pd.datetools.Day())
<class 'panda... | {
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"labels_url": "htt... | 31 | 2013-12-04T00:10:22Z | 2014-06-13T17:00:32Z | 2014-03-18T17:42:12Z | CONTRIBUTOR | null | close #3796
Addresses some of the concerns in issue #3796. New code allows the DataFrame and Series Line plots and Bar plot functions to include errorbars using `xerr` and `yerr` keyword arguments to `DataFrame/Series.plot()`. It supports specifying x and y errorbars as 1. a separate list/numpy/Series, 2. a DataFrame ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5639/events | https://github.com/pandas-dev/pandas/issues/5639 | 23,710,607 | MDU6SXNzdWUyMzcxMDYwNw== | 5,639 | Selecting same column twice in DataFrame does not work when some columns are bool | {
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``` python
import pandas as pd
#Data with just bool columns
dbool = {'one' : pd.Series([True, True, False], index=['a', 'b', 'c']),
'two' : pd.Series([False, False, True, False], inde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5641/events | https://github.com/pandas-dev/pandas/issues/5641 | 23,733,949 | MDU6SXNzdWUyMzczMzk0OQ== | 5,641 | assignment to slice | {
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```
import pandas as pd
import numpy as np
from pandas.tseries.offsets import *
from datetime import date
M_ASSETS=50
K_FACTORS=3
dates = pd.bdate_range(date(2013, 1, 1), date(2013, 1, 10))
factor_names=['F'+str(y) for y in xrange(1, K_FACTORS+1)]
asset_names = ['A'+str(y)... | {
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pandas version: 0.12.0
numpy version: 1.7.1
psycopg2 version: 2.5.1
I observe this as I parse the result set from Postgres (my psycopg2 installation is configured to return mx.DateTimes). The ideal scenario is that these objects are converted to numpy.datetime64 along with appropriate time zone ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5643/events | https://github.com/pandas-dev/pandas/issues/5643 | 23,749,919 | MDU6SXNzdWUyMzc0OTkxOQ== | 5,643 | NaT.date returns garbage | {
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pd.Series([pd.NaT]).apply(lambda x:x.date())
```
```
0 0001-255-255
dtype: object
```
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Eliminate some more noise from tests, manifests only on system where `locale` isn't available.
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... | 16 | 2013-12-05T00:47:00Z | 2021-08-31T09:25:30Z | null | CONTRIBUTOR | null | related #6360
Based on [this SO question](http://stackoverflow.com/questions/20383972/binary-operation-broadcasting-across-multiindex?noredirect=1#comment30444368_20383972)
Consider the following two Series:
``` python
x = pd.DataFrame({'year':[1,1,1,1,2,2,2,2],
'country':['A','A','B','B','A','A','... | {
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I'm working on a clustered heatmap figure for pandas, like R's `heatmap.2` which I used I ton when I was still in R-land.
I'm having some trouble wrapping my mind around `pandas.tools.plotting` but I have a draft version of this `heatmap` function with some initial parameters in my fork: https://github.co... | {
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```
df1
```
> <class 'pandas.core.frame.DataFrame'>
> Int64Index: 71958 entries, 0 to 104014
> Data columns (total 3 columns):
> FactorDate 71958 non-null values
> FundSecNo 71958 non-nu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5648 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5648/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5648/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5648/events | https://github.com/pandas-dev/pandas/issues/5648 | 23,816,449 | MDU6SXNzdWUyMzgxNjQ0OQ== | 5,648 | idxmax, idxmin inconsistency | {
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python 2.7.5
anaconda 1.8.0
pandas 12.0
numpy 1.7.1
What seems to be occurring: DataFrame.idxmax/idxmin functions work or not depending on how the DataFrame object is created...
This works: **************************************
In [2]: df = pandas.DataFrame( {1:[1,2,3], 2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5649 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5649/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5649/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5649/events | https://github.com/pandas-dev/pandas/pull/5649 | 23,825,090 | MDExOlB1bGxSZXF1ZXN0MTA1NzUxMjY= | 5,649 | BUG: repr_html, fix GH5588 for the MultiIndex case | {
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#5588
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https://api.github.com/repos/pandas-dev/pandas/issues/5650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5650/events | https://github.com/pandas-dev/pandas/issues/5650 | 23,838,951 | MDU6SXNzdWUyMzgzODk1MQ== | 5,650 | Correct place for SettingWithCopyWarning? | {
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"followers_url": "https://api.github.com/users/wesm... | 8 | 2013-12-06T05:40:56Z | 2013-12-07T00:18:10Z | 2013-12-07T00:18:10Z | CONTRIBUTOR | null | I am really happy about the SettingWithCopyWarning but is this situation the right place to pop up? Because what I want to do seems to work, maybe I don't see the danger of a possible case?
``` python
In [19]: df.convert_objects().dtypes
Out[19]:
hour float64
minute float64
second float64
tb float64... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5651/events | https://github.com/pandas-dev/pandas/issues/5651 | 23,862,539 | MDU6SXNzdWUyMzg2MjUzOQ== | 5,651 | ValueError: zero-size array to reduction operation maximum | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5652 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5652/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5652/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5652/events | https://github.com/pandas-dev/pandas/issues/5652 | 23,871,675 | MDU6SXNzdWUyMzg3MTY3NQ== | 5,652 | "df.get(None)" throws error in 0.13 -> regression? | {
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"followers_url": "https://api.github.com/users/... | 10 | 2013-12-06T17:16:00Z | 2017-09-15T13:15:20Z | 2014-01-23T22:03:09Z | CONTRIBUTOR | null | This works with pandas 0.12:
``` python
from ggplot import meat
meat.get(None)
[returns None]
```
But throws an error in 0.13 (RC):
``` python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-86be... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5653 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5653/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5653/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5653/events | https://github.com/pandas-dev/pandas/issues/5653 | 23,873,316 | MDU6SXNzdWUyMzg3MzMxNg== | 5,653 | BUG: Series.rename() ignores level argument on MultiIndex | {
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``` python
In [35]: idx = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3)])
In [36]: s = pd.Series(np.arange(6), index=idx)
In [37]: s
Out[37]:
1 1 0
2 1
3 2
2 1 3
2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5654 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5654/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5654/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5654/events | https://github.com/pandas-dev/pandas/issues/5654 | 23,873,803 | MDU6SXNzdWUyMzg3MzgwMw== | 5,654 | performance of DataFrame.apply between 0.12 and 0.13rc1 | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-12-06T17:51:35Z | 2014-01-21T15:16:06Z | 2013-12-06T18:53:50Z | CONTRIBUTOR | null | Here is a small example of a performance regression I've noticed between 0.12 and 0.13rc1:
``` python
s = pd.Series(np.arange(4096.))
df = pd.DataFrame({i:s for i in range(4096)})
# under 0.12
%timeit df.apply((lambda x: np.corrcoef(x, s)[0, 1]), axis=1)
1 loops, best of 3: 792 ms per loop
# under 0.13rc1
%timeit d... | {
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In [12]: pd.read_json("No Such File")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-f479cae2f65a> in <module>()
----> 1 pd.read_json("No Such File")
/home/user1/src/pandas/pandas/io/j... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5657 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5657/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5657/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5657/events | https://github.com/pandas-dev/pandas/issues/5657 | 23,880,220 | MDU6SXNzdWUyMzg4MDIyMA== | 5,657 | df.filter(like='col_name') 2.975X slower than basic column list comprehension | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2013-12-06T19:39:53Z | 2018-07-06T22:02:32Z | 2018-07-06T22:02:31Z | NONE | null | I have found that using the filter method to select columns that match a string pattern is ~3x slower than basic list comprehension on the df.columns list. Not sure how its implemented under the hood but for basic 'in' checks on lots of columns this could slow you down depending on how often you filter.
``` python
imp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5658 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5658/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5658/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5658/events | https://github.com/pandas-dev/pandas/pull/5658 | 23,889,799 | MDExOlB1bGxSZXF1ZXN0MTA2MTE2ODY= | 5,658 | API: change _is_copy to is_copy attribute on pandas objects GH(5650) | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2013-12-07T00:27:33Z | 2014-06-18T19:01:49Z | 2013-12-10T13:19:30Z | CONTRIBUTOR | null | closes #5616 caused by the updated Excel MultiIndex handling.
This change should go into v0.13.
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https://api.github.com/repos/pandas-dev/pandas/issues/5660 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5660/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5660/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5660/events | https://github.com/pandas-dev/pandas/issues/5660 | 23,900,675 | MDU6SXNzdWUyMzkwMDY3NQ== | 5,660 | 0.13rc1+ vs. 0.12 vbench, perf regressions. | {
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```
melt_dataframe | 2.7920 | 2.1487 | 1.2994 |
concat_small_frames | 22.1807 | 17.0054 | 1.3043 |
dti_reset_index | 0.2950 | 0.2257 | 1.3070 |
frame_reindex_upcast | 28.803... | {
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when sub classing have to copy a mutable property before modifying
http://stackoverflow.com/questions/20444593/pandas-compiled-from-source-default-pickle-behavior-changed
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... | 0 | 2013-12-07T21:46:47Z | 2015-03-31T21:06:09Z | 2015-03-31T21:06:09Z | NONE | null | The upper left y axis tick marks in a scatter_matrix appear to have units/values pulled from the (0, 0) histogram counts, instead of the data values that are used for all other axis tick values.
Example:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.12.0'
In [4]: df =... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5664/events | https://github.com/pandas-dev/pandas/issues/5664 | 23,917,926 | MDU6SXNzdWUyMzkxNzkyNg== | 5,664 | BUG: read_csv segfault | {
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```
>>> import pandas as pd
>>> from StringIO import StringIO
>>> pd.__version__
'0.13.0rc1-43-g4f9fefc'
>>>
>>> f = StringIO('1,1,1,1,0\n'*2 + '\n'*2)
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**Short story:**
In [this](https://github.com/pydata/pandas/issues/5497) issue I signaled that the x_lim and y_lim in scatter_matrix plots are different. In particular x/y_lim of diagonal hists and x/y_lim of scatter plots are different.
For instance, this causes peaks nonalignments in the 1-margin and... | {
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from ndarray.item() when the array is not of length 1. This can be
seen as a failure of
```
computation.tests.test_eval.TestScope.test_global_scope
```
for the cases that engine='numexpr'.
Absorb the splatter from this regression by explicitly catching ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5668 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5668/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5668/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5668/events | https://github.com/pandas-dev/pandas/issues/5668 | 23,971,639 | MDU6SXNzdWUyMzk3MTYzOQ== | 5,668 | ENH: unsparsify multi-index column labels | {
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... | 1 | 2013-12-09T16:02:33Z | 2021-04-11T03:58:01Z | null | CONTRIBUTOR | null | `read_csv` requires the multi-index column labels to be non-sparse in order to properly parse them. I am not sure if this can be done automatically, though maybe a keyword could trigger this.
```
In [40]: def unsparsify_labels(index):
....: new_labels = []
....: for label in index.values:
....: ... | {
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... | 9 | 2013-12-09T16:20:44Z | 2021-04-11T04:00:18Z | null | CONTRIBUTOR | null | reported here:
http://stackoverflow.com/questions/20459782/what-is-the-functionality-of-the-filling-method-when-reindexing
does not appear to be any tests for it, nor is fix that trivial
```
import pandas as pd
import numpy as np
hf_index = pd.date_range(start='2013-05-09 9:00', end='2013-05-13 23:59', freq='1min')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5670/events | https://github.com/pandas-dev/pandas/issues/5670 | 23,978,195 | MDU6SXNzdWUyMzk3ODE5NQ== | 5,670 | BUG: lexsort segfault | {
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```
>>> import numpy as np, pandas as pd
>>> pd.__version__
'0.13.0rc1-47-g9ac008c'
>>> df = pd.DataFrame(range(5))
>>> np.lexsort(df.values)
array([0])
>>> np.lexsort(df)
Segmentation fault
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5671 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5671/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5671/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5671/events | https://github.com/pandas-dev/pandas/issues/5671 | 23,986,718 | MDU6SXNzdWUyMzk4NjcxOA== | 5,671 | performance of DataFrame.corrwith vs raw apply | {
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"labels_url": "htt... | 8 | 2013-12-09T19:29:45Z | 2014-01-28T17:09:24Z | 2014-01-28T16:49:31Z | CONTRIBUTOR | null | For the following example I see corrwith performing considerably more slowly than doing a "raw" apply of np.corrcoef, with the tip code as of 2013-12-08:
```
s = pd.Series(np.arange(4096.))
df = pd.DataFrame({i:i + np.arange(3360.) for i in range(4096)})
%timeit -n 3 df.apply((lambda x: np.corrcoef(x.values, s.values... | {
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"f... | 15 | 2013-12-10T10:27:50Z | 2020-05-03T19:58:04Z | 2020-05-03T19:58:03Z | CONTRIBUTOR | null | Since the output of the string methods is primarily boolean, do we want to preserve NaNs? Use case. I load in some data, empty strings (i.e., missing data in a column that's string data) gets automatically converted to NaNs. I have to do fillna everywhere first, or I run into situations like this.
```
dta = pd.DataFr... | {
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"followers_url": "https://api.github.com/users/... | 3 | 2013-12-10T12:10:44Z | 2014-01-03T01:43:47Z | 2014-01-03T01:43:47Z | NONE | null | ``` python
In [83]: pd.options.display.max_rows=1
...: pd.options.display.max_columns=1
...: df=DataFrame(np.random.randn(1000,1000))
...: %prun -l 5 df._repr_html_()
ncalls tottime percall cumtime percall filename:lineno(function)
209 0.000 0.000 0.000 0.000 {isinstance}
In [84]:... | {
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http://stackoverflow.com/questions/20489519/how-come-pandas-select-results-in-the-examples-dont-match
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https://api.github.com/repos/pandas-dev/pandas/issues/5675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5675/events | https://github.com/pandas-dev/pandas/pull/5675 | 24,069,456 | MDExOlB1bGxSZXF1ZXN0MTA3MDUxNjU= | 5,675 | BUG: properly handle a user function ingroupby that returns all scalars (GH5592) | {
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BUG: handle coercion of None for date/time like dtypes in a groupby result
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https://api.github.com/repos/pandas-dev/pandas/issues/5676 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5676/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5676/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5676/events | https://github.com/pandas-dev/pandas/issues/5676 | 24,077,349 | MDU6SXNzdWUyNDA3NzM0OQ== | 5,676 | Violin plots? | {
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http://nbviewer.ipython.org/gist/olgabot/7902901
I'd like to add this to pandas. Is there interest in these plots? It currently depends on my `prettyplotlib` but that can be removed. My concern is that it depends on `scipy.stats.gau... | {
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``` python
In [11]: idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3)])
In [12]: idx.names = ['outer', 'inner']
In [13]: df = pd.DataFrame({"A": np.arange(6), 'B': ['one', 'one', 'two', 'two', 'one', 'one']}, index=idx)
```
So the idea ... | {
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---
```
In [19]: import pandas as pd
In [20]: import numpy as np
In [21]: import random
In [22]: df = pd.DataFrame(np.random.random_sample((20,5)), index=[random.choice('ABCDE') for x in range(20)])
In [23]: df.loc[:,0].ix['A'].median()
Out[23]: 0.57704085832236685
In [24]: pd.version.ve... | {
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pretty common error that I have seen, originally from:
http://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug
```
In [1]: a = [12, 23]
In [2]: b = [123, None]
In [3]: c = [1234, 2345]
In [4]: d = [12345, 23456]
In [5]: tuples = [('eyes', 'left... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5681/events | https://github.com/pandas-dev/pandas/issues/5681 | 24,113,175 | MDU6SXNzdWUyNDExMzE3NQ== | 5,681 | Can't assign a sum'ed series to a dataframe | {
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p = pandas.DataFrame({ 'x' : [1,2,3], 'y' : [1,2,3] })
sumOfP = p.sum() #Gives a Series of [6,6]. OK.
totals = pandas.DataFrame({ 'someOtherSeries' : [1,2])
totals['sumOfP'] = sumOfP #BAD! T... | {
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Old:
```
In [39]: DataFrame(randn(10,2)).info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 2 columns):
0 10 non-null values
1 10 non-null values
dtypes: float64(2)
```
New:
```
In [39]: DataFrame(randn(10,2)).info()
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```
#!/bin/env python
import pandas as pd
import numpy as np
import sys
print sys.version
print "NUMPY:", np.__version__
print "PANDAS:", pd.__version__
df = pd.DataFrame([[0, None], [4, 3]], columns=['a','b'])
print df
df["c"] = 1.0
#print df #uncomment and result change
df["c"][~df.all(axis=1)] = 2
print d... | {
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"labels_url": "htt... | 4 | 2013-12-12T14:47:14Z | 2014-02-05T02:08:12Z | 2014-02-05T02:08:12Z | CONTRIBUTOR | null | _Edited to clarify the bug_
`Series.xs` slice fails with string index labels and MultiIndex:
``` python
In [1]: idx = pd.MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), ('b', 'two')])
In [2]: df = pd.Series(np.random.randn(4), index=idx)
In [4]: df.index.set_names(['L1', 'L2'], inplace=True)
In [... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5685/events | https://github.com/pandas-dev/pandas/issues/5685 | 24,191,574 | MDU6SXNzdWUyNDE5MTU3NA== | 5,685 | Attribute access to DataFrame behaves incorrectly | {
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In [80]: dat = pd.DataFrame({'a' : [1,2,3], 'b' : [4,5,6]})
In [81]: dat
Out[81]:
a b
0 1 4
1 2 5
2 3 6
[3 rows x 2 columns]
In [82]: dat.c = 1
In [83]: dat
Out[83]:
a b
0 1 4
1 2 5
2 3 6
[3 rows x 2 columns]
In [84]: dat['c'] = 2
In [85]: dat
Out[85]:
a b c
0 1 4 2
1 2 5 2
2 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5686 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5686/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5686/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5686/events | https://github.com/pandas-dev/pandas/issues/5686 | 24,193,460 | MDU6SXNzdWUyNDE5MzQ2MA== | 5,686 | Add ability to process bad lines for read_csv | {
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... | 19 | 2013-12-12T17:16:47Z | 2021-10-16T17:40:03Z | null | NONE | null | CSV files can contains some errors, for example:
```
NAME,PAT
Peter,cat
really bad line
Fedor,cat
```
to skip really bad lines exist `error_bad_lines=False` parameter.
Another example without quotes and delimiters in field:
```
NAME,PAT
Peter,cat
Ira,cat,dog
Fedor,cat
```
Which with quotes will look like this:
```... | {
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numpy version: '1.7.1'
erlport version: '1.0.0beta'
I am passing data between erlang and python using erlport, following the example here:
http://erlport.org/docs/python.html
the python file I'm calling only contains the line:
import pandas as pd
I am getting the error:
``` python
** except... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5688 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5688/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5688/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5688/events | https://github.com/pandas-dev/pandas/issues/5688 | 24,209,728 | MDU6SXNzdWUyNDIwOTcyOA== | 5,688 | NaT Not Correctly showing up as null value | {
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"followers_url": "https://api.github.com/users/... | 1 | 2013-12-12T21:27:31Z | 2013-12-12T21:59:22Z | 2013-12-12T21:59:22Z | CONTRIBUTOR | null | On pandas 0.12:
```
pd.DataFrame( pd.Series([datetime(2012,12,12).replace(tzinfo=pytz.utc)] + [pd.NaT]*100))
```
I would argue this should show 1 non null-value:
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 101 entries, 0 to 100
Data columns (total 1 columns):
0 101 non-null values
dtypes: object(1)
```... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5689 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5689/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5689/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5689/events | https://github.com/pandas-dev/pandas/issues/5689 | 24,216,004 | MDU6SXNzdWUyNDIxNjAwNA== | 5,689 | BUG: timedelta64 dtype inference in Series | {
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"followers_url": "https://api.github.com/users/... | 3 | 2013-12-12T23:09:22Z | 2014-02-04T15:53:19Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | related #5690
This works:
```
pd.Series(np.array([np.timedelta64(300000000)]))
```
```
0 00:00:00.300000
dtype: timedelta64
```
but this does not:
```
pd.Series([np.timedelta64(300000000)])
```
on Numpy 1.7:
```
TypeError: don't know how to convert scalar number to float
```
and on Numpy 1.8:
```
0 300000... | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/... | 4 | 2013-12-12T23:50:07Z | 2013-12-13T15:20:00Z | 2013-12-13T15:20:00Z | CONTRIBUTOR | null | related #5689
On pandas v12:
I thought this was fixed in #2990, but:
both of these:
```
pd.DataFrame(pd.Series((np.array([np.timedelta64(3000000000),np.timedelta64(3000000000)])))).min()
```
and
```
pd.Series((np.array([np.timedelta64(3000000000),np.timedelta64(3000000000)]))).min()
```
produce:
```
/usr/lib64/... | {
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I'm using Python 7.2.3 and Pandas 0.12.0.
When I run the code below, it works as expected, by creating new items and updating them.
```
quotes_premiums = quotes_cc_returns.transpose(2, 1, 0)
quotes_premiums['RiskPremium'] = quotes_premiums.CC_Returns.sub(ff_data_factors_subset.RF, axis=0)
quotes_premiums['Mk... | {
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"creator": null,
"description": "2014-02-3: previous version released",
"due_on": "2014-05-31T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
"id": 526062,
"labels_url": "htt... | 4 | 2013-12-13T12:27:49Z | 2014-02-18T19:48:40Z | 2014-02-18T19:48:40Z | CONTRIBUTOR | null | In master, `NDFrame.fillna`'s `inplace` parameter's docs claim:
> If True, fill in place. Note: this will modify any
> other views on this object, (e.g. a no-copy slice for a column in a
> DataFrame). **Still returns the object.**
But this is not consistently true. If `inplace=True`, `self` is only returned on a cou... | {
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(pandas version: 0.12.0, Python version: 3.3.2, OS: Linux):
pandas.Series([0], ["y"])[["x", "x", "y"]]
If "x" is replaced to 1 and "y" to 2, then the error disappears.
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Given this DataFrame, with an index containing the moment when DST changes (October 27th in the case of the "Europe/Paris" timezone):
```
index = pandas.date_range('2013-09-30', '2013-11-02', freq = '30Min', tz = 'UTC').tz_convert('Europe/Paris')
column_a = pandas.np.random.random(index.size)
column_b =... | {
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"followers_url": "https://api.github.com/users/... | 0 | 2013-12-13T16:31:35Z | 2014-01-20T23:37:17Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | related example: http://stackoverflow.com/questions/20789976/python-pandas-dataframe-1st-line-issue-with-datetime-timedelta/20802902?noredirect=1#comment31195305_20802902
```
import datetime
import pandas as pd
parms = {'d': datetime.datetime(2013, 11, 5, 5, 56), 't':datetime.timedelta(0, 22500)}
df = pd.DataFrame(co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5696/events | https://github.com/pandas-dev/pandas/issues/5696 | 24,258,516 | MDU6SXNzdWUyNDI1ODUxNg== | 5,696 | Display Pure "Date"s Without Time | {
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"color": "00980... | closed | false | null | [] | null | 3 | 2013-12-13T16:58:01Z | 2013-12-13T17:30:57Z | 2013-12-13T17:04:52Z | CONTRIBUTOR | null | I have a `Series` of "dates" (actually `datetime`s all at midnight), Is there any way to get this `Series` or `column` to render as just the date without the time string of "00:00:00"?
```
In [248]:
pd.Series([datetime(2013,1,1),datetime(2013,2,1)])
Out[248]:
0 2013-01-01 00:00:00
1 2013-02-01 00:00:00
dtype: dat... | {
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"color": "006... | closed | false | null | [] | {
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"f... | 4 | 2013-12-13T17:09:12Z | 2017-05-04T13:41:27Z | 2017-05-04T13:41:27Z | NONE | null | The read_csv function gives strange results for certain input files
(pandas version: 0.12.0, Python version: 3.3.2, OS: Linux):
import io, pandas
f = io.StringIO("x\n\n1111111111111111111")
df = pandas.read_csv(f)
print(int(df.x[1])) # 1111111111111111168
We have data loss due to rounding! The correct behavior in my ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5698 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5698/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5698/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5698/events | https://github.com/pandas-dev/pandas/issues/5698 | 24,279,073 | MDU6SXNzdWUyNDI3OTA3Mw== | 5,698 | Series Changes breaks rpy2? | {
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"labels_url": "htt... | 63 | 2013-12-13T23:22:57Z | 2014-09-21T00:55:42Z | 2014-01-24T15:24:19Z | CONTRIBUTOR | null | It seems that the changes to to Series break the data conversion to R: running this Notebook doesn't work anymore with some dev version from last week:
https://gist.github.com/kevindavenport/7771325/raw/87ab5603f406729c6a3866f95af9a1ebfedcf619/Mahalanobis_Outliers.ipynb
The resulting error is this:
``` python
#xydata... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5699/events | https://github.com/pandas-dev/pandas/issues/5699 | 24,299,463 | MDU6SXNzdWUyNDI5OTQ2Mw== | 5,699 | Odd StringMixin behavior | {
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"followers_url": "https://api.github.com/users/... | 15 | 2013-12-14T20:58:43Z | 2013-12-14T23:15:34Z | 2013-12-14T22:57:24Z | MEMBER | null | A unicode-indexed frame:
```
In [2]: df = mkdf(3, 2, r_idx_type='u')
In [3]: df
Out[3]:
C0 C_l0_g0 C_l0_g1
R0
7זףאןמםצמ4 R0C0 R0C1
גקךכו441יץ R1C0 R1C1
35דז5ר13ג2 R2C0 R2C1
[3 rows x 2 columns]
In [4]: <Ctrl-D>
sqlite3.ProgrammingError: You must not use 8-bit bytestrings unless you use a ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5700/events | https://github.com/pandas-dev/pandas/pull/5700 | 24,299,757 | MDExOlB1bGxSZXF1ZXN0MTA4MzMyNTQ= | 5,700 | Add cdfplot | {
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"labels_url": "htt... | 4 | 2013-12-14T21:16:42Z | 2014-06-14T08:27:47Z | 2014-01-29T04:19:09Z | CONTRIBUTOR | null | WIP for now. Closes #2669
I'm using statsmodels' KDE implementation right now; it has a `cdf` method, but scipy's gaussian_kde doesn't. I need to check the math, but I think something like doing a cumsum on the density and normalizing by the sum should be the same.
I'm also going to add kwargs for things like the inv... | {
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