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https://api.github.com/repos/pandas-dev/pandas/issues/2701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2701/events | https://github.com/pandas-dev/pandas/issues/2701 | 9,992,313 | MDU6SXNzdWU5OTkyMzEz | 2,701 | multiindex column in to_excel | {
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"followers_url": "https://api.github.com/users/... | 18 | 2013-01-15T19:39:58Z | 2019-08-06T13:22:17Z | 2013-11-10T11:25:29Z | CONTRIBUTOR | null | link to #1651
Saving a multiindex column to_excel saves a sparse index.
Migrated from this [StackOverflow question](http://stackoverflow.com/questions/14341584/exporting-pandas-dataframe-with-multiindex), with a smaller DataFrame.
```
In [1]: m = MultiIndex.from_tuples([(1,1),(1,2)], names=['a','b'])
In [2]: df = D... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2702/events | https://github.com/pandas-dev/pandas/issues/2702 | 9,996,620 | MDU6SXNzdWU5OTk2NjIw | 2,702 | Bug in Fancy/Boolean Indexing with nested lists | {
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"f... | 9 | 2013-01-15T21:43:41Z | 2013-09-06T03:11:15Z | 2013-09-06T03:11:15Z | NONE | null | Fancy or Boolean indexing on a Series has two strange behaviors. My examples only show the behavior with Fancy indexing, but it's the same for Boolean indexing.
## LHS vs RHS length
```
>>> s = pd.Series(list('abc'))
>>> s[[0,1,2]] = range(27)
>>> list(s)
[0, 1, 2]
```
I would have expected an error,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2703/events | https://github.com/pandas-dev/pandas/issues/2703 | 10,006,419 | MDU6SXNzdWUxMDAwNjQxOQ== | 2,703 | DataFrame bug when setting column after filtering | {
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In [615]: df = pd.DataFrame([3,4,5],columns=['a'])
In [616]: df['b'] = 'nope'
In [617]: df[df['a'] == 3]['b'] = 'found'
In [618]: df
Out[618]:
a b
0 3 nope
1 4 nope
2 5 nope
#Expected
In [618]: df
Out[618]:
a b
0 3 found
1 4 nope
2 5 nope
```
I believe the intermediate filter s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2704 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2704/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2704/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2704/events | https://github.com/pandas-dev/pandas/issues/2704 | 10,058,188 | MDU6SXNzdWUxMDA1ODE4OA== | 2,704 | closed='both' option for resample | {
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"f... | 3 | 2013-01-17T13:26:47Z | 2019-12-24T04:22:16Z | null | CONTRIBUTOR | null | When I call resample to downsample a certain time series and aggregate the in-between values, sometimes I want both borders included in the aggregation bin (repeating the borders in two neighbouring bins).
This might be offered as a `closed='both'` option for `resample`.
An example when this might be necessary is e.g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2705/events | https://github.com/pandas-dev/pandas/issues/2705 | 10,074,417 | MDU6SXNzdWUxMDA3NDQxNw== | 2,705 | Loading of large pickled dataframes fails | {
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"f... | 15 | 2013-01-17T20:39:12Z | 2014-05-29T05:01:18Z | 2014-05-29T05:01:18Z | NONE | null | I tried pickling a very large dataframe (20GB or so) and that succeeded to write to disk, but when I try to read it, it fails with: ValueError: buffer size does not match array size
Now I did a bit of research and found the following:
http://stackoverflow.com/questions/12060932/unable-to-load-a-previously-dumped-pick... | {
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"followers_url": "https://api.github.com/users/wesm/f... | 4 | 2013-01-19T03:39:00Z | 2013-01-21T02:25:40Z | 2013-01-20T21:59:52Z | NONE | null | I am upgrading Pandas from 0.8.1 to 0.10.1.dev-f7f7e13 . My environment is Window XP with below: Python: 2.7.3 Numpy: 1.6.2 MPL: 1.1.1 Pandas: 0.10.1.dev-f7f7e13.
Then OK application on 0.8.1 now meets errors. I trace the root cause to filtering the duplicated index of Series. Detail in : http://stackoverflo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2707/events | https://github.com/pandas-dev/pandas/issues/2707 | 10,128,617 | MDU6SXNzdWUxMDEyODYxNw== | 2,707 | Buggy .ix assignment | {
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```
1) With numeric values everywhere:
df['tt1'] = df['tt2'] = 0
df.ix[1, ['tt1', 'tt2']] = [1, 2]
print df.ix[0:2,['tt1', 'tt2']]
Output:
tt1 tt2
0 0 0
1 1 2
2 0 0
2) With string values everywhere:
df['tt1'] = df['tt2'] = '0'
df.ix[1, ['tt1', 'tt2']] = ['1', '2']
print df.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2708/events | https://github.com/pandas-dev/pandas/pull/2708 | 10,129,745 | MDExOlB1bGxSZXF1ZXN0Mzc1Mjc5MA== | 2,708 | ENH/BUG/DOC: allow propogation and coexistance of numeric dtypes | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-01-19T21:27:44Z | 2014-06-17T21:41:04Z | 2013-02-10T16:19:30Z | CONTRIBUTOR | null | Support for numeric dtype propogation and coexistance in DataFrames. Prior to 0.10.2, numeric dtypes passed to DataFrames were always casted to `int64` or `float64`. Now, if a dtype is passed (either directly via the `dtype` keyword, a passed `ndarray`, or a passed `Series`, then it will be preserved in DataFrame opera... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2709 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2709/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2709/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2709/events | https://github.com/pandas-dev/pandas/issues/2709 | 10,131,107 | MDU6SXNzdWUxMDEzMTEwNw== | 2,709 | BLD: add 64-bit build machine to travis | {
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```
I'm still having problems with this:
data = ('a:b\n'
'c:d:e\n'
'f:g:h:i\n'
'j:k\n'
'ln\n')
##if first row does not match longest line length, you error out doing this
print pandas.read_csv(StringIO(data),header=None,sep=':')
##even if u specify names..
print pandas.read_csv(StringIO(data),header=None,s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2712/events | https://github.com/pandas-dev/pandas/pull/2712 | 10,132,741 | MDExOlB1bGxSZXF1ZXN0Mzc1MzkwMA== | 2,712 | DOC: whatsnew example for PR #2595 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2713/events | https://github.com/pandas-dev/pandas/issues/2713 | 10,133,079 | MDU6SXNzdWUxMDEzMzA3OQ== | 2,713 | Google Analytics does not do the right thing for segments | {
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"followers_url": "https://api.github.com/users/wesm/f... | 1 | 2013-01-20T05:18:32Z | 2013-01-20T16:39:57Z | 2013-01-20T16:39:57Z | CONTRIBUTOR | null | I'm getting an error with the segment option now. When I go to the query explorer, my segments look like this: "gaid::707967480"
http://ga-dev-tools.appspot.com/explorer/
So the error I'm getting is related to that string construction - is this a bug, or is this somehow my segment ids?
-------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2714/events | https://github.com/pandas-dev/pandas/issues/2714 | 10,141,264 | MDU6SXNzdWUxMDE0MTI2NA== | 2,714 | inserting new column with DataFrame.ix buggy | {
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In [22]: df
Out[22]:
A A B C
0 0.148734 -0.041765 1.575866 1.422524
1 0.188963 2.086219 1.595425 0.539364
2 1.498709 -0.338857 -0.210994 0.984697
3 -0.197789 -0.093690 -0.421010 0.710587
In [23]: df.ix[:, 'D'] = ['X', 'Y', 'Z', 'V']
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https://api.github.com/repos/pandas-dev/pandas/issues/2715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2715/events | https://github.com/pandas-dev/pandas/issues/2715 | 10,141,725 | MDU6SXNzdWUxMDE0MTcyNQ== | 2,715 | Add duplicated/drop_duplicates top-level array functions | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2716 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2716/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2716/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2716/events | https://github.com/pandas-dev/pandas/issues/2716 | 10,145,001 | MDU6SXNzdWUxMDE0NTAwMQ== | 2,716 | BUG: perf_HEAD reports wrong vbench name when an exception is raised | {
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Rather than adding all possible dialects to pandas, another option is to detect whether sqlalchemy is installed and prefer to use its DB support.
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I can fix for 0.10.2
```
In [4]: df = pd.DataFrame({ 'a' : 'foo', 'b' : 1. },index=np.arange(10))
In [6]: df .dtype... | {
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] | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-01-21T16:41:18Z | 2013-12-04T00:40:06Z | 2013-03-21T18:10:03Z | NONE | null | It looks to me like a bug in pandas.Series.
```
a = pd.Series([1,2,3,4])
b = a.reshape(2,2)
b
```
b has type Series but can not be displayed, the last statement gives exception, very lengthy, the last line is "TypeError: %d format: a number is required, not numpy.ndarray". b.shape returns (2,2), which contradicts its... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2720/events | https://github.com/pandas-dev/pandas/issues/2720 | 10,169,267 | MDU6SXNzdWUxMDE2OTI2Nw== | 2,720 | ENH: stat ops on mixed numeric dtypes | {
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```
(Pdb) p self.mixed_float.dtypes
A float32
B float32
C float16
D float64
# this coerces (no way around this)
(Pdb) p self.mixed_float.sum(1).dtype
dtype('float64')
# question is here - I can do block by block computations to give a mixed dtype res... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2721/events | https://github.com/pandas-dev/pandas/issues/2721 | 10,172,865 | MDU6SXNzdWUxMDE3Mjg2NQ== | 2,721 | Implement deep copy option in Series.copy | {
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> > > from pandas import Series, DataFrame
> > > s=Series([1,2,3])
> > > df=DataFrame(s)
> > > df.copy(deep=True)
> > > 0
> > > 0 1
> > > 1 2
> > > 2 3
> > > s.copy(deep=True... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2722/events | https://github.com/pandas-dev/pandas/issues/2722 | 10,190,905 | MDU6SXNzdWUxMDE5MDkwNQ== | 2,722 | Some dates cause error when calling dataframe.convert_objects | {
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<pre>
li = [datetime.datetime(3800, 1, 1), ]
li_df = DataFrame(li)
li_df.convert_objects()
</pre>
then raise the following exception:
<pre>
ValueError Traceback (most recent call last)
<ipython-input-112-1cb5a6d822e9> in <module>()
----> 1 li_df.conver... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2723/events | https://github.com/pandas-dev/pandas/pull/2723 | 10,191,293 | MDExOlB1bGxSZXF1ZXN0Mzc3OTY2Mg== | 2,723 | lag_plot() accepts variable lags | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-22T13:10:30Z | 2014-06-20T08:05:53Z | 2013-02-10T21:27:11Z | CONTRIBUTOR | null | the default is still 1, so nothing should change if one wants a 'classic' lag plot
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https://api.github.com/repos/pandas-dev/pandas/issues/2724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2724/events | https://github.com/pandas-dev/pandas/pull/2724 | 10,192,509 | MDExOlB1bGxSZXF1ZXN0Mzc4MDIzMw== | 2,724 | dataframe.convert_objects now ignores datetime64 outside supported timeperiod | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2725/events | https://github.com/pandas-dev/pandas/issues/2725 | 10,208,471 | MDU6SXNzdWUxMDIwODQ3MQ== | 2,725 | 0.10.1: DataFrame.plot(x='x', y='y') does not correctly plot one column versus another | {
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... | closed | false | null | [] | {
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df = DataFrame(index=range(100))
df['x'] = np.random.randn(100)
df['y'] = df['x']
df.plot(x='x', y='y', linestyle='None', marker='o')
```
I expected to see column 'y' plotted against column 'x' and, as such, to see scatter points along the 45-degree line. Instead, I get a strange scatter plot. Am I doing something... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2726/events | https://github.com/pandas-dev/pandas/issues/2726 | 10,209,721 | MDU6SXNzdWUxMDIwOTcyMQ== | 2,726 | 0.10.1: DataFrame.plot() does not use integer index as ticks for x axis | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-01-22T21:18:10Z | 2016-10-12T23:03:29Z | 2013-02-09T21:08:57Z | NONE | null | ```
df = DataFrame(index=range(99, -1, -1))
df['y'] = range(100)
df.plot()
```
I observe a 45-degree line while what I expect to see is the -45-degree line (from top left corner to bottom right corner). Am I doing something wrong?
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https://api.github.com/repos/pandas-dev/pandas/issues/2727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2727/events | https://github.com/pandas-dev/pandas/issues/2727 | 10,212,086 | MDU6SXNzdWUxMDIxMjA4Ng== | 2,727 | BUG: Mismatch between get and set behavior for slices of floating indices | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-01-22T22:22:18Z | 2013-02-26T22:24:32Z | 2013-02-11T00:32:19Z | CONTRIBUTOR | null | When _df.index.inferred_type_ is _'floating'_, _df.ix[start:end]_ uses only label-based indexing only when **both** _start_ and _end_ are floating on the ___getitem___ side but when **either** _start_ or _end_ are floating on the ___setitem___ side (where _start_ and _end_ are both either integers or floats within epsi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2728/events | https://github.com/pandas-dev/pandas/issues/2728 | 10,215,730 | MDU6SXNzdWUxMDIxNTczMA== | 2,728 | DataFrame.copy(), at least, should be threadsafe | {
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"f... | 13 | 2013-01-23T00:06:26Z | 2021-08-11T10:25:46Z | null | NONE | null | dataframe.copy() should happen atomically/be threadsafe, meaning that it should produce a consistent dataframe even if the call to .copy() is made while another thread is deleting entries from the dataframe, or if another thread calls a deletion method while the call to .copy() is working (in other words, i guess .copy... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2729/events | https://github.com/pandas-dev/pandas/issues/2729 | 10,215,749 | MDU6SXNzdWUxMDIxNTc0OQ== | 2,729 | http://pandas.pydata.org/pandas-docs/stable/gotchas.html should note that DataFrame objects are not threadsafe | {
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(as of 0.7.3, they still are not threadsafe afaict)
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https://api.github.com/repos/pandas-dev/pandas/issues/2730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2730/events | https://github.com/pandas-dev/pandas/pull/2730 | 10,217,593 | MDExOlB1bGxSZXF1ZXN0Mzc5MjkzNg== | 2,730 | BUG: Mismatch between get and set behavior for slices of floating indices (fixes #2727) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2731/events | https://github.com/pandas-dev/pandas/issues/2731 | 10,220,360 | MDU6SXNzdWUxMDIyMDM2MA== | 2,731 | BUG? Inconsistent index type inferencing for mixed integer/floating, index=list vs index=Index(list) | {
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```
In [2]: df=p.DataFrame(["a", "b",... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2732/events | https://github.com/pandas-dev/pandas/issues/2732 | 10,224,042 | MDU6SXNzdWUxMDIyNDA0Mg== | 2,732 | Installation fails due to lack of numpy | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-23T08:15:48Z | 2014-04-23T19:36:56Z | 2013-03-14T03:48:03Z | CONTRIBUTOR | null | I have a case where I wrote a requirements file to install pandas. Included in the requirements file is an entry to install numpy alongside. The installation fails because pandas is trying to do an import (in its setup.py) of numpy which isn't installed yet.
Due to the fact that either setuptools or distribute is used... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2733/events | https://github.com/pandas-dev/pandas/issues/2733 | 10,227,303 | MDU6SXNzdWUxMDIyNzMwMw== | 2,733 | read_csv: `usecols` doesn't work if separator is not "," | {
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"f... | 10 | 2013-01-23T10:30:39Z | 2014-03-01T07:45:12Z | 2014-03-01T07:45:12Z | NONE | null | If I have data (without a header) separated by "," usecols works, however if the data is separated by white space it doesn't seem to work:
``` python
In [30]: data = '1,2,3\n4,5,6\n7,8,9'
In [31]: pd.read_csv(StringIO(data), usecols=[0, 1], header=None)
Out[31]:
0 1
0 1 2
1 4 5
2 7 8
In [32]: data = '1 2 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2734/events | https://github.com/pandas-dev/pandas/pull/2734 | 10,237,738 | MDExOlB1bGxSZXF1ZXN0MzgwMjcyOA== | 2,734 | ENH: provide squeeze method for removing 1-len dimensions, close (GH #2544) | {
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In [1]: p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
...: major_axis=date_range('20010102',periods=4),
...: minor_axis=['A','B','C','D'])
...:
In [2]: p
Out[2]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2735/events | https://github.com/pandas-dev/pandas/issues/2735 | 10,242,055 | MDU6SXNzdWUxMDI0MjA1NQ== | 2,735 | Add suppress option to pd.set_option | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-01-23T17:28:13Z | 2013-03-11T14:59:23Z | 2013-03-11T14:59:23Z | CONTRIBUTOR | null | I do computational physics and economics and often generate data that is very close to zero (on the order of 1e-14 to 1e-16). When printing out a numpy array I often use the command
``` python
import numpy as np
np.set_printoptions(suppress=True)
```
This will cause items close to zero to be displayed as zero.
I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2736/events | https://github.com/pandas-dev/pandas/issues/2736 | 10,243,501 | MDU6SXNzdWUxMDI0MzUwMQ== | 2,736 | Add "corner" function | {
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I often have `DataFrame`s that have 4 - 5 `MultiIndex` levels on each axis and I can't get a visual idea about the structure unless I do something like
``` python
df.head().T.head().T
```
Of course, in the case that the indices are simp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2737/events | https://github.com/pandas-dev/pandas/pull/2737 | 10,244,580 | MDExOlB1bGxSZXF1ZXN0MzgwNTc0OA== | 2,737 | improving docstring for swaplevel method | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2738 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2738/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2738/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2738/events | https://github.com/pandas-dev/pandas/issues/2738 | 10,244,942 | MDU6SXNzdWUxMDI0NDk0Mg== | 2,738 | Handling of duplicate columns in pandas.io.sql.read_frame | {
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"description": "2014-02-3: previous version released",
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"labels_url": "htt... | 9 | 2013-01-23T18:42:41Z | 2014-04-04T15:03:29Z | 2014-01-24T14:35:14Z | NONE | null | Calling pandas.io.sql.read_frame can results in data frame with duplicate column names. For example when SQL query contains joins on tables with duplicate columns.
Data frames with duplicate column names cause errors in many pandas functions. I can't even rename columns as df.columns = new_columns generates errors.
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https://api.github.com/repos/pandas-dev/pandas/issues/2739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2739/events | https://github.com/pandas-dev/pandas/pull/2739 | 10,246,232 | MDExOlB1bGxSZXF1ZXN0MzgwNjU3NA== | 2,739 | ENH: add option chop_threshold to control display of small numerical values as zero | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-01-23T19:16:43Z | 2014-06-12T08:13:07Z | 2013-02-10T21:33:29Z | NONE | null | Doesn't cover complex numbers for now (no formatter implemented in format.py)
I think the "supress" kwd choice in numpy is not that awesome. So I borrowed `chop` from
the mathematica parlance.
#2734
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https://api.github.com/repos/pandas-dev/pandas/issues/2740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2740/events | https://github.com/pandas-dev/pandas/pull/2740 | 10,246,802 | MDExOlB1bGxSZXF1ZXN0MzgwNjg3OA== | 2,740 | A patch to make pandas install and use numpy if it doesn't find it on the target host | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-23T19:30:53Z | 2014-06-30T05:04:02Z | 2013-02-10T21:53:47Z | CONTRIBUTOR | null | Pandas will normally fail to install if numpy is not installed on the target host. This should normally be resolved by specifying numpy as a dependency but Pandas will still require that numpy is installed first due to its need of the numpy include files.
This patch resolves this problem by changing the way the numpy ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2741 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2741/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2741/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2741/events | https://github.com/pandas-dev/pandas/issues/2741 | 10,251,888 | MDU6SXNzdWUxMDI1MTg4OA== | 2,741 | read_csv character encoding bug? | {
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"description": "IO issues that don't fit... | closed | false | null | [] | {
"closed_at": "2016-10-03T08:52:13Z",
"closed_issues": 733,
"created_at": "2016-03-11T21:24:45Z",
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"followers_url": "https://api.github.com/users/jr... | 10 | 2013-01-23T21:45:55Z | 2016-08-01T21:17:30Z | 2016-08-01T21:17:12Z | CONTRIBUTOR | null | This is a weird one from [StackOverflow](http://stackoverflow.com/questions/14488697/pandas-read-csv-failing-on-columns-with-null-characters), this file has some `\x00`s which seem to be ignored when printing but confuse `read_csv`:
```
x = 'x,y\n \x00\x00\x00,Reg\n \x00\x00\x00,Reg\nI,Swp\nI,Swp\n'
X = StringIO(x)
I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2742 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2742/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2742/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2742/events | https://github.com/pandas-dev/pandas/issues/2742 | 10,256,386 | MDU6SXNzdWUxMDI1NjM4Ng== | 2,742 | Incorrect period end_time | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-01-24T00:01:57Z | 2013-04-01T19:58:06Z | 2013-04-01T19:58:06Z | NONE | null | Used versions: Python 2.7.3 Pandas 0.10.1
Code:
from pandas import Period
from datetime import datetime
Period(datetime(2013, 1, 1, 0, 0), freq='W-SAT')
outputs: Period('2012-12-30/2013-01-05', 'W-SAT') - correct
Period(datetime(2013, 1, 1, 0, 0), freq='W-SAT').end_time
outputs: Timestamp: 2013-01-11 23:59:59.9999... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2743 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2743/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2743/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2743/events | https://github.com/pandas-dev/pandas/issues/2743 | 10,257,433 | MDU6SXNzdWUxMDI1NzQzMw== | 2,743 | tiny dataframe non-string colors bug | {
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"f... | 3 | 2013-01-24T00:44:16Z | 2013-03-03T20:15:58Z | 2013-03-03T20:15:58Z | NONE | null | Hi,
If we specify custom rgb 3-ples for rcParams['axes.color_cycle'], DataFrame.plot breaks because _get_colors expects strings (the usual rcParams['axes.color_cycle'] = ['b','g'...] etc).
Test case:
rcParams['axes.color_cycle'] = [(1,1,1),(.1,.1,.1)]
DataFrame({'a':[1,1], 'b':[2,2]}).plot()
Seems like having _get_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2744 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2744/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2744/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2744/events | https://github.com/pandas-dev/pandas/issues/2744 | 10,257,676 | MDU6SXNzdWUxMDI1NzY3Ng== | 2,744 | `sqlite3.OperationalError: too many SQL variables` on write_frame with > 1000 columns | {
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"f... | 1 | 2013-01-24T00:54:00Z | 2014-05-29T05:53:27Z | 2014-05-29T05:53:27Z | NONE | null | I'm doing some data processing in pandas, "pivoting" the dataframe and saving it to a sqlite table.
The data processing is rather expensive and, for performance reasons, I'd like to process it once with pandas and write the cross-tabulated data frame into a sqlite table for later queries. This strategy works well unt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2745 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2745/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2745/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2745/events | https://github.com/pandas-dev/pandas/issues/2745 | 10,267,729 | MDU6SXNzdWUxMDI2NzcyOQ== | 2,745 | regression in 0.10.1 with boolean indexing? | {
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"followers_url": "https://api.github.com/users/wesm/... | 19 | 2013-01-24T10:30:18Z | 2013-09-05T15:00:16Z | 2013-03-22T18:54:28Z | CONTRIBUTOR | null | this used to work in 0.10 but now fails in 0.10.1:
```
import pandas
df = pandas.DataFrame(index=[1,2])
df['test'] = [1,2]
df['test'][[True, False]] = [0]
```
Now gives:
ValueError: Length of replacements must equal series length
possibly related to closed issue https://github.com/pydata/pandas/issues/2703
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https://api.github.com/repos/pandas-dev/pandas/issues/2746 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2746/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2746/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2746/events | https://github.com/pandas-dev/pandas/issues/2746 | 10,272,254 | MDU6SXNzdWUxMDI3MjI1NA== | 2,746 | BUG: partial boolean indexing fails with dtype = int | {
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"followers_url": "https://api.github.com/users/wesm/... | 7 | 2013-01-24T13:22:45Z | 2013-03-01T18:30:55Z | 2013-03-01T18:30:55Z | CONTRIBUTOR | null | this fails because we are putmasking on a dtype=int ndarray
need to cast if this is the case (in series.where)
```
import pandas
df = pandas.DataFrame(index=[1,2])
df['test'] = [1,2]
df['test'][[True, False]] = pandas.Series([0],index=[1])
---------------------------------------------------------------------------
T... | {
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``` python
def test_dataframe_clip(lb=-1, ub=1):
df = pd.DataFrame(np.random.randn(1000,2))
lb_mask = df.values <= lb
ub_mask = df.values >= ub
mask = ~lb_mask & ~ub_mask
clipped_df = df.clip(lb, ub)
assert (clipped_df.values[lb_mask] == lb).all()
assert (clip... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2748 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2748/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2748/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2748/events | https://github.com/pandas-dev/pandas/pull/2748 | 10,278,454 | MDExOlB1bGxSZXF1ZXN0MzgyMjkzNQ== | 2,748 | Add a test case to io.test_parsers about issue #2733 | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-24T16:18:16Z | 2014-08-04T13:02:02Z | 2013-02-10T22:06:19Z | CONTRIBUTOR | null | The test case doesn't fix the issue. Just a test about the `usecols` parameter in `read_csv` when there are whitespaces in the reading data. I'm not sure the test case is worth it. Let me know.
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https://api.github.com/repos/pandas-dev/pandas/issues/2749 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2749/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2749/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2749/events | https://github.com/pandas-dev/pandas/issues/2749 | 10,281,727 | MDU6SXNzdWUxMDI4MTcyNw== | 2,749 | Feature Request: DataFrame.describe() | {
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Thank You.
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https://api.github.com/repos/pandas-dev/pandas/issues/2750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2750/events | https://github.com/pandas-dev/pandas/pull/2750 | 10,282,116 | MDExOlB1bGxSZXF1ZXN0MzgyNDgzNw== | 2,750 | BUG: DataFrame.clip arguments inconsistent (close #GH 2747) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2751 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2751/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2751/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2751/events | https://github.com/pandas-dev/pandas/issues/2751 | 10,309,903 | MDU6SXNzdWUxMDMwOTkwMw== | 2,751 | BUG: datetime64[ns] conversion to object in DataFrame constructor inconsistent | {
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```
In [55]: df = pd.DataFrame({'a':[1,2,4,7],
'b':[1.2, 2.3, 5.1, 6.3],
'c':list('abcd'),
'd':[pd.datetime(2000,1,1) for i in range(4)]})
In [56]: df
Out[56]:
a b c d
0 1 1.2 a 2000-01-01 00:00:00
1 2 2.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2752 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2752/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2752/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2752/events | https://github.com/pandas-dev/pandas/pull/2752 | 10,311,582 | MDExOlB1bGxSZXF1ZXN0MzgzOTY5Nw== | 2,752 | BUG: various bug fixes for DataFrame/Series construction | {
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"followers_url": "https://api.github.com/users/wesm/... | 10 | 2013-01-25T13:55:02Z | 2014-06-16T01:01:22Z | 2013-02-10T20:39:14Z | CONTRIBUTOR | null | 0 and 1 len ndarrays not inferring dtype correctly
datetimes that are single objects not inferring dtype
mixed datetimes and objects (GH #2751), casting datetimes to object
timedelta64 creation on series subtraction (of datetime64[ns])
astype on datetimes to object are now handled (as well as NaT conversions to np.nan)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2753 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2753/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2753/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2753/events | https://github.com/pandas-dev/pandas/issues/2753 | 10,320,750 | MDU6SXNzdWUxMDMyMDc1MA== | 2,753 | BUG resample business days/months | {
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"followers_url": "https://api.github.com/users/wesm/... | 6 | 2013-01-25T18:14:57Z | 2016-10-12T23:03:29Z | 2013-04-01T10:30:45Z | CONTRIBUTOR | null | The first resample starts at 2000-01-03 (as does `foo`) whilst the second resample starts at 2000-01-04, should it?
```
foo = pd.Series(index=pd.bdate_range(datetime.date(2000,1,1),datetime.date(2001,1,1)))
foo.resample("BMS")
foo.resample("BMS").resample("B")
```
_Migrated from [StackOverflow](http://stackoverflow.c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2754 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2754/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2754/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2754/events | https://github.com/pandas-dev/pandas/issues/2754 | 10,320,951 | MDU6SXNzdWUxMDMyMDk1MQ== | 2,754 | ENH: sql support for NaN/NaT conversions | {
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"description": "IO issues that don't fit... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2013-01-25T18:21:21Z | 2014-09-13T21:48:56Z | 2014-09-13T21:48:56Z | CONTRIBUTOR | null | UPDATE from @jorisvandenbossche:
Overview of current status writing nan values (see also tests added in #7100):
- For MySQL using pymysql/MySQLdb nothing works: you get message `Unknown column 'nan' in 'field list'` (see also eg http://stackoverflow.com/questions/23353732/python-pandas-write-to-sql-with-nan-values)
- ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2755 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2755/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2755/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2755/events | https://github.com/pandas-dev/pandas/pull/2755 | 10,323,463 | MDExOlB1bGxSZXF1ZXN0Mzg0NTg5Ng== | 2,755 | BUG: issue in HDFStore with too many selectors in a where | {
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just to be clear, you really have to do something like this:
select.('df',[ ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2756/events | https://github.com/pandas-dev/pandas/issues/2756 | 10,325,546 | MDU6SXNzdWUxMDMyNTU0Ng== | 2,756 | plotting 3x3 scatter-matrix: missing labels | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-01-25T20:40:53Z | 2013-03-18T18:52:00Z | 2013-03-18T18:26:02Z | NONE | null | I create a pandas scatter-matrix usng the following code:
``` python
import numpy as np
import pandas as pd
a = np.random.normal(1, 3, 100)
b = np.random.normal(3, 1, 100)
c = np.random.normal(2, 2, 100)
df = pd.DataFrame({'A':a,'B':b,'C':c})
pd.scatter_matrix(df, diagonal='kde')
```
This result in the following sc... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2757/events | https://github.com/pandas-dev/pandas/pull/2757 | 10,327,245 | MDExOlB1bGxSZXF1ZXN0Mzg0Nzg3Nw== | 2,757 | DataFrame.from_items clean-up | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-01-25T21:34:44Z | 2013-02-04T12:07:27Z | 2013-02-04T12:07:19Z | CONTRIBUTOR | null | - Improve docstrings and error messages
- Convert final `elif` to `else`: the `elif` condition was guaranteed to be true, so this is a silent code cleanup -- it does not change the behavior of the code.
### Testing
I checked that the `from_items` method was working as usual, and that the new docstrings and error messa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2758 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2758/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2758/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2758/events | https://github.com/pandas-dev/pandas/pull/2758 | 10,335,801 | MDExOlB1bGxSZXF1ZXN0Mzg1MTk5OA== | 2,758 | Updated pandas.io.data.Options | {
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"followers_url": "https://api.github.com/users/wesm/... | 8 | 2013-01-26T07:45:56Z | 2014-06-13T11:53:46Z | 2013-02-10T21:16:38Z | CONTRIBUTOR | null | Mostly housekeeping, but there were a few functionality additions:
- BUG: The URL's for current month options have been changed by yahoo so I changed the methods to reflect that.
- ENH: Now using lxml to html scraping under the hood (was using BeautifulSoup).
- ENH: When `Options.get_xxx_data()`, where xxx is either `o... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2759/events | https://github.com/pandas-dev/pandas/issues/2759 | 10,352,515 | MDU6SXNzdWUxMDM1MjUxNQ== | 2,759 | Integer dtype is promoted to int64 | {
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"followers_url": "https://api.github.com/users/wesm/... | 11 | 2013-01-27T10:17:34Z | 2013-02-11T07:44:07Z | 2013-02-11T02:17:32Z | CONTRIBUTOR | null | It seems that DataFrame (both constructor and `astype`) promotes the dtypes np.int[8,16,32] to np.int64 ::
```
>>> pd.DataFrame(data=np.ones((10, 2), dtype=np.int32), dtype=np.int32).get_dtype_counts()
int64 2
```
I'm not sure whether this is a but or on purpose (if so, a note in the docstring would be great).
Is ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2760/events | https://github.com/pandas-dev/pandas/issues/2760 | 10,358,277 | MDU6SXNzdWUxMDM1ODI3Nw== | 2,760 | Series near-zero subtraction loss of precision | {
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Data set:
```
a = [49105.962326,
49106.062271,
49106.112167,
49106.222305,
49106.332290]
s = pd.Series(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2761/events | https://github.com/pandas-dev/pandas/issues/2761 | 10,359,585 | MDU6SXNzdWUxMDM1OTU4NQ== | 2,761 | ENH: should shift return same dtype objects as input? | {
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"f... | 1 | 2013-01-27T21:48:41Z | 2013-02-15T00:29:44Z | 2013-02-15T00:29:44Z | CONTRIBUTOR | null | see the example in #2759
should certain operations, e.g. `shift` preserve the dtype of the output object,
simulated like this (rather than return an upcasted version)
this obviously is easier if we had an integer NA
would have to add a fillna keyword to shift to make this work
(so we don't have to guess on the fill v... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2762/events | https://github.com/pandas-dev/pandas/pull/2762 | 10,369,748 | MDExOlB1bGxSZXF1ZXN0Mzg2MjQ2OQ== | 2,762 | Update doc/source/install.rst | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-28T10:00:50Z | 2013-02-08T10:59:33Z | 2013-02-08T10:59:28Z | NONE | null | Python 2.5 isn't supported ( #2323 )
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https://api.github.com/repos/pandas-dev/pandas/issues/2763 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2763/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2763/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2763/events | https://github.com/pandas-dev/pandas/issues/2763 | 10,385,548 | MDU6SXNzdWUxMDM4NTU0OA== | 2,763 | groupby first and last modify datatype | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-01-28T18:06:38Z | 2013-03-24T19:51:29Z | 2013-03-24T19:50:25Z | NONE | null | Using `groupby(level=0).first()` or `groupby(level=0).last()` to eliminate duplicate index entries modifies integer data types to float.
```
import pandas as pd
idx = range(10)
idx.append(9)
df = pd.Series(data=range(11), index=idx, name='IntCol')
print df
print df.dtype
0 0
1 1
2 2
3 3
4 4
5 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2764/events | https://github.com/pandas-dev/pandas/issues/2764 | 10,391,794 | MDU6SXNzdWUxMDM5MTc5NA== | 2,764 | resample time series with 2 quarters | {
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... | 2 | 2013-01-28T20:49:06Z | 2016-04-08T13:13:51Z | 2016-04-08T13:13:51Z | NONE | null | see this question on so: http://stackoverflow.com/questions/14569223/timegrouper-pandas
``` python
In [213]: idx = pd.date_range('2008-07-01', '2010-12-01', freq='MS')
In [214]: s = pd.Series(np.random.randn(idx.size), index=idx)
In [215]: s.index[0] + pd.datetools.QuarterEnd(startingMonth=12) * 2
Out[215]: <Timesta... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2765 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2765/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2765/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2765/events | https://github.com/pandas-dev/pandas/issues/2765 | 10,393,466 | MDU6SXNzdWUxMDM5MzQ2Ng== | 2,765 | BUG: Various inconsistencies in DataFrame __getitem__ and __setitem__ behavior | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2013-01-28T21:30:54Z | 2013-02-10T22:38:03Z | 2013-02-10T22:38:03Z | CONTRIBUTOR | null | ##### ___setitem___ fails with row slices supported by ___getitem___
``` python
In [27]: df=p.DataFrame(
....: {"alpha": ["a", "b", "c", "d", "e"],
....: "beta":[1,2,3,4,5]},
....: columns=["alpha","beta"],
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [28]: d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2766 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2766/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2766/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2766/events | https://github.com/pandas-dev/pandas/pull/2766 | 10,393,929 | MDExOlB1bGxSZXF1ZXN0Mzg3NDM5OQ== | 2,766 | BUG: Various inconsistencies in DataFrame getitem/setitem behavior (fixes #2765) | {
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Note, to address " ___getitem___ does not reindex boolean Series key but ___setitem___ does ", I have changed the ___getitem___ behavior rather than the ___setitem___ behavior because it is more consistent with other indexing behavior and does not break any tests...just in case, I have inserted a warning ab... | {
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... | closed | false | null | [] | null | 1 | 2013-01-29T01:49:03Z | 2013-01-29T02:53:21Z | 2013-01-29T02:53:21Z | MEMBER | null | http://stackoverflow.com/questions/14511752/pandas-3x3-scatter-matrix-missing-labels
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https://api.github.com/repos/pandas-dev/pandas/issues/2768 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2768/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2768/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2768/events | https://github.com/pandas-dev/pandas/issues/2768 | 10,418,350 | MDU6SXNzdWUxMDQxODM1MA== | 2,768 | Incorrect calculation of weekofyear? | {
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``` python
In [25]: import mx.DateTime
...: from datetime import datetime
...: d = datetime(2013,12,31)
...:
In [26]: d.isocalendar()
Out[26... | {
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either doesn't run the `tearDown`, or maybe the file handle isn't closed so
the `os.remove` fails.
One example which left behind a file in the root of the source tree.
``` python
λ nosetests pandas/io/tests/test_pytables.py
.S......SSSS..^CTrac... | {
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... | 34 | 2013-01-29T18:36:11Z | 2017-12-04T15:32:22Z | 2017-12-04T15:32:21Z | NONE | null | Using pandas 0.10. If we create a Dataframe with a multi-index, then delete all the rows with value X, we'd expect the index to no longer show value X. But it does.
Note the apparent inconsistency between "index" and "index.levels" -- one shows the values have been deleted but the other doesn't.
``` python
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https://api.github.com/repos/pandas-dev/pandas/issues/2771 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2771/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2771/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2771/events | https://github.com/pandas-dev/pandas/issues/2771 | 10,435,358 | MDU6SXNzdWUxMDQzNTM1OA== | 2,771 | BLD: do we need to add scipy to build matrix? (for FULL_DEPS) | {
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https://travis-ci.org/jreback/pandas/jobs/4457513
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https://api.github.com/repos/pandas-dev/pandas/issues/2772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2772/events | https://github.com/pandas-dev/pandas/issues/2772 | 10,445,134 | MDU6SXNzdWUxMDQ0NTEzNA== | 2,772 | GroupBy on DatetimeIndex with float32 values VERY slow | {
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``` ipython
In [35]: %timeit a.groupby(level=0).last()
1 loops, best of 3: 2.11 s per loop
In [36]: a = a.astype(np.float64)
In[37]: %timeit a.groupby(level=0).last()
1000 loops, best of 3: 911 us per loop
```
Either way, the result of the groupby is ... | {
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df = get_joined_data(data_prefix, data_rev_prefix, date_prefix, store_filename)
File "XXXXXX", line 739, in get_joined_data
write_dataframe("joined_{0}".format(date_prefix), df, store)
File "XXXXXXX",... | {
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pandas.DataFrame(columns=["x"]).x.fillna(method="pad", inplace=1)
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Note, to address `__getitem__` does not reindex boolean Series key but `__setitem__` does ", I have changed the `__getitem__` behavior rather than the `__setitem__` behavior because it is more consistent with other indexing behavior and does not break any tests...just in case, I have inserted a warning ab... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2777 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2777/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2777/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2777/events | https://github.com/pandas-dev/pandas/pull/2777 | 10,468,172 | MDExOlB1bGxSZXF1ZXN0MzkxMDgxNw== | 2,777 | BUG: Mismatch between get and set behavior for slices of floating indices (fixes #2727) | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-01-30T17:45:07Z | 2014-06-26T00:45:12Z | 2013-02-11T00:08:51Z | CONTRIBUTOR | null | fixes #2727: `_NDFrameIndexer._convert_to_indexer` modified to match behavior in `_NDFrameIndexer._get_slice_axis`, new tests added to existing unit test
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https://api.github.com/repos/pandas-dev/pandas/issues/2778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2778/events | https://github.com/pandas-dev/pandas/pull/2778 | 10,469,061 | MDExOlB1bGxSZXF1ZXN0MzkxMTMwOQ== | 2,778 | BUG: fillna with method segfaults on zero-length input (fixes #2775) | {
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"followers_url": "https://api.github.com/users/wesm/... | 12 | 2013-01-30T18:10:19Z | 2014-06-16T15:02:12Z | 2013-02-10T16:43:27Z | CONTRIBUTOR | null | fixes #2775
just added a check for zero-length data to the backfill and pad templates
not sure if I should add test coverage? the problem is that the tests will not fail without the fix, but rather segfault, so it might not be a good idea
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https://api.github.com/repos/pandas-dev/pandas/issues/2779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2779/events | https://github.com/pandas-dev/pandas/issues/2779 | 10,475,951 | MDU6SXNzdWUxMDQ3NTk1MQ== | 2,779 | read_csv() force dtype or return np.nan (missing) on a column | {
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"f... | 3 | 2013-01-30T21:05:16Z | 2014-06-25T01:21:48Z | 2013-03-11T16:41:41Z | NONE | null | I have data for which I know what the data type should be for each column, e.g. float. Occasionally these columns will also have spurious non-float string values due to erroneous data processing/pivoting up stream. I would like read_csv to help me ignore this (non-predictable) spurious data by parsing them as np.nan (m... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2780/events | https://github.com/pandas-dev/pandas/pull/2780 | 10,476,198 | MDExOlB1bGxSZXF1ZXN0MzkxNTAyOQ== | 2,780 | ENH: time slicing on datetime indicies (closes #2681) | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-01-30T21:12:44Z | 2014-06-24T08:47:20Z | 2013-02-10T22:33:39Z | CONTRIBUTOR | null | closes #2681
Added support for `df.ix[start_time:end_time]`, where `df.index` is a `DateTimeIndex`, both on the `__getitem__` and `__setitem__` side; test coverage in test_frame.py
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https://api.github.com/repos/pandas-dev/pandas/issues/2781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2781/events | https://github.com/pandas-dev/pandas/issues/2781 | 10,496,294 | MDU6SXNzdWUxMDQ5NjI5NA== | 2,781 | Periods don't sort correctly | {
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"followers_url": "https://api.github.com/users/wesm/... | 5 | 2013-01-31T11:06:47Z | 2013-03-28T05:40:23Z | 2013-03-28T05:40:23Z | CONTRIBUTOR | null | Unless I'm mistaken I think the following unit-test should pass:
``` python
def test_period_sorting():
periods = [pd.Period('2008-04', 'M'),
pd.Period('2008-02', 'M'),
pd.Period('2008-01', 'M'),
pd.Period('2008-05', 'M'),
pd.Period('2008-06', 'M'),
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https://api.github.com/repos/pandas-dev/pandas/issues/2782 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2782/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2782/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2782/events | https://github.com/pandas-dev/pandas/issues/2782 | 10,500,067 | MDU6SXNzdWUxMDUwMDA2Nw== | 2,782 | Period slicing with period_range returns error | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-01-31T13:27:26Z | 2013-02-10T02:11:03Z | 2013-02-10T02:11:03Z | NONE | null | Slicing a TimeSeries object with a PeriodIndex fails with an index out of bounds error
Example
dates = pd.period_range(start='01-Apr-11',end='31-Mar-12 23:00',freq='h')
ts = pd.TimeSeries(0,dates)
ts[ts.index[4:10]]
Traceback (most recent call last):
File "<ipython-input-2-c28eee792d28>", line 3, in <module>
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2783 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2783/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2783/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2783/events | https://github.com/pandas-dev/pandas/issues/2783 | 10,506,357 | MDU6SXNzdWUxMDUwNjM1Nw== | 2,783 | Axis Indexing Issue | {
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``` python
import numpy as np
from pandas import DataFrame
x = DataFrame(randn(3, 2))
xm = x.mean(axis=0) # broadcast along the rows
xc1 = x - xm
xc2 = x.sub(xm, axis=1) # broadcast along rows with axis == 1??
assert np.array... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2784 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2784/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2784/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2784/events | https://github.com/pandas-dev/pandas/issues/2784 | 10,507,695 | MDU6SXNzdWUxMDUwNzY5NQ== | 2,784 | Another HDFStore error | {
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"f... | 26 | 2013-01-31T17:00:58Z | 2013-02-18T23:07:47Z | 2013-02-18T23:07:47Z | NONE | null | After a long run of extracting features for some random forest action I ran into this when serializing the features:
Traceback (most recent call last):
File "XXXXX.py", line 1043, in <module>
write_dataframe("features", all_df, store)
File "XXXXX.py", line 55, in write_dataframe
store[name] = df
File "/L... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2785 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2785/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2785/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2785/events | https://github.com/pandas-dev/pandas/pull/2785 | 10,520,282 | MDExOlB1bGxSZXF1ZXN0MzkzNjc3OA== | 2,785 | BUG: Period/DateTime slicing and setitem fixes (fixes #2782, #2788, #2789) | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-01-31T22:23:13Z | 2014-06-18T19:02:10Z | 2013-02-09T23:04:59Z | CONTRIBUTOR | null | fixes #2782
simple fix...can't see any reason why the original code was doing what it was, especially since indices are immutable
added test coverage in test_series.py
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```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(np.random.random((3,4)))
In [4]: cols = pd.Index(['a','a','a','a'])
In [5]: df.columns = cols
In [6]: df.applymap(s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2787 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2787/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2787/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2787/events | https://github.com/pandas-dev/pandas/issues/2787 | 10,524,476 | MDU6SXNzdWUxMDUyNDQ3Ng== | 2,787 | DatetimeIndex.resolution throws NameError | {
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``` python
34 @classmethod
35 def get_str(cls, reso):
36 return {RESO_US : 'microsecond',
37 RESO_SEC : 'second',
38 RESO_MIN: 'minute',
NameError: global name 'RESO_US' is not defined
```
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``` python
In [1]: import pandas as p
In [2]: import numpy as np
In [3]: index=p.date_range("2012-01-01", "2012-01-02",
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``` python
In [1]: from pandas import Timestamp as ts, DataFrame as df
In [2... | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-02-02T12:26:14Z | 2016-08-16T00:46:47Z | 2016-08-16T00:46:47Z | CONTRIBUTOR | null | example of impl. I think this is pretty straightforward; what is hard is align (well not that hard but needs to accept a lot of different cases)
http://stackoverflow.com/questions/14650341/boolean-mask-in-pandas-panel
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"f... | 1 | 2013-02-02T17:59:48Z | 2016-04-09T23:12:07Z | 2016-04-09T23:12:07Z | NONE | null | Refer to https://github.com/pydata/pandas/issues/1889#issuecomment-11601189
And below is the completed code to do that. Can put it into the codebase and make it as the default option to display dataframe ?
def _sw(df, up_rows=10, down_rows=5, left_cols=4, right_cols=3):
''' display df data at four corners
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I've added some missing indentation, which fixes the issue for me.
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``` python
In [1]: import pandas as p
In [2]: import numpy as np
In [3]: df=p.DataFrame({'a': [... | {
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boolean indexing, if possible?
Its a pretty limited case, in that you have to have all values in a column non-null
and they all have to be integers (just casted as floats)
this is actually a little tricky to implement as this has to be done column by column (and i... | {
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"followers_url": "https://api.github.com/users/wesm/... | 10 | 2013-02-04T10:25:59Z | 2014-06-16T03:40:19Z | 2013-02-10T21:15:18Z | CONTRIBUTOR | null | Added functionality of listing stock index components via query to yahoo finance
API. Expanded existing functionality of other yahoo finance features in
`pandas.io.data.py`, in detail:
- Created new function `get_component_yahoo()`, allowing for query of stock index
components. Returns DataFrame containing list of c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2796/events | https://github.com/pandas-dev/pandas/issues/2796 | 10,606,435 | MDU6SXNzdWUxMDYwNjQzNQ== | 2,796 | ImportError: cannot import name Series | {
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I have python installs on both my mac and WIndows machine. cant be sure it i have used pandas from command line python in both as most of my stuff has been running in ipython so far. when i do import pandas or any other option (from pandas import *, form pandas import DataFrame etc. I get the following err... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2797/events | https://github.com/pandas-dev/pandas/issues/2797 | 10,620,846 | MDU6SXNzdWUxMDYyMDg0Ng== | 2,797 | BUG: `concat` throwing TypeError when `ignore_index` is True | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2013-02-04T19:06:25Z | 2013-03-28T05:51:14Z | 2013-03-28T05:51:14Z | CONTRIBUTOR | null | ``` python
In [24]: dates = pd.date_range('01-Jan-2013', '01-Jan-2014', freq='MS')[0:-1]
...: s1 = pd.Series(randn(len(dates)), index=dates, name='value')
...: s2 = pd.Series(randn(len(dates)), index=dates, name='value')
In [31]: df = pd.concat([s1, s2], axis=1, ignore_index=True)
Traceback (most recent ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2798/events | https://github.com/pandas-dev/pandas/issues/2798 | 10,664,387 | MDU6SXNzdWUxMDY2NDM4Nw== | 2,798 | BUG: Pandas treats NaN and None differently when multiplying booleans | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-02-05T19:47:00Z | 2013-03-28T12:15:05Z | 2013-03-28T12:15:05Z | NONE | null | Using pandas 0.10.
Pandas throws error when multiplying a series with booleans and a series with None. However, it works fine when you reverse the order. Also, it works fine if you use NaN instead of None. But isn't pandas supposed to treat NaN and None interchangably?
import pandas
import numpy as np
nans = panda... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2799/events | https://github.com/pandas-dev/pandas/pull/2799 | 10,666,053 | MDExOlB1bGxSZXF1ZXN0NDAwMTM0OQ== | 2,799 | Change wording in `corr` doc string | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-02-05T20:28:41Z | 2013-02-08T11:20:30Z | 2013-02-08T11:20:30Z | CONTRIBUTOR | null | Not sure if this is the best option, another one might be:
"Compute correlation between two Series"
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https://api.github.com/repos/pandas-dev/pandas/issues/2800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2800/events | https://github.com/pandas-dev/pandas/issues/2800 | 10,668,017 | MDU6SXNzdWUxMDY2ODAxNw== | 2,800 | Series with dtype='M8[us]' does not create NaT values | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2013-02-05T21:17:38Z | 2013-09-13T00:05:49Z | 2013-09-13T00:05:49Z | NONE | null | Using Pandas 0.10.1, python 2.7, and Numpy 1.6.2:
```
>>> npa = numpy.ndarray(1, dtype=numpy.int64)
>>> npa[0] = -9223372036854775808
>>> type(pandas.Series(npa, dtype='M8[ns]')[0])
<class 'pandas.tslib.NaTType'>
>>> type(pandas.Series(npa, dtype='M8[us]')[0])
<class 'pandas.tslib.Timestamp'>
```
The latter should al... | {
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