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https://api.github.com/repos/pandas-dev/pandas/issues/6501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6501/events | https://github.com/pandas-dev/pandas/issues/6501 | 28,467,007 | MDU6SXNzdWUyODQ2NzAwNw== | 6,501 | get_loc() returns integer or slice or KeyError nondeterministic in multiindex data frame | {
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http://stackoverflow.com/questions/22067205/when-does-pandas-xs-drop-dimensions-and-how-can-i-force-it-to-not-to
``` python
n=23
df = pd.DataFram... | {
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"f... | 3 | 2014-02-28T07:12:45Z | 2021-04-11T04:12:45Z | null | CONTRIBUTOR | null | http://stackoverflow.com/a/22088497/1240268
Maybe date would be useful too?
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"gists_url": "https://... | [] | closed | false | null | [] | null | 1 | 2014-02-28T12:54:34Z | 2014-03-13T01:22:21Z | 2014-03-13T01:22:21Z | MEMBER | null | Occasionally I find myself and others wanting to select the same or or and conditions in a query. http://stackoverflow.com/questions/22093006/python-pandas-filter-dataframe-by-applying-regular-expression for example.
Was just wondering if it might be useful to have a more convenient way to apply the same query to mul... | {
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"labels_url": "htt... | 1 | 2014-02-28T13:44:27Z | 2014-02-28T16:06:28Z | 2014-02-28T16:06:28Z | MEMBER | null | The last line of the following code snippet does not work, while it seems it should. Am I overlooking something?
```
df = pd.DataFrame(np.random.randn(9,2), index=[1,1,1,2,2,2,3,3,3], columns=['a', 'b'])
df
df.loc[[1, 2]] # works
df.loc[:,['a', 'b']] # works
df.loc[[1, 2], ['a', 'b']] # does not work
```
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The output contains random values or fails with an `UnicodeDecodeError`.
After commenting out the fast version (`astype`) in `_unconvert_string_array` the issue did not show up anymore.
pyton 3.3, numpy 1.8.0, pytables 3.1.0
I'll try to ge... | {
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"f... | 4 | 2014-02-28T16:10:03Z | 2020-09-22T01:48:06Z | null | CONTRIBUTOR | null | related #6501
```
In [1]: n=23
In [2]: df = pd.DataFrame({'a':np.append(np.random.randint(0,10,n), -1),
...: 'b':np.append(np.random.randint(0,10,n), -1),
...: 'c':np.append(np.random.randint(0,10,n), -1),
...: 'value':np.random.randint(0,100,n+1)})
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```
df.query('[col with space] < col')
```
I came across many external data files which have spaces in the column names. It would be nice to be able to do quick analysis on the data without first renaming the columns.
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``` python
s1 = pd.Series([0],dtype=np.float32)
s2 = pd.Series([2**27 + 1],dtype=np.int32)
df = pd.DataFrame({'float':s1, 'int':s2})
for row in df.iterrows():
print row[1][1] - (2**27 + 1)
```
(Th... | {
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"labels_url": "htt... | 3 | 2014-02-28T18:23:18Z | 2014-07-16T08:56:38Z | 2014-03-11T11:53:19Z | CONTRIBUTOR | null | It's not necessary and causes some DB engines to choke (e.g., Impala)
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https://api.github.com/repos/pandas-dev/pandas/issues/6511 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6511/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6511/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6511/events | https://github.com/pandas-dev/pandas/issues/6511 | 28,527,819 | MDU6SXNzdWUyODUyNzgxOQ== | 6,511 | pandas.DataFrame.duplicated to allow take_all | {
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```
datecol,valuecol
...
2014-01-01,12
2014-01-01,13
2014-01-02,10
...
```
In this use case, it would be good i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6512/events | https://github.com/pandas-dev/pandas/issues/6512 | 28,538,217 | MDU6SXNzdWUyODUzODIxNw== | 6,512 | groupby filter can't access all columns | {
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"labels_url": "htt... | 5 | 2014-02-28T22:25:05Z | 2014-03-11T00:54:30Z | 2014-03-11T00:54:21Z | CONTRIBUTOR | null | Unexpectedly? don't have access to the grouped columns.
Also seems to allow returning a boolean Series (and takes the first item as the condition).
```
In [11]: df = pd.DataFrame([[1, 2], [1, 3], [5, 6]], columns=['A', 'B'])
In [12]: g = df.groupby('A') # same with as_index=False (which *correctly* has no effect)
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https://api.github.com/repos/pandas-dev/pandas/issues/6513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6513/events | https://github.com/pandas-dev/pandas/issues/6513 | 28,547,699 | MDU6SXNzdWUyODU0NzY5OQ== | 6,513 | read_csv can't read right-aligned columns delimited by spaces | {
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I often work with tables that are right aligned and space delimited. This means that the spaces in front of the first column varies from 0 to N. In this screenshot, this transition happens fro... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6514/events | https://github.com/pandas-dev/pandas/pull/6514 | 28,549,094 | MDExOlB1bGxSZXF1ZXN0MTMwNzkzODM= | 6,514 | ENH: add method='dense' to rank | {
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(Let's see how it goes on Travis; last time there was some strange behaviour which I think turned out to be because of an uninitialized variable which was behaving differently on my system than on the buildbots.)
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https://api.github.com/repos/pandas-dev/pandas/issues/6515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6515/events | https://github.com/pandas-dev/pandas/issues/6515 | 28,559,614 | MDU6SXNzdWUyODU1OTYxNA== | 6,515 | ENH: allow dict to name columns for multiple-aggregations in groupby | {
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... | 3 | 2014-03-01T15:53:11Z | 2017-01-23T14:10:04Z | 2017-01-23T14:10:03Z | CONTRIBUTOR | null | from SO:
http://stackoverflow.com/questions/22115671/apply-different-resampling-method-to-the-same-column-pandas
related #7700 (make sure to have name support)
Might be nice to support a dict for how on resample/groupby.agg so that the resulting
columns of an aggregation are named as the keys
`df.resample('A',how={... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6516 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6516/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6516/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6516/events | https://github.com/pandas-dev/pandas/pull/6516 | 28,567,591 | MDExOlB1bGxSZXF1ZXN0MTMwODcwNzE= | 6,516 | BUG/API: allow TimeGrouper with other columns in a groupby (GH3794) | {
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```
In [3]: df
Out[3]:
Branch Buyer Date Quantity
0 A Carl 2013-01-01 13:00:00 1
1 A Mark 2013-01-01 13:05:00 3
2 A Carl 2013-10-01 20:00:00 5
3 A Carl 2013-10-02 10:00:00 1
4 A Joe 2013-10-01 20:00:00 8
5 A J... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6517 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6517/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6517/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6517/events | https://github.com/pandas-dev/pandas/issues/6517 | 28,571,943 | MDU6SXNzdWUyODU3MTk0Mw== | 6,517 | panel count function will raise TypeError when the value of dataframe is None | {
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import numpy as np
df1=pd.DataFrame(index=range(8),columns=['a','b','c']) #a empty dataframe
df1.ix[:]=None #assign NULL
panel1=pd.Panel(dict(df1=df1))
df1.count() #good
panel1.count() #raise exception now
the exception is rasied,it says,TypeError: ufunc 'isfinite' not supported for the input ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6518 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6518/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6518/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6518/events | https://github.com/pandas-dev/pandas/issues/6518 | 28,574,596 | MDU6SXNzdWUyODU3NDU5Ng== | 6,518 | applymap returns empty list even though function is boolean | {
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```
In [11]: df = pd.DataFrame({'feat 1': {0: [], 1: 6, 2: None}, 'feat 2': {0: [], 1: 8, 2: 10}, 'feat 3': {0: 5.0, 1: 3.0, 2: np.nan}})
In [14]: df.applymap(lambda x: x == [] or x is None)
Out[14]:
feat 1 feat 2 feat 3
0 True True... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6519/events | https://github.com/pandas-dev/pandas/pull/6519 | 28,578,521 | MDExOlB1bGxSZXF1ZXN0MTMwOTE2ODE= | 6,519 | Allow the use of buffers and None in to_hdf and read_hdf. | {
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"labels_url": "htt... | 9 | 2014-03-02T12:22:23Z | 2014-07-02T14:14:18Z | 2014-04-05T23:57:22Z | CONTRIBUTOR | null | This patch basically enables the expression
```
pd.read_hdf(BytesIO(df.to_hdf(key="/data")), key="/data") == df
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6520 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6520/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6520/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6520/events | https://github.com/pandas-dev/pandas/issues/6520 | 28,585,767 | MDU6SXNzdWUyODU4NTc2Nw== | 6,520 | Issues with FRED Tests | {
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```
FAIL: test_fred_multi (pandas.io.tests.test_data.TestFred)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.7_with_system_site_packages/l... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6521/events | https://github.com/pandas-dev/pandas/pull/6521 | 28,587,610 | MDExOlB1bGxSZXF1ZXN0MTMwOTUzNTM= | 6,521 | DOC: Add common error message to byte-ordering gotcha. | {
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"gists... | [] | closed | false | null | [] | null | 1 | 2014-03-02T20:32:18Z | 2014-06-26T12:43:08Z | 2014-03-02T20:53:43Z | CONTRIBUTOR | null | A friend of mine got tripped up by [this gotcha](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#byte-ordering-issues). We document it well, but Googling the error message doesn't turn up our documentation, so it took him a lot of digging to get there.
This commit adds an example error message that byte-order... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6522/events | https://github.com/pandas-dev/pandas/issues/6522 | 28,587,949 | MDU6SXNzdWUyODU4Nzk0OQ== | 6,522 | read_csv should promote int values to float64 if column contains NaNs | {
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"f... | 3 | 2014-03-02T20:47:55Z | 2016-08-22T13:13:34Z | 2016-08-22T13:13:34Z | CONTRIBUTOR | null | Versions: WinXP, pd 13.1, np-1.7.1, numexpr-2.2.2
Consider the following code snippit:
from io import StringIO
import pandas as pd
import numpy as np
data = 'a;b;c\n1;2;3\n4;NaN;6\n7;8;9'
df = pd.read_csv(StringIO(data),
sep = ';', dtype = np.float64)
print(df.b)
df2 = pd.read_csv(StringIO(data),
sep = ';', dt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6523/events | https://github.com/pandas-dev/pandas/issues/6523 | 28,595,890 | MDU6SXNzdWUyODU5NTg5MA== | 6,523 | dataframe select by datetimeitem | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6524 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6524/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6524/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6524/events | https://github.com/pandas-dev/pandas/issues/6524 | 28,601,637 | MDU6SXNzdWUyODYwMTYzNw== | 6,524 | Grouby select multiple columns | {
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```
g[['X', 'Y']] # do groupby stuff with just these columns
```
http://stackoverflow.com/q/22139053/1240268
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https://api.github.com/repos/pandas-dev/pandas/issues/6525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6525/events | https://github.com/pandas-dev/pandas/pull/6525 | 28,618,754 | MDExOlB1bGxSZXF1ZXN0MTMxMTE1Njc= | 6,525 | BUG: fix DataFarame column slice indexer | {
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``` python
In [1]: df = pd.DataFrame(np.random.rand(5,3))
In [2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6526/events | https://github.com/pandas-dev/pandas/issues/6526 | 28,621,725 | MDU6SXNzdWUyODYyMTcyNQ== | 6,526 | DataFrame with dtype=object converts Numpy Arrays to Series | {
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in: `mytable = pd.DataFrame(data={'data':[np.array([1,2,3]... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6527/events | https://github.com/pandas-dev/pandas/issues/6527 | 28,624,045 | MDU6SXNzdWUyODYyNDA0NQ== | 6,527 | Inplace operation on PeriodIndex cast it to Int64Index | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-03-03T14:13:14Z | 2014-07-23T21:28:50Z | 2014-07-23T21:28:50Z | CONTRIBUTOR | null | ``` python
In [1]: dates = pd.period_range('01-Jan-2015', '01-Jan-2016', freq='M')
...: series = pd.TimeSeries(np.arange(dates.size), dates)
...:
In [2]: series
Out[2]:
2015-01 0
2015-02 1
2015-03 2
2015-04 3
2015-05 4
2015-06 5
2015-07 6
2015-08 7
2015-09 8
2015-10 9
20... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6528 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6528/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6528/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6528/events | https://github.com/pandas-dev/pandas/issues/6528 | 28,628,994 | MDU6SXNzdWUyODYyODk5NA== | 6,528 | NaN values impact binary or operations asymmetrically | {
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"closed_at": "2020-01-30T12:18:05Z",
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"followers_url": "https://api.github.com/users/wesm... | 10 | 2014-03-03T15:21:38Z | 2019-12-02T12:48:15Z | 2019-12-02T12:48:07Z | CONTRIBUTOR | null | I am not sure if this is on purpose, but as far as I know, `or` operation should be symmetric ( I mean `a | b == b | a` ); not in pandas:
```
>>> a = pd.Series( [True] )
>>> b = pd.Series( [np.nan] )
>>> a | b
0 True
dtype: bool
>>> b | a
0 False
dtype: bool
```
is there any reason pandas is treating `NaN` valu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6529/events | https://github.com/pandas-dev/pandas/issues/6529 | 28,629,199 | MDU6SXNzdWUyODYyOTE5OQ== | 6,529 | np.datetime64 values cast to datetime.date iff dtype == M8 | {
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"labels_url": "htt... | 6 | 2014-03-03T15:24:25Z | 2014-03-03T19:13:31Z | 2014-03-03T19:13:31Z | CONTRIBUTOR | null | This seems a little inconsistent to me. IMHO, it would be good to be able to rely on the the fact that if you specify a numpy array of _any_ datetime64 type the result will still be a datetime64 type and not cast to a different type dependant on the exact dtype passed in.
``` python
In [1]: dates = pd.date_range('01-J... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6530/events | https://github.com/pandas-dev/pandas/pull/6530 | 28,636,442 | MDExOlB1bGxSZXF1ZXN0MTMxMjE4MDE= | 6,530 | BUG: Regression from 0.13 in the treatment of numpy datetime64 non-ns dtypes in Series creation (GH6429) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6531/events | https://github.com/pandas-dev/pandas/pull/6531 | 28,637,400 | MDExOlB1bGxSZXF1ZXN0MTMxMjIzNzk= | 6,531 | BUG/TST: fix several issues with slice bound checking code | {
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BUG/TST: `obj.iloc[:,-4:-10]` should return empty slice (because slice.step > 0 and slice.start > slice.stop)
There's no way to describe this by setting stop to a value in `[0; len]` interval, it should remain nega... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6532/events | https://github.com/pandas-dev/pandas/issues/6532 | 28,647,328 | MDU6SXNzdWUyODY0NzMyOA== | 6,532 | index and items switched in series error message | {
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```
pd.Series([1, 2], index=[1, 2, 3, 4, 5])
```
Raises this value error:
```
ValueError: Wrong number of items passed 5, indices imply 2
```
I expected to see this:
```
ValueError: Wrong number of items passed 2, index implies 5
```
Comparing my traceback with the current code it looks like an... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6533/events | https://github.com/pandas-dev/pandas/pull/6533 | 28,668,446 | MDExOlB1bGxSZXF1ZXN0MTMxNDAyOTg= | 6,533 | API change in groupby head and tail | {
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"labels_url": "htt... | 15 | 2014-03-03T23:54:07Z | 2014-06-27T15:35:00Z | 2014-03-06T00:21:26Z | CONTRIBUTOR | null | BUG/API groupby head and tail act like filter, since they dont aggregage fixes column selection
Breaking API change to groupby head and tail, these were never aggregated output, so should not have the group labels as the index, it should always filter. (It was a legacy from when this was `g.apply(lambda x: x.head(n))`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6534/events | https://github.com/pandas-dev/pandas/issues/6534 | 28,677,016 | MDU6SXNzdWUyODY3NzAxNg== | 6,534 | deprecation warning | {
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```
/home/apratap/softwares/anaconda/lib/python2.7/site-packages/pandas/computation/ops.py:62: DeprecationWarning: object() takes no parameters
return supr_new(klass, name, env, side=side, encoding=encoding)
/home/apratap/softwares/anaconda/lib/python... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6535 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6535/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6535/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6535/events | https://github.com/pandas-dev/pandas/pull/6535 | 28,678,314 | MDExOlB1bGxSZXF1ZXN0MTMxNDYyMjY= | 6,535 | BUG: informative error message with Series ValueError | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6536/events | https://github.com/pandas-dev/pandas/issues/6536 | 28,705,875 | MDU6SXNzdWUyODcwNTg3NQ== | 6,536 | iloc with empty list raises IndexError | {
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"labels_url": "htt... | 2 | 2014-03-04T13:46:17Z | 2014-03-06T23:01:26Z | 2014-03-06T23:01:26Z | CONTRIBUTOR | null | When you slice a dataframe with an empty list you get an IndexError instead of an empty dataframe. Slicing with a non-empty list works fine. Tested in pandas 0.13.1. This is a regression from pandas 0.11.0.
``` python
df = pd.DataFrame(np.arange(25.0).reshape((5,5)), columns=list('abcde'))
```
Slice with non-empty ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6537 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6537/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6537/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6537/events | https://github.com/pandas-dev/pandas/issues/6537 | 28,715,901 | MDU6SXNzdWUyODcxNTkwMQ== | 6,537 | Can't select from MultiIndex using .xs() | {
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df_param.index.levels[3]
>>> Index([u'a', u't', u't_anti', u'v_anti', u'v_pro', u'v_stop'], dtype='object')
df_param.xs(u'a', level=3)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-103-99e... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6538 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6538/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6538/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6538/events | https://github.com/pandas-dev/pandas/issues/6538 | 28,717,874 | MDU6SXNzdWUyODcxNzg3NA== | 6,538 | weekofyear gives different results on a DatetimeIndex (if tz is given) | {
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... | closed | false | null | [] | {
"closed_at": "2017-05-06T10:20:19Z",
"closed_issues": 987,
"created_at": "2016-02-08T15:30:21Z",
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-03-04T16:12:10Z | 2017-05-01T11:39:28Z | 2017-05-01T11:34:20Z | CONTRIBUTOR | null | Hello,
The following code
```
dates = ["2013/12/29", "2013/12/30", "2013/12/31"]
dates = pandas.DatetimeIndex(dates, tz="Europe/Brussels")
print dates.weekofyear
print [d.weekofyear for d in dates]
```
gives
```
[52 1 1]
[52, 52, 1]
```
i.e. calling weekofyear on the datetimeindex directly or iterating on the di... | {
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``` python
s = pd.Series(np.arange(5.)[::-1])
np.sort(s)
Out[68]: array([ 0., 1., 2., 3., 4.])
```
This doesn't happen for all numpy functions e.g.
``` python
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2014-03-04T17:49:10Z | 2020-07-28T21:09:13Z | 2020-07-28T21:09:13Z | CONTRIBUTOR | null | The fall through value_counts (for Series) is a bit strange, I think better result would be (with the standard options):
Can put together if people think it's good.
```
In [132]: df = pd.DataFrame([['a_link', 'dofollow'], ['a_link', 'dofollow'], ['a_link', 'nofollow'], ['b_link', 'javascript']], columns=['link', 'typ... | {
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```
data = {'me':list('rttti'),'foo': list('aaade'), 'bar': arange(5)*1.34+2, 'bar2': arange(5)*-.34+2}
df = pd.DataFrame(data).set_index('me')
df.loc['r',['bar','bar2']]*=2.0
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``` python
In [1]: timestamp = pd.Timestamp(datetime.datetime(2014, 3, 4))
In [2]: timestamp + datetime.timedelta(seconds=1)
Out[2]: Timestamp('2014-03-04 00:00:01', tz=None)
In [3]: timestamp - datetime.timedelta(seconds=1)
O... | {
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This is my first PR so I may have done a few things wrong. If I should have added my tests to a different file/location, please let me know.
I should point out that this code is also tested by many other sections of the pandas codebase, on my first pass I hadn't captured the "Timestamp - datetime" case a... | {
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Feature Request: I would like to be able to write code that produces, byte-for-byte, reproducible outputs. To that end I want to write Stata dta files with a blank (or constant) timestamp. It would be nice to allow write_file() to accept a timestamp (or some option... | {
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``` python
>>> import pandas as pd
>>> df = pd.DataFrame({1:[1,1],2:[2,2],'a':['a','a']})
>>> df
1 2 a
0 1 2 a
1 1 2 a
[2 rows x 3 columns]
>>> df[1]
0 1
1 1
Name: 1, dtype: int64
>>> df[['a',1]]
a 1
0 a 1
1 a 1
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```
df sum_sq mean_sq F PR(>F)
C(Fitness) 2 672 336.000000 16.961538 0.000041
Residual 21 416 19.809524 NaN NaN
[2 rows x 5 columns]
```
Where'd that bottom `[]` business c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6549 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6549/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6549/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6549/events | https://github.com/pandas-dev/pandas/issues/6549 | 28,756,119 | MDU6SXNzdWUyODc1NjExOQ== | 6,549 | Overflow adding pd.timedelta to pd.Timestamp | {
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... | 3 | 2014-03-05T00:52:15Z | 2015-08-29T01:39:57Z | 2015-08-29T01:39:57Z | NONE | null | xref #9442
# Assume following code
``` python
import pandas as pd
additions = [1e9, 1e10]
for ns in additions:
added = pd.Timestamp(0) + pd.to_timedelta(str(ns), unit='ns')
print added
```
This will work fine on Linux 64 bits. However, it won't work on Windows (both 32 and 64 bits).
As for now, the followin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6550 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6550/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6550/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6550/events | https://github.com/pandas-dev/pandas/issues/6550 | 28,775,690 | MDU6SXNzdWUyODc3NTY5MA== | 6,550 | DataFrame.rolling.mean resets windows with NaN | {
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"f... | 12 | 2014-03-05T09:27:47Z | 2020-05-08T19:21:16Z | null | NONE | null | hello all,
I posted a question on SO a few days ago regarding unexpected behaviour with rolling_mean().
http://stackoverflow.com/questions/22146105/compute-a-sequential-rolling-mean-in-pandas-as-array-function
Looking at the sourse code this could have implications to a host functions that call rolling_mea... | {
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"labels_url": "htt... | 6 | 2014-03-05T12:03:51Z | 2014-10-15T13:40:48Z | 2014-03-06T23:01:26Z | CONTRIBUTOR | null | This should fix #6536.
There's a side issue though with loc/ix indexers that somehow discard column names for empty indexers.
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https://api.github.com/repos/pandas-dev/pandas/issues/6552 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6552/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6552/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6552/events | https://github.com/pandas-dev/pandas/issues/6552 | 28,787,541 | MDU6SXNzdWUyODc4NzU0MQ== | 6,552 | BUG: loc/ix indexers with empty list key lose names of respective axes | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2014-03-05T12:59:50Z | 2014-10-04T20:02:08Z | 2014-10-04T20:02:08Z | CONTRIBUTOR | null | This issue was encountered when fixing #6536. It seems that `.loc` indexer with empty list key seems to lose names of respective axes:
``` python
In [2]: pd.DataFrame(np.arange(9).reshape(3,3), columns=pd.Series(list('abc'), name='foobar'))
Out[2]:
foobar a b c
0 0 1 2
1 3 4 5
2 6 7 8
[3 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6553 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6553/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6553/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6553/events | https://github.com/pandas-dev/pandas/pull/6553 | 28,790,187 | MDExOlB1bGxSZXF1ZXN0MTMyMTA4NDg= | 6,553 | ENH: Allow timestamp and data label to be set when exporting to Stata | {
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"labels_url": "htt... | 9 | 2014-03-05T13:46:45Z | 2014-07-16T08:57:07Z | 2014-03-07T00:01:16Z | CONTRIBUTOR | null | closes #6545
Added code which allows the time stamp and the data label to be set using
either StataWriter or to_stata. Also simplified reading these values using
StataReader by removing null bytes from the string values read.
Added basic test for both.
Also fixed one small bug where variables could be stored using ... | {
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"labels_url": "htt... | 1 | 2014-03-05T14:41:19Z | 2014-04-06T17:45:52Z | 2014-04-06T17:45:52Z | CONTRIBUTOR | null | should be mentioned that it starts in 0.13.0
http://pandas.pydata.org/pandas-docs/stable/io.html#table-format
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"labels_url": "htt... | 1 | 2014-03-05T18:41:50Z | 2014-03-06T23:35:30Z | 2014-03-06T23:35:30Z | CONTRIBUTOR | null | Would love it if Series.quantile() converted the dtype to float (as DataFrame.quantile() appears to do). As it is, I get the following error with dtype=object:
```
In [8]: low
Out[8]:
...
Name: p, Length: 2218, dtype: object
In [9]: low.quantile(0.33)
-----------------------------------------------------------------... | {
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... | 5 | 2014-03-05T21:17:26Z | 2018-07-06T21:32:28Z | 2018-07-06T21:32:28Z | CONTRIBUTOR | null | @dsm054 posted ridiculous fast version for this, perhaps we can wrap it in an API with filter?
http://stackoverflow.com/questions/22208562/replace-rarely-occurring-values-in-a-pandas-dataframe#comment33717682_22208838
cc @danielballan
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https://api.github.com/repos/pandas-dev/pandas/issues/6557 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6557/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6557/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6557/events | https://github.com/pandas-dev/pandas/issues/6557 | 28,829,546 | MDU6SXNzdWUyODgyOTU0Ng== | 6,557 | Performance regression in combine_first() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6558 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6558/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6558/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6558/events | https://github.com/pandas-dev/pandas/pull/6558 | 28,832,763 | MDExOlB1bGxSZXF1ZXN0MTMyMzY2MDQ= | 6,558 | BUG: Series.quantile raising on an object dtype (GH6555) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6559 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6559/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6559/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6559/events | https://github.com/pandas-dev/pandas/issues/6559 | 28,834,252 | MDU6SXNzdWUyODgzNDI1Mg== | 6,559 | pandas groupby apply | {
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when apply functions to a groupby-ed dataframe, the html output is:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>deal_name</th>
<th>curr_bal</th>
<th>orig_bal</th>
</tr>
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6560 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6560/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6560/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6560/events | https://github.com/pandas-dev/pandas/pull/6560 | 28,839,788 | MDExOlB1bGxSZXF1ZXN0MTMyNDA4NTk= | 6,560 | BUG: preserve frequency across Timestamp addition/subtraction (#4547) | {
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My second PR, on basically the same code.
Assuming people agree with my comments on #4547 (that users adding timedeltas that don't match frequencies better know what they are doing), then this would be my submission.
I'm also assuming that something is up with Travis and it is not anything I have done -... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6561 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6561/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6561/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6561/events | https://github.com/pandas-dev/pandas/issues/6561 | 28,842,765 | MDU6SXNzdWUyODg0Mjc2NQ== | 6,561 | problem writing DateTime data to sqlite with sql.write_frame | {
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"labels_url": "htt... | 9 | 2014-03-06T01:26:16Z | 2014-04-25T14:34:48Z | 2014-04-25T14:34:48Z | NONE | null | Although this problem was [ostensibly solved](https://github.com/pydata/pandas/issues/6015), it still seems to affect the [latest](https://github.com/pydata/pandas/commit/1bab0a2d806dccf1b5bf6bf501d38b5f1a9b3bc7) commit as of the present time; the following code throws an `InterfaceError` at the `write_frame()` call; c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6562/events | https://github.com/pandas-dev/pandas/issues/6562 | 28,852,264 | MDU6SXNzdWUyODg1MjI2NA== | 6,562 | to_datetime loses freq on DatetimeIndex with offset | {
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... | 7 | 2014-03-06T06:04:33Z | 2020-03-31T04:22:07Z | null | CONTRIBUTOR | null | It would be cool if the offset were kept. Perhaps even an error should be raised if not all of the `Timestamp`s have the same `offset`:
```
In [36]:
dti = pd.to_datetime([pd.Timestamp("2014-1-1", offset="M"),])
dti
Out[36]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-01-01]
Length: 1, Freq: None, Timezone: Non... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6563 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6563/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6563/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6563/events | https://github.com/pandas-dev/pandas/issues/6563 | 28,866,149 | MDU6SXNzdWUyODg2NjE0OQ== | 6,563 | UTF-8 and read_csv: addressing data frame columns gives KeyError | {
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```
df = pd.read_csv(file.csv)
```
gives problems when specific data frame columns are addressed.
Reading the csv itself doesn't give any problem or warning. And it is possible to view the data frame ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6564 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6564/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6564/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6564/events | https://github.com/pandas-dev/pandas/issues/6564 | 28,897,845 | MDU6SXNzdWUyODg5Nzg0NQ== | 6,564 | nosetests pandas error | {
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## FAIL: test_fred_multi (pandas.io.tests.test_data.TestFred)
Traceback (most recent call last):
File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/util/testing.py", line 1135, in wrapper
return t(_args, *_kwargs)
File "/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6565 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6565/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6565/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6565/events | https://github.com/pandas-dev/pandas/issues/6565 | 28,900,085 | MDU6SXNzdWUyODkwMDA4NQ== | 6,565 | Unstacked data in unexpected order | {
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... | closed | false | null | [] | null | 1 | 2014-03-06T17:56:16Z | 2014-03-06T23:15:39Z | 2014-03-06T23:15:39Z | NONE | null | After unstacking data that were grouped using groupby the data appears in an unexpected order. The groupby data are in the ascending order, but after unstacking this is no longer true.
The following code demonstrates:
import numpy as np
import pandas as pd
print pd.**version**
# random data
data = pd.DataFrame()
dat... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6566 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6566/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6566/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6566/events | https://github.com/pandas-dev/pandas/issues/6566 | 28,911,940 | MDU6SXNzdWUyODkxMTk0MA== | 6,566 | TST: TravisCI failures | {
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"labels_url": "htt... | 6 | 2014-03-06T20:31:07Z | 2014-03-15T16:07:01Z | 2014-03-06T22:41:35Z | CONTRIBUTOR | null | It looks like Travis is failing to install the dependencies recently.
In `ci/install.sh`, we run
```
pip install $PIP_ARGS -r ci/requirements-${wheel_box}.txt
```
where `PIP_ARGS` (I think) is something like `-I --use-wheel --find-links=http://cache27diy-cpycloud.rhcloud.com/2.7/ --allow-external --allow-insecure
` ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6567 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6567/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6567/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6567/events | https://github.com/pandas-dev/pandas/pull/6567 | 28,919,028 | MDExOlB1bGxSZXF1ZXN0MTMyODQ4OTU= | 6,567 | BLD: change wheel url to pandas.pydata.org | {
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closes #6566
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https://api.github.com/repos/pandas-dev/pandas/issues/6568 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6568/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6568/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6568/events | https://github.com/pandas-dev/pandas/issues/6568 | 28,947,606 | MDU6SXNzdWUyODk0NzYwNg== | 6,568 | Option to set large_repr to info(verbose=False) missing | {
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"labels_url": "htt... | 13 | 2014-03-07T09:16:44Z | 2014-05-14T22:25:41Z | 2014-05-14T22:25:41Z | CONTRIBUTOR | null | In v0.13.1 the default representation for pd.DataFrame changed. I would like to be able to restore the previous default (see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#dataframe-repr-changes)
The option display.large_repr only allows to set info(verbose=True) as default. The default in previous version ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6569 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6569/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6569/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6569/events | https://github.com/pandas-dev/pandas/pull/6569 | 28,984,684 | MDExOlB1bGxSZXF1ZXN0MTMzMjA1NjI= | 6,569 | ENH/BUG groupby nth now filters, works with DataFrames | {
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partial for #5264
```
In [101]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
In [102]: g = df.groupby('A')
In [103]: g.nth(0)
Out[103]:
A B
0 1 NaN
2 5 6
In [104]: g.nth(1)
Out[104]:
A B
1 1 4
In [105]: g.nth(-1)
Out[105]:
A B
1 1 4
2 5 6
In [106]: g.nth... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6570/events | https://github.com/pandas-dev/pandas/pull/6570 | 28,987,607 | MDExOlB1bGxSZXF1ZXN0MTMzMjIzNTQ= | 6,570 | FIX use selected_obj rather the obj throughout groupby | {
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"labels_url": "htt... | 15 | 2014-03-07T18:33:48Z | 2014-06-30T09:34:37Z | 2014-03-09T19:09:40Z | CONTRIBUTOR | null | fixes many parts of for #5264... Changes lots of `self.obj` to `self._selected_obj`.
probably should vbench before merging?
cc @jreback @TomAugspurger
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https://api.github.com/repos/pandas-dev/pandas/issues/6571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6571/events | https://github.com/pandas-dev/pandas/issues/6571 | 28,987,918 | MDU6SXNzdWUyODk4NzkxOA== | 6,571 | Unable to access multidimensional ndarray in a Series (regression) | {
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import numpy as np
import pandas as pd
arr = np.array([[8,8],[9,9]])
s = pd.Series([3, arr])
s[1]
```
Results in
```
Exception Traceback (most recent call last)
<ipython-input-34-88de191fe097> in <module>()
----> 1 s[1]
/usr/lib/python2.7/dist-packages/pandas/core/series.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6572 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6572/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6572/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6572/events | https://github.com/pandas-dev/pandas/issues/6572 | 28,990,282 | MDU6SXNzdWUyODk5MDI4Mg== | 6,572 | ValueError: Cannot compare tz-naive and tz-aware timestamps | {
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... | closed | false | null | [] | {
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```
>>> import pandas
>>> pandas.date_range('2013-01-01T00:00:00+05:30','2014-03-07T23:59:59+05:30',freq='AS')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages... | {
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"labels_url": "htt... | 5 | 2014-03-07T20:14:08Z | 2014-03-12T13:12:25Z | 2014-03-12T13:12:25Z | CONTRIBUTOR | null | Excel workbooks have 'Sheet1' as the default sheet. Can `pd.read_excel` have this as a default? `pandas.io.excel.read_excel(io, sheetname='Sheet1', **kwds)`
This makes pandas more accessible to the user transitioning from Excel by avoiding the common error below:
```
In [9]: test = pd.read_excel('/Users/clark/Desktop... | {
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"labels_url": "htt... | 2 | 2014-03-07T20:29:09Z | 2014-03-09T15:15:14Z | 2014-03-09T15:15:00Z | NONE | null | Illustration:
``` python
acc = [
('a','abcde',1),
('b','bbcde',2),
('y','yzcde',25),
('z','xbcde',24),
('z',None,26),
('z','zbcde',25),
('z','ybcde',26),
]
df1 = pd.DataFrame(acc, columns=['a1','a2','cnt']).set_index(['a1','a2'])
```
```
In [476]: df1
Out[476]:
cnt
a1 a2 ... | {
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Hopefully the `test_repr()` implementation I included was sufficient. I see some `test_repr()` instances in the code that are more minimal - literally just making sure that repr(obj) doesn't blow up. And some which are pretty detailed, which makes me worry they might be a bit fragile. I went more middle-o... | {
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Implements a default sheetname of 0 for `read_excel` #6573.
Before: `read_excel(io, sheetname, **kwds)`
After: `read_excel(io, sheetname=0, **kwds)`
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We try to keep these for *three* major versions as actual deprecations. e.g. deprecate in 0.17, 0.18 & 0.19 get the warning, removed in 0.20.
## 0.24.0
- [x] #24596 `__array__` for tzaware Series/Index (0.24.0)
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Originally from:
http://stackoverflow.com/questions/22286930/is-it-possible-to-use-cut-on-a-collection-of-datetimes?noredirect=1#comment33862629_22286930
Would it be possible to enhance the cut function to handle datetime inputs?
For example, say I have the following setup:
```
import pandas as pd
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Traceback:
``` python
File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 929, in concat
verify_integrity=verify_integrity)
File "/usr/local/lib/python2.7/dist-p... | {
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"labels_url": "htt... | 17 | 2014-03-10T14:05:03Z | 2014-03-18T10:08:23Z | 2014-03-18T10:08:23Z | CONTRIBUTOR | null | The [custom-business-days](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#custom-business-days-experimental) are currently significantly slower (around factor 4) compared to pd.offsets.BusinessDay(). Without actually specifying any custom business days:
```
import datetime as dt
import pandas as pd
dat... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-03-10T14:47:55Z | 2014-06-17T12:08:52Z | 2014-06-17T12:08:52Z | CONTRIBUTOR | null | I came across this issue while looking at the [this](http://stackoverflow.com/questions/22298542) question on stackoverflow;
say I have a data-frame like:
```
>>> df
tag val1 val2
0 B 0 0
1 A 1 2
2 A 2 4
3 B 3 6
4 B 4 8
```
and I want to reverse columns `val1` an... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6586 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6586/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6586/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6586/events | https://github.com/pandas-dev/pandas/issues/6586 | 29,100,577 | MDU6SXNzdWUyOTEwMDU3Nw== | 6,586 | Plot issue, timeseries always honored, | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2014-03-10T14:48:06Z | 2017-11-24T10:34:26Z | 2017-11-24T10:34:22Z | NONE | null | When work with tick data, and the density of data is not time evenly distributed. When I use dataframe plot function, the default plot always honor by time, not by the number of data points. I have to convert the datetimeindex back to string, and plot correspondingly, isn't it handy to make an extra flag to plot with n... | {
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print s
0 NaT
1 NaT
2 2013-08-05 15:30:00.**000001**
dtype: datetime64[ns]
print s.fillna(method='backfill')
0 2013-08-05 15:30:00.**000001024**
1 2013-08-05 15:30:00.**000001024**
2 2013-08-05 1... | {
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or control via `verbose` keyword
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https://api.github.com/repos/pandas-dev/pandas/issues/6590 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6590/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6590/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6590/events | https://github.com/pandas-dev/pandas/issues/6590 | 29,127,901 | MDU6SXNzdWUyOTEyNzkwMQ== | 6,590 | BUG: impossible to select string with special character from DataFrame via query | {
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Source:
```
df = DataFrame({'Licença': [1, 2, 3, 4]})
df.query("Licença == 1")
```
Error:
```
Traceback (most recent call last):
File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/tornado/web.py", line 1192, in _stack_conte... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6591/events | https://github.com/pandas-dev/pandas/pull/6591 | 29,132,019 | MDExOlB1bGxSZXF1ZXN0MTMzOTMyNTU= | 6,591 | BUG: replace iterrows with itertuples in sql insert (GH6509) | {
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Should still add a test
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https://api.github.com/repos/pandas-dev/pandas/issues/6592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6592/events | https://github.com/pandas-dev/pandas/pull/6592 | 29,139,816 | MDExOlB1bGxSZXF1ZXN0MTMzOTgwOTE= | 6,592 | Improve performance for custom business days (GH6584) | {
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Tests are passing. Incrementing datetime with custom dateoffset is at about twice the speed now. This could probably be optimized more.
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TST for selected groupby add resample ohlc and filter
add couple of tests for #5264
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"closed_at": "2016-05-05T00:34:40Z",
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```
In [1]: rng = pd.date_range('2012', periods=3)
In [2]: df = pd.DataFrame([[1, 2], [1, 3], [5, 6]], rng, columns=['A', 'B'])
In [3]: g = df.groupby('A')
In [4]: g.fo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6595 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6595/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6595/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6595/events | https://github.com/pandas-dev/pandas/issues/6595 | 29,147,975 | MDU6SXNzdWUyOTE0Nzk3NQ== | 6,595 | current master compile fails | {
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commit a265c5c676039af160890eca949a0a3562277fe6
Merge: 26e8fa8 022ab40
Author: Andy Hayden <xxxx>
Date: Mon Mar 10 17:54:21 2014 -0700
```
Merge pull request #6593 from hayd/groupby_filter_filter
FIX filter selects selected columns
```
Here's the error log from a `python setup.py install`. This has wo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6597 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6597/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6597/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6597/events | https://github.com/pandas-dev/pandas/pull/6597 | 29,174,057 | MDExOlB1bGxSZXF1ZXN0MTM0MTc3NDg= | 6,597 | FIX: Bug whereby array_equivalent was not correctly comparing Float64Ind... | {
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```
>>> import pandas.core.common as com
>>> com.array_equivalent(Float64Index([0, np.nan]), Float64Index([0, np.nan]))
False
```
Although the current pandas code base does not use `array_equivalent` to compare Float64Indexes, leaving `array_equivalent` in its current state may be a bug waiting to happen.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6598 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6598/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6598/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6598/events | https://github.com/pandas-dev/pandas/pull/6598 | 29,175,394 | MDExOlB1bGxSZXF1ZXN0MTM0MTg1MDk= | 6,598 | SQL: move df.to_sql to generic + update with new functionality | {
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Questions:
- To catch Panels (and raiseNotImplementedError), should I do this in sql.to_sql or in NDFrame.to_sql?
- To support Series, I can just call `to_frame` on it. Is this OK?
And should add tests for this.
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"gists_url": "ht... | null | 57 | 2014-03-11T16:29:40Z | 2015-01-19T22:25:36Z | 2015-01-18T21:41:38Z | CONTRIBUTOR | null | closes #4818
- need to unify api for drop and filter
- should they take args for regex (regex, flags, case)
- should we keep select or filter?
closes #6189
xref #8530
xref #8594
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https://api.github.com/repos/pandas-dev/pandas/issues/6600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6600/events | https://github.com/pandas-dev/pandas/issues/6600 | 29,197,567 | MDU6SXNzdWUyOTE5NzU2Nw== | 6,600 | Series.pop() removes additional keys not specified | {
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import pandas as pd
df = pd.DataFrame({
'A': 0,
'B': range(5),
'C': 0,
})
k = df.iloc[4]
k.pop('B')
print k
```
```
out:
A 0
Name: 4, dtype: int64
```
```
expected:
A 0
C 0
Name: 4, dtype: int64
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.4.final.0
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