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https://api.github.com/repos/pandas-dev/pandas/issues/2901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2901/events | https://github.com/pandas-dev/pandas/issues/2901 | 11,180,899 | MDU6SXNzdWUxMTE4MDg5OQ== | 2,901 | Assign to Series slice of a Panel | {
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x = Series([2, 3, 4, 5], index=p.minor_axis)
p.ix[0, 0] = 4 # works
p.ix[0, 0, :] = x # works
p.ix[0, 0] = x # doesn't work
pandas.version.version
'0.10.1'
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https://api.github.com/repos/pandas-dev/pandas/issues/2902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2902/events | https://github.com/pandas-dev/pandas/issues/2902 | 11,183,346 | MDU6SXNzdWUxMTE4MzM0Ng== | 2,902 | MultiIndex on complex DataFrame.apply | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-02-20T03:22:35Z | 2013-03-28T17:44:22Z | 2013-03-28T17:44:22Z | CONTRIBUTOR | null | Seem to be converting MultiIndex into tuples when I do a complex apply:
```
import pandas as pd
s = pd.DataFrame([[1,2], [3,4], [5,6]])
s.index = pd.MultiIndex.from_arrays([['a','a','b'], ['c','d','d']])
s.columns = ['col1','col2']
s
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2903 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2903/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2903/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2903/events | https://github.com/pandas-dev/pandas/issues/2903 | 11,187,594 | MDU6SXNzdWUxMTE4NzU5NA== | 2,903 | MultiIndex xs failure on axis=1 | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-02-20T07:42:05Z | 2013-04-17T00:13:10Z | 2013-03-22T17:37:35Z | CONTRIBUTOR | null | ``` python
In [40]: columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), ('b', 'hello'), ('b', 'world')], names=['lvl0', 'lvl1'])
In [41]: df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
In [42]: df
Out[42]:
lvl0 a b
lvl1 foo bar hello world
0 2.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2904 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2904/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2904/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2904/events | https://github.com/pandas-dev/pandas/issues/2904 | 11,191,799 | MDU6SXNzdWUxMTE5MTc5OQ== | 2,904 | df.ix[] inconsistency between axis for MultiIndex | {
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"f... | 11 | 2013-02-20T10:29:37Z | 2017-01-19T22:11:43Z | 2017-01-19T22:11:43Z | CONTRIBUTOR | null | ``` python
In [42]: from itertools import product
In [43]: import pandas as pd
In [44]: import numpy as np
In [45]: index = pd.MultiIndex.from_tuples([t for t in product([10, 20, 30], ['a', 'b'])])
In [46]: df = pd.DataFrame(np.random.randn(6, 6), index, index)
In [47]: df
Out[47]:
10 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2905 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2905/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2905/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2905/events | https://github.com/pandas-dev/pandas/issues/2905 | 11,217,123 | MDU6SXNzdWUxMTIxNzEyMw== | 2,905 | Feature request: add a count_uniques function on dataframes | {
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"f... | 7 | 2013-02-20T21:52:07Z | 2014-05-29T06:11:32Z | 2014-05-29T06:11:32Z | NONE | null | I find myself writing this utility over and over in my usual work:
``` python
def count_uniques(df, groupby, unique_on):
"""Takes a dataframe, groups it by groupby, and counts the unique
values of unique_on."""
return df.groupby(groupby).agg({unique_on: lambda x: len(x.unique())})
```
This is easiest to ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2906/events | https://github.com/pandas-dev/pandas/issues/2906 | 11,217,478 | MDU6SXNzdWUxMTIxNzQ3OA== | 2,906 | date_range bug? | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-02-20T22:00:55Z | 2013-03-13T23:53:50Z | 2013-03-13T23:53:50Z | NONE | null | If 'start' is a timezone-aware datetime.datetime or Timestamp, then it will get shifted by several hours.
``` python
In [1]: from pandas import *
In [2]: start = Timestamp('20130220 10:00', tz='US/Eastern')
In [3]: start
Out[3]: <Timestamp: 2013-02-20 10:00:00-0500 EST, tz=US/Eastern>
In [4]: dr = date_range(start,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2907 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2907/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2907/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2907/events | https://github.com/pandas-dev/pandas/issues/2907 | 11,219,941 | MDU6SXNzdWUxMTIxOTk0MQ== | 2,907 | BUG: df.reindex() does not return a copy | {
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copy is not triggered, even though copy flag is True by default
in the same situation align will copy, and reindex_axis will as well
should I fix this? or leave as is?
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https://api.github.com/repos/pandas-dev/pandas/issues/2908 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2908/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2908/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2908/events | https://github.com/pandas-dev/pandas/issues/2908 | 11,222,690 | MDU6SXNzdWUxMTIyMjY5MA== | 2,908 | Feature request: specify chunksize for read_sql | {
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"description": "IO issue... | closed | false | null | [] | {
"closed_at": "2014-10-23T11:43:09Z",
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"created_at": "2014-07-06T18:44:54Z",
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"followers_url": "https://api.github.com/users/jr... | 10 | 2013-02-21T00:35:13Z | 2014-10-07T00:15:24Z | 2014-10-07T00:15:24Z | NONE | null | It would be helpful to iterate through rows returned from an sql query (sqlite specifically) chunk by chunk just as is done in the read_csv and text files function as described here: http://pandas.pydata.org/pandas-docs/stable/io.html#iterating-through-files-chunk-by-chunk
The return value should be an iterable object... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2909 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2909/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2909/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2909/events | https://github.com/pandas-dev/pandas/issues/2909 | 11,243,535 | MDU6SXNzdWUxMTI0MzUzNQ== | 2,909 | applymap and misc/float columns | {
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```
> df = pandas.DataFrame(data=[1,"a"])
> print(df)
0
0 1
1 a
> print(df.applymap(lambda x: x))
0
0 1
1 a
```
This does not (note the float "1." instead if "1"):
```
> df = pandas.DataFrame(data=[1.,"a"])
> print(df)
0
0 1
1 a
> print(df.applymap(lambda x: x)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2910/events | https://github.com/pandas-dev/pandas/pull/2910 | 11,248,911 | MDExOlB1bGxSZXF1ZXN0NDI1NzAxNw== | 2,910 | BUG: incorrect default in df.convert_objects was converting object types (#2909) | {
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] | closed | false | null | [] | null | 2 | 2013-02-21T16:50:34Z | 2014-06-22T07:36:52Z | 2013-02-23T01:19:53Z | CONTRIBUTOR | null | showing up in applymap (#2909)
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https://api.github.com/repos/pandas-dev/pandas/issues/2911 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2911/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2911/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2911/events | https://github.com/pandas-dev/pandas/issues/2911 | 11,250,908 | MDU6SXNzdWUxMTI1MDkwOA== | 2,911 | Slicing a dataframe for rows by set of index labels returns empty rows for missing labels, feature or bug? | {
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"name": "Indexing",
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"color": "AD... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-02-21T17:38:18Z | 2013-09-21T12:21:43Z | 2013-09-21T12:21:37Z | CONTRIBUTOR | null | what is the best way to safely slice using multiple index labels? (i.e. not create new NaN rows?) is the best way to do this to intersect the index with the searched values before searching?
```
import pandas
df = pandas.DataFrame([[1,2], [3,4]], index=['X', 'Y'], columns=['A', 'B'])
df.ix[['X', 'Z'], :]
Out[1]:
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2912/events | https://github.com/pandas-dev/pandas/issues/2912 | 11,253,587 | MDU6SXNzdWUxMTI1MzU4Nw== | 2,912 | Groupby and transform on DataFrame produces FutureWarning | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-02-21T18:45:31Z | 2013-03-20T01:22:50Z | 2013-03-20T01:22:50Z | NONE | null | Using pandas 0.10.1:
```
df1 = pd.DataFrame(np.random.randn(10))
df1.groupby(np.arange(len(df1))).transform(lambda x: x)
```
produces:
```
FutureWarning: rename with inplace=True will return None from pandas 0.11 onward.
```
In the release notes for 0.10.1 I saw the changes regarding functions with the `inplace` o... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2913 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2913/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2913/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2913/events | https://github.com/pandas-dev/pandas/issues/2913 | 11,270,634 | MDU6SXNzdWUxMTI3MDYzNA== | 2,913 | groupby float column with as_index=False will return an object column | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2013-02-22T01:43:13Z | 2013-09-21T14:17:43Z | 2013-09-21T14:17:43Z | NONE | null | I'm using version 0.10.0
```
import pandas as pd
import numpy as np
df = pd.DataFrame({
"A":np.random.randint(0, 5, 20).astype(float),
"B":np.random.random(20)})
df2 = df.groupby("A", as_index=False).mean()
print df2.dtypes
```
the result is
```
A object
B float64
```
I think this is because float ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2914 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2914/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2914/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2914/events | https://github.com/pandas-dev/pandas/pull/2914 | 11,270,869 | MDExOlB1bGxSZXF1ZXN0NDI2NzI1MQ== | 2,914 | BUG: support data column indexers that have a large numbers of values | {
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] | closed | false | null | [] | null | 0 | 2013-02-22T01:53:33Z | 2014-06-13T05:35:05Z | 2013-02-23T16:43:51Z | CONTRIBUTOR | null | This is conceptually (and implemented) like an `isin`
You can give a list of values that you want for a particular column, rather
than a boolean expression. This was already speced in the API, just
not implemented correctly!
Something like this
```
store.select('df',[pd.Term('users',[ 'a%03d' % i for i in xrange(60)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2915 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2915/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2915/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2915/events | https://github.com/pandas-dev/pandas/pull/2915 | 11,301,327 | MDExOlB1bGxSZXF1ZXN0NDI4MTA2Mw== | 2,915 | CLN: refactor of frame.py/panel.py to move common code to generic.py | {
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-02-22T19:27:53Z | 2013-04-03T19:03:07Z | 2013-04-03T19:03:07Z | CONTRIBUTOR | null | - Refactor of frame.py/panel.py to move common code to generic.py
all axis creation and manipulation code is now common (except for Series)
- added _setup_axes to create generic NDFrame structures
- moved methods (some methods moved from series as well)
- from_axes,_wrap_array,axes,ix,shape,empty,swapaxes,tra... | {
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"f... | 11 | 2013-02-23T20:20:45Z | 2019-01-01T17:28:25Z | 2019-01-01T17:28:25Z | NONE | null | Just pushed `scripts/find_undoc_args.py`. putting this here
for the occasional drive-by contributor, and newcomers who would appreciate
an opportunity to make a "low-risk" first PR.
- [x] 1) [pandas/compat/scipy.py:8 (scoreatpercentile())](https://github.com/pydata/pandas/blob/master/pandas/compat/scipy.py#L8) - **Mis... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2917 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2917/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2917/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2917/events | https://github.com/pandas-dev/pandas/pull/2917 | 11,325,890 | MDExOlB1bGxSZXF1ZXN0NDI5MDkzMg== | 2,917 | DOC: add "kde" and "density" to plot kind docstring | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-02-23T21:51:01Z | 2013-09-19T07:56:45Z | 2013-02-23T22:07:36Z | CONTRIBUTOR | null | Changes accepted `kind` parameter values from `{'line', 'bar', 'barh'}` to `{'line', 'bar', 'barh', 'kde', 'density'}`
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https://api.github.com/repos/pandas-dev/pandas/issues/2918 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2918/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2918/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2918/events | https://github.com/pandas-dev/pandas/pull/2918 | 11,326,181 | MDExOlB1bGxSZXF1ZXN0NDI5MTAyOA== | 2,918 | Verbose info configuration | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2919 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2919/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2919/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2919/events | https://github.com/pandas-dev/pandas/pull/2919 | 11,326,223 | MDExOlB1bGxSZXF1ZXN0NDI5MTA0MA== | 2,919 | ENH: add escape parameter to to_html() | {
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"followers_url": "https://api.github.com/users/wesm/... | 15 | 2013-02-23T22:17:49Z | 2014-06-13T07:22:05Z | 2013-04-10T07:34:26Z | CONTRIBUTOR | null | Treating DataFrame content as plain text, rather than HTML markup, by escaping
everything (#2617) seems like the right default for `to_html()`, however, if a DataFrame contains HTML ([example](http://stackoverflow.com/questions/14627380/pandas-html-output-with-conditional-formatting)) or text already HTML escaped, it r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2920 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2920/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2920/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2920/events | https://github.com/pandas-dev/pandas/pull/2920 | 11,328,540 | MDExOlB1bGxSZXF1ZXN0NDI5MTc3NA== | 2,920 | BLD: fixed file modes | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2921/events | https://github.com/pandas-dev/pandas/pull/2921 | 11,335,475 | MDExOlB1bGxSZXF1ZXN0NDI5Mzc1OQ== | 2,921 | ENH: change flatten to ravel (in internals/pytables) | {
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- added frame_to_csv_mixed to vbench
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https://api.github.com/repos/pandas-dev/pandas/issues/2922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2922/events | https://github.com/pandas-dev/pandas/pull/2922 | 11,346,103 | MDExOlB1bGxSZXF1ZXN0NDI5NzEzMg== | 2,922 | ENH: add .iloc attribute to provide location-based indexing | {
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`.iloc` for pure integer based indexing
`.loc` for pure label based indexing
`.iat` for fast scalar access by integer location
`.at` for fast scalar access by label location
Much updated docs, test suite, and example
In the new `test_indexing.py`, you can change the `_verbose` flag t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2923/events | https://github.com/pandas-dev/pandas/issues/2923 | 11,349,945 | MDU6SXNzdWUxMTM0OTk0NQ== | 2,923 | Bug with plot of a non-monotonic Series | {
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The following example gives markers at 4 different positions, whereas it should be 2 different positions.
```
In [168]: x1
Out[168]:
2012-07-03 17:16:07+00:00 1.784596
2012-07-03 15:42:00+00:00 1.786576
In [169]: x1.sort_index().plot(style='g^'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2924/events | https://github.com/pandas-dev/pandas/issues/2924 | 11,354,483 | MDU6SXNzdWUxMTM1NDQ4Mw== | 2,924 | Multilevel indexing with .to_latex() method merges two index columns | {
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using the .to_latex method on a multilevel frame (specifically using a pivot_table) merges the two index columns so that it does not render correctly in latex. Looks like it is missing the & for blank cells in index. and also an & for the columns. Manually inserted those in output and it fixe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2925/events | https://github.com/pandas-dev/pandas/pull/2925 | 11,361,929 | MDExOlB1bGxSZXF1ZXN0NDMwNDMwOQ== | 2,925 | ENH: numexpr on boolean frames | {
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```
name
indexing_dataframe_boolean 13.3200 125.3521 0.1063
frame_mult 21.7157 36.6353 0.5928
frame_add ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2926/events | https://github.com/pandas-dev/pandas/issues/2926 | 11,369,083 | MDU6SXNzdWUxMTM2OTA4Mw== | 2,926 | date_range with a given timezone fails if the start argument also has a timezone. | {
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This code will raise [this assert](https://github.com/pydata/pandas/blob/master/pandas/tseries/index.py#L303-304)
My debugging shows that the code is testing the equality of a timezone object (inferred_tz) to a string (tz).
date_range si supposed to receive a string so I ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2927/events | https://github.com/pandas-dev/pandas/pull/2927 | 11,369,466 | MDExOlB1bGxSZXF1ZXN0NDMwNzg5OA== | 2,927 | BUG: Fix tz handling in date_range when start arg has tz, #2926 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2928/events | https://github.com/pandas-dev/pandas/issues/2928 | 11,376,318 | MDU6SXNzdWUxMTM3NjMxOA== | 2,928 | Care with dtype line in Series repr | {
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```
you 12
the 8
and 8
a 7
it 7
for 6
in 6
gcc-40 5
to 5
installed 5
some 4
pip 4
if 4
have 4
is 4
Dtype: int64
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2013-02-25T19:55:42Z | 2013-03-14T00:38:12Z | 2013-03-14T00:38:12Z | NONE | null | When I unstack() one level of a DataFrame having only int64 column dtypes (and a MultiIndex created only from int64 values), the result will have float64 columns. This happens even when the unstacked DataFrame does not have any missing data.
Is this expected? Is there any way to prevent unstack() from changing the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2930/events | https://github.com/pandas-dev/pandas/pull/2930 | 11,382,745 | MDExOlB1bGxSZXF1ZXN0NDMxMzU4MA== | 2,930 | BUG: formatting for Series was printing multiple Dtype lines for long display | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2931/events | https://github.com/pandas-dev/pandas/issues/2931 | 11,385,938 | MDU6SXNzdWUxMTM4NTkzOA== | 2,931 | hdf5 compression breaks on sparse series which have all values censored | {
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Compression works fine if there's at least one non-sparse value in each series of a sparse dataframe. And dataframes with series that are completely sparse can be sto... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2932/events | https://github.com/pandas-dev/pandas/issues/2932 | 11,388,553 | MDU6SXNzdWUxMTM4ODU1Mw== | 2,932 | BUG date_parser not accurate with Timestamp | {
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```
from StringIO import StringIO
data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25
05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25"""
```... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2933/events | https://github.com/pandas-dev/pandas/pull/2933 | 11,389,362 | MDExOlB1bGxSZXF1ZXN0NDMxNzIwNw== | 2,933 | BUG: fixes issue in HDFStore w.r.t. compressed empty sparse series (GH #2931) | {
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Closes GH #2931
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https://api.github.com/repos/pandas-dev/pandas/issues/2934 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2934/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2934/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2934/events | https://github.com/pandas-dev/pandas/pull/2934 | 11,390,843 | MDExOlB1bGxSZXF1ZXN0NDMxNzk1Nw== | 2,934 | CLN autopep8 applied to all py files | {
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:)
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https://api.github.com/repos/pandas-dev/pandas/issues/2935 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2935/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2935/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2935/events | https://github.com/pandas-dev/pandas/pull/2935 | 11,393,528 | MDExOlB1bGxSZXF1ZXN0NDMxOTA4Mg== | 2,935 | BUG: timezone offset double counted using date_range and DateTimeIndex.append (fixes #2906, #2938) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2936 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2936/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2936/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2936/events | https://github.com/pandas-dev/pandas/issues/2936 | 11,395,748 | MDU6SXNzdWUxMTM5NTc0OA== | 2,936 | ENH: option to force slow code path (don't call apply function 1 too many times) in GroupBy.apply | {
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"closed_at": "2019-07-19T00:34:29Z",
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>>> def applym(df):
... print df.irow(0)['a']
... return DataFrame({'a':[1],'b':[1]})
>>> df = DataFrame({'a':[1,1,1,2,3],'b':['a','a','a','b','c']})
>>> df.groupby('b').apply(applym)
1
1
2
3
a b
b
a 0 1 1
b 0 1 1
c 0 1 1
>>>
```
applym is called twice on the first group.
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https://api.github.com/repos/pandas-dev/pandas/issues/2937 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2937/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2937/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2937/events | https://github.com/pandas-dev/pandas/pull/2937 | 11,411,296 | MDExOlB1bGxSZXF1ZXN0NDMyNjc2NA== | 2,937 | BUG: fix handling of the 'kind' argument in Series.order (GH #2811) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2938 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2938/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2938/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2938/events | https://github.com/pandas-dev/pandas/issues/2938 | 11,429,134 | MDU6SXNzdWUxMTQyOTEzNA== | 2,938 | Bugs related to DatetimeIndex, date_range. Question: Is is possible to create a index of datetime, without being auto converted to a DatetimeIndex? | {
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I wrote a lot of code using Index containing timezone-aware date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2939 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2939/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2939/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2939/events | https://github.com/pandas-dev/pandas/issues/2939 | 11,440,441 | MDU6SXNzdWUxMTQ0MDQ0MQ== | 2,939 | DataFrame.drop loses index name | {
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```
import numpy as np
import pandas
x = np.array([[0,2,4],[2,0,7],[4,7,0]])
df = pandas.DataFrame(x, index=["a","b", "c"], columns=["a","b","c"])
df.sort("b", inplace=True)
#raises error for axis=1
#df.sort("c", axis=1, inplace=True)
# same with sort_index
# desired result
df.sort("b").... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2941 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2941/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2941/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2941/events | https://github.com/pandas-dev/pandas/issues/2941 | 11,460,660 | MDU6SXNzdWUxMTQ2MDY2MA== | 2,941 | DataFrame.asof | {
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"f... | 11 | 2013-02-27T15:45:52Z | 2015-06-13T03:37:23Z | 2015-06-13T03:37:23Z | CONTRIBUTOR | null | like Series.asof, should also take skipna={none, 'any', 'all'}
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https://api.github.com/repos/pandas-dev/pandas/issues/2942 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2942/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2942/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2942/events | https://github.com/pandas-dev/pandas/issues/2942 | 11,460,762 | MDU6SXNzdWUxMTQ2MDc2Mg== | 2,942 | Multilevel indexing with .to_latex() method merges two index columns | {
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"f... | 3 | 2013-02-27T15:47:43Z | 2015-12-08T01:13:34Z | 2015-12-08T01:13:34Z | NONE | null | The to_latex method produces wrong latex code for multi-indexed funds. looks like it are missing a few blank {} and &. Net result is that the current method merges the two indeces. when used with output from pivot_table with major index giving categories and other one details this produces totally garbled result... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2943 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2943/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2943/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2943/events | https://github.com/pandas-dev/pandas/issues/2943 | 11,467,494 | MDU6SXNzdWUxMTQ2NzQ5NA== | 2,943 | Problem with import using rpy2 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2944 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2944/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2944/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2944/events | https://github.com/pandas-dev/pandas/issues/2944 | 11,473,538 | MDU6SXNzdWUxMTQ3MzUzOA== | 2,944 | where broken after sort | {
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] | closed | false | null | [] | null | 2 | 2013-02-27T20:39:07Z | 2013-02-27T22:58:12Z | 2013-02-27T22:58:12Z | CONTRIBUTOR | null | Glad I hunted this one down. Was seeing some very weird stuff. Using 0.10.1
```
import pandas
names = ["a","b","c"]
df = pandas.DataFrame([[0,2,4],[2,0,7],[4,7,0]], index=names, columns=names)
df
# a b c
#a 0 2 4
#b 2 0 7
#c 4 7 0
df2 = df.sort("b").T.sort("c").T
df.where(df <= 4, 0)
# a b c
#a 0 2... | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-02-27T22:00:01Z | 2013-02-27T22:34:45Z | 2013-02-27T22:34:45Z | CONTRIBUTOR | null | In [34]: df
Out[34]:
0 1 time
0 -0.602181 -0.657444 2009-07-01 00:00:00
1 -2.102987 -0.486064 2009-07-02 00:00:00
2 0.819956 -0.800478 2009-07-03 00:00:00
3 1.118629 -0.008019 2009-07-04 00:00:00
4 0.404452 0.606187 2009-07-05 00:00:00
In [35]: df.time.dtype
Out[35]: dtype('dateti... | {
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``` python
line = 'col1\n"\\"a,b,c\\""\n'
pandas.read_csv(StringIO(line))
Out[25]:
col1
a b c""
pandas.read_csv(StringIO(line)).ix[0]
Out[26]:
col1 c""
Name: (a, b)
```
i was expecting
``` python
pandas.read_csv(StringIO(line))
col1
0 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2949 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2949/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2949/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2949/events | https://github.com/pandas-dev/pandas/issues/2949 | 11,514,450 | MDU6SXNzdWUxMTUxNDQ1MA== | 2,949 | Alternate way of implementing PeriodIndex | {
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Code is at https://githu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2950 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2950/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2950/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2950/events | https://github.com/pandas-dev/pandas/pull/2950 | 11,515,661 | MDExOlB1bGxSZXF1ZXN0NDM3OTYwMg== | 2,950 | BUG: in-place conversion of integer series to float (on putmasking), GH #2746. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2951 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2951/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2951/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2951/events | https://github.com/pandas-dev/pandas/pull/2951 | 11,516,050 | MDExOlB1bGxSZXF1ZXN0NDM3OTc3MA== | 2,951 | CLN: argument order and dtype formatting to be consistent with standard | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2952 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2952/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2952/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2952/events | https://github.com/pandas-dev/pandas/issues/2952 | 11,530,908 | MDU6SXNzdWUxMTUzMDkwOA== | 2,952 | shift(..) applied to datetime series | {
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As an example below, I excerpted the first ten rows of end_time from the data frame listed. The dtype of this timeseries is datetime6... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2953 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2953/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2953/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2953/events | https://github.com/pandas-dev/pandas/issues/2953 | 11,545,582 | MDU6SXNzdWUxMTU0NTU4Mg== | 2,953 | BUG: centered moving windows mishandle series edges | {
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... | 20 | 2013-03-01T14:04:06Z | 2020-12-28T00:07:08Z | 2020-12-28T00:07:08Z | NONE | null | Seems like there is a bug in the centered mooving window functions, although the examples [here](http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments) don't show it.
Also `np.convolve` used like in [this post](http://stackoverflow.com/a/11352216/1301710) on Stackoverflow show... | {
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what should we do about this when an op crosses dtype boundaries and is wrong ?
(this is harder than it looks, because you can't always just get the numeric data,
a frame with only say timedelta64[ns] is ok)
maybe have a raise_on_error parameter to min? (default of T... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2955 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2955/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2955/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2955/events | https://github.com/pandas-dev/pandas/pull/2955 | 11,547,830 | MDExOlB1bGxSZXF1ZXN0NDM5NTg4OQ== | 2,955 | BUG: negative timedeltas not printing correctly | {
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BUG: fixed timedelta (in nanops.py) to work with min/max....abs still not working
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https://api.github.com/repos/pandas-dev/pandas/issues/2956 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2956/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2956/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2956/events | https://github.com/pandas-dev/pandas/pull/2956 | 11,548,131 | MDExOlB1bGxSZXF1ZXN0NDM5NjA0Mw== | 2,956 | BUG: _possibly_cast_to_timedelta should catch exceptions and quietly do no cast #2948 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2957 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2957/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2957/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2957/events | https://github.com/pandas-dev/pandas/issues/2957 | 11,555,945 | MDU6SXNzdWUxMTU1NTk0NQ== | 2,957 | BUG: numpy bug in np.abs on timedelta64 | {
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It converts them to us for some reason on numpy <= 1.6.2. (maybe numpy 1.7 is better)
workaround is to wrap in a Series and astype
tricky to fix this because we really can't detect is except in finalize, but then
have to check all return dtypes from t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2958 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2958/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2958/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2958/events | https://github.com/pandas-dev/pandas/pull/2958 | 11,564,752 | MDExOlB1bGxSZXF1ZXN0NDQwNDYxOA== | 2,958 | BUG: fixed .abs on Series with a timedelta (partial fix for 2957) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2959 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2959/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2959/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2959/events | https://github.com/pandas-dev/pandas/pull/2959 | 11,566,768 | MDExOlB1bGxSZXF1ZXN0NDQwNTYxNw== | 2,959 | BUG: changed Dtype to dtype on series displays | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2960 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2960/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2960/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2960/events | https://github.com/pandas-dev/pandas/issues/2960 | 11,574,144 | MDU6SXNzdWUxMTU3NDE0NA== | 2,960 | Plotting irregular timeseries with different indexes: last plotted timeseries dictates xaxis range | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2013-03-02T09:49:03Z | 2014-07-01T23:59:35Z | 2014-07-01T23:59:35Z | CONTRIBUTOR | null | In the following I took the extreme case where there is no overlap between the two indexes for illustration purposes. When there is some overlap there is still the same issue and the last plotted timeseries is dictating the xaxis range.
Here is the code, tested on pandas 0.13.1:
``` python
import numpy as np
import ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2961 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2961/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2961/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2961/events | https://github.com/pandas-dev/pandas/pull/2961 | 11,576,280 | MDExOlB1bGxSZXF1ZXN0NDQwOTQ3Mg== | 2,961 | Keep name after DataFrame drop (issue 2939) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2962 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2962/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2962/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2962/events | https://github.com/pandas-dev/pandas/pull/2962 | 11,590,280 | MDExOlB1bGxSZXF1ZXN0NDQxMzkyNA== | 2,962 | BUG keep name after DataFrame drop, add check_name to assert_dataframe_equal | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2963/events | https://github.com/pandas-dev/pandas/issues/2963 | 11,602,006 | MDU6SXNzdWUxMTYwMjAwNg== | 2,963 | statistics bootstrap/sample function? | {
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"f... | 4 | 2013-03-04T01:41:20Z | 2015-08-12T23:56:46Z | 2013-07-18T00:51:18Z | CONTRIBUTOR | null | Would you be interested in adding statistics bootstrap/sample function to pandas, implementing bootstrap described at:
http://statistics.ats.ucla.edu/stat/r/library/bootstrap.htm
Here is an actual implementation:
http://stackoverflow.com/a/15192860/304690
Thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/2964 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2964/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2964/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2964/events | https://github.com/pandas-dev/pandas/pull/2964 | 11,615,374 | MDExOlB1bGxSZXF1ZXN0NDQyMjYyMQ== | 2,964 | ENH Assert_frame_equal check_names to True | {
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I've added in `check_names=False` where it wo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2965 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2965/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2965/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2965/events | https://github.com/pandas-dev/pandas/pull/2965 | 11,642,606 | MDExOlB1bGxSZXF1ZXN0NDQzNTczNw== | 2,965 | BUG check both left and right indexers for None (fix 2843) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2966 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2966/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2966/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2966/events | https://github.com/pandas-dev/pandas/pull/2966 | 11,647,945 | MDExOlB1bGxSZXF1ZXN0NDQzODQyMw== | 2,966 | Enable display of cursor coordinate info in t-series plots | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-03-05T01:05:56Z | 2014-06-12T21:00:19Z | 2013-04-10T00:10:11Z | CONTRIBUTOR | null | See Github Issue [#1670](https://github.com/pydata/pandas/issues/1670).
Apologies for not having squashed my commits together; got rather confused by the process.
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] | closed | false | null | [] | null | 3 | 2013-03-05T01:21:44Z | 2013-03-06T12:41:02Z | 2013-03-06T12:41:02Z | CONTRIBUTOR | null | pretty simple fix though, just need to make sure that the result array
is typed float64 (to accomodate the nans) or int64 (if no nans)
(rather than the same as input type)
(which could give weird results in some cases), e.g. you wouldn't
want a float array back just because you fed it nans....
heres the inspiration ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2968 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2968/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2968/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2968/events | https://github.com/pandas-dev/pandas/issues/2968 | 11,649,844 | MDU6SXNzdWUxMTY0OTg0NA== | 2,968 | A weird thing | {
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```
def volchange(df,col):
del df[col]
ext = pd.DataFrame(np.random.randn(4,2),columns=['add1','add2'])
df = df.join(ext)
return df
aa = pd.DataFrame(np.random.randn(4,2),columns=['a','b'])
cc = aa
bb = volchange(cc,'a')
```
The result from running this code is:
bb has 3 co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2969 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2969/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2969/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2969/events | https://github.com/pandas-dev/pandas/issues/2969 | 11,651,162 | MDU6SXNzdWUxMTY1MTE2Mg== | 2,969 | Allow joins between MultiIndexes and normal Indexes to make use of common keys | {
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"labels_url": "htt... | 4 | 2013-03-05T03:25:39Z | 2014-02-18T22:20:28Z | 2014-02-18T22:20:07Z | NONE | null | Hi not sure the best way to describe this but this stackoverflow question explains the problem nicely: http://stackoverflow.com/questions/14149156/merge-multi-indexed-with-single-indexed-data-frames-in-pandas
Would be nice to be able to do this without the awkward reindex. Thanks again for a great project.
Currently ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2970/events | https://github.com/pandas-dev/pandas/issues/2970 | 11,671,407 | MDU6SXNzdWUxMTY3MTQwNw== | 2,970 | argmax? | {
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] | closed | false | null | [] | null | 10 | 2013-03-05T15:34:25Z | 2013-03-06T20:55:45Z | 2013-03-05T15:38:18Z | CONTRIBUTOR | null | Before I make a PR, is there an existing argmax anywhere? I often find myself wanting an easier way of asking for the argmax in a series, and I don't see a way to do it easily. From experience, this doesn't mean it doesn't exist.
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https://api.github.com/repos/pandas-dev/pandas/issues/2971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2971/events | https://github.com/pandas-dev/pandas/issues/2971 | 11,691,598 | MDU6SXNzdWUxMTY5MTU5OA== | 2,971 | BUG: io.sql.write_frame(if_exists='replace') not working as expected (with fix) | {
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```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\io\sql.py", line 202, in write_frame
cur.execute(create)
sqlite3.OperationalError: no such table: aggregate... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2972/events | https://github.com/pandas-dev/pandas/issues/2972 | 11,692,123 | MDU6SXNzdWUxMTY5MjEyMw== | 2,972 | pandas.concat bug with series of the same name and axis=1 | {
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The following snippet shows that when you concatenate a column of zeros and a column of ones the resulting dat... | {
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``` python
store.select('df', 'x != none') #this is not NaN, but the string 'none'
```
one receives exactly the same object as when doing
``` python
store.select('df', 'x = none')
```
The suggestion is that the query parser warns about the unknown or unimplemented ope... | {
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One of the examples included, a grid UI for dataframes, can be seen in the image below.
Please check it out, report issues and help spread the word.
![grid](https://f.cloud... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2975 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2975/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2975/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2975/events | https://github.com/pandas-dev/pandas/pull/2975 | 11,696,147 | MDExOlB1bGxSZXF1ZXN0NDQ2MjIwMQ== | 2,975 | BUG: handle single column frame in DataFrame.cov | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2976 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2976/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2976/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2976/events | https://github.com/pandas-dev/pandas/pull/2976 | 11,696,509 | MDExOlB1bGxSZXF1ZXN0NDQ2MjM5NQ== | 2,976 | BUG: HDFStore didn't implement != correctly for string columns query, GH #2973 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2977/events | https://github.com/pandas-dev/pandas/pull/2977 | 11,696,955 | MDExOlB1bGxSZXF1ZXN0NDQ2MjYzMQ== | 2,977 | BUG: Series.argsort failing on datetime64[ns] when NaT present, GH #2967 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2978 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2978/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2978/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2978/events | https://github.com/pandas-dev/pandas/issues/2978 | 11,707,022 | MDU6SXNzdWUxMTcwNzAyMg== | 2,978 | BUG: Printing a DataFrame containing user-defined object causes infinite loop | {
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OS: Ubuntu 12.04 x64
Description:
I put a user-defined object (which defines only **getitem**) into a dataframe. Printing the dataframe causes the system to hang. I suspect that the construction of a text representation of the object is calling my object's **getitem**.
A simple code... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2979/events | https://github.com/pandas-dev/pandas/pull/2979 | 11,709,448 | MDExOlB1bGxSZXF1ZXN0NDQ2ODM0Ng== | 2,979 | ENH: add display.max_seq_items to limit len of pprinted long sequences | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-03-06T11:42:01Z | 2014-06-13T08:16:29Z | 2013-03-10T16:12:08Z | NONE | null | There's a circuit-breaker in higher layers of the display code,
but just in case.
``` python
n [2]: pd.options.display.max_seq_items=10
In [3]: pd.core.common.pprint_thing(range(10000))
Out[3]: u'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...]'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2980 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2980/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2980/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2980/events | https://github.com/pandas-dev/pandas/issues/2980 | 11,728,747 | MDU6SXNzdWUxMTcyODc0Nw== | 2,980 | xtick label setting with improper scale | {
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... | open | false | null | [] | {
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... | 2 | 2013-03-06T19:33:23Z | 2021-04-11T01:55:15Z | null | NONE | null | I have some data that I have used to generate a stacked bar graph.
```
by_conclusion = measles_data.groupby(["AGE", "CONCLUSION"])
counts_by_cause = by_conclusion.size().unstack().fillna(0)
ax = counts_by_cause.plot(kind='bar', stacked=True)
```
However, I get a pretty crowded x-axis:
![too_many_ticks](https://f.cl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2981 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2981/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2981/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2981/events | https://github.com/pandas-dev/pandas/issues/2981 | 11,729,607 | MDU6SXNzdWUxMTcyOTYwNw== | 2,981 | Issues reading ragged CSV files | {
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"followers_url": "https://api.github.com/users/wesm/... | 4 | 2013-03-06T19:53:49Z | 2013-04-08T07:36:14Z | 2013-04-08T07:36:14Z | MEMBER | null | http://stackoverflow.com/questions/15242746/handling-variable-number-of-columns-with-pandas-python
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https://api.github.com/repos/pandas-dev/pandas/issues/2982 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2982/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2982/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2982/events | https://github.com/pandas-dev/pandas/issues/2982 | 11,732,215 | MDU6SXNzdWUxMTczMjIxNQ== | 2,982 | argmin/argmax, idxmin/idxmax and datetime64 | {
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"gists_url": "https:/... | [] | closed | false | null | [] | null | 9 | 2013-03-06T20:54:27Z | 2015-02-26T19:07:31Z | 2013-03-09T21:00:31Z | CONTRIBUTOR | null | ``` python
In [65]: s
Out[65]:
a 2013-01-02 00:00:00
b 2013-01-03 00:00:00
c 2013-01-04 00:00:00
d 2013-01-05 00:00:00
e NaT
dtype: datetime64[ns]
In [66]: s.argmin()
Out[66]: 4
In [67]: s.idxmin()
---------------------------------------------------------------------------
TypeError ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2983 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2983/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2983/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2983/events | https://github.com/pandas-dev/pandas/issues/2983 | 11,733,232 | MDU6SXNzdWUxMTczMzIzMg== | 2,983 | Google analytics error with eventLabel and totalEvents | {
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"creator": null,
"description": "2014-02-3: previous version released",
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"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
"id": 526062,
"labels_url": "htt... | 1 | 2013-03-06T21:19:44Z | 2014-02-16T22:12:44Z | 2014-02-16T22:12:44Z | NONE | null | pandas 0.10.1, on OSX mountain lion, installed through pip.
So, this works fine for me,
``` python
events = ga.read_ga(['visits'],
dimensions=["date"],
start_date='2013-01-15',
secrets=secretsfile,
account_name="******************",
prop... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2984 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2984/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2984/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2984/events | https://github.com/pandas-dev/pandas/issues/2984 | 11,739,876 | MDU6SXNzdWUxMTczOTg3Ng== | 2,984 | Feature: NaN in float64 series should become NaT with astype() | {
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"gists_url": "... | [] | closed | false | null | [] | null | 4 | 2013-03-07T00:12:25Z | 2013-03-19T23:54:33Z | 2013-03-19T23:54:33Z | CONTRIBUTOR | null | To get an auto-diff on a timeseries I do:
``` python
In [47]: ts
Out[47]:
0 2011-04-16 00:00:00.025000
1 2011-04-16 00:00:00.152999
2 2011-04-16 00:00:00.280999
3 2011-04-16 00:00:00.409000
4 2011-04-16 00:00:00.537000
5 2011-04-16 00:00:00.665000
6 2011-04-16 00:00:00.792999
7 2011-04-16 00:00... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2985 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2985/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2985/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2985/events | https://github.com/pandas-dev/pandas/pull/2985 | 11,741,683 | MDExOlB1bGxSZXF1ZXN0NDQ4NTA2NA== | 2,985 | BUG: Bug in idxmin/idxmax of ``datetime64[ns]`` Series with ``NaT`` (GH2982) | {
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- Bug in idxmin/idxmax of `datetime64[ns]` Series with `NaT`
- refactor in core/nanops.py to generically handle ops with
datetime64[ns]/timedelta64[ns]
- Series.argsort to always return int64 dtype (and ignore numpy dtype on ints),
was failing a test, indicated in a711db075d5835f45f04867752f3db64be35... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2986 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2986/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2986/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2986/events | https://github.com/pandas-dev/pandas/issues/2986 | 11,742,255 | MDU6SXNzdWUxMTc0MjI1NQ== | 2,986 | failed to build pandas on windows. | {
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] | closed | false | null | [] | null | 3 | 2013-03-07T01:33:08Z | 2013-03-12T13:39:09Z | 2013-03-12T13:39:09Z | NONE | null | I am build the latest stable version Pandas on my Windows 7 32-bit. On first try, I got a "Error: Unable to find vcvarsall.bar" error. Then I installed VC++2008 Express correctly, but the same error still didn't pass. I can locate the vcvarsall.bat on the VC++ installation directory manually, and I read msvc9compiler.p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2987 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2987/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2987/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2987/events | https://github.com/pandas-dev/pandas/pull/2987 | 11,742,821 | MDExOlB1bGxSZXF1ZXN0NDQ4NTYwMw== | 2,987 | add comparisons operator to the Period object and fixes issue 2781 | {
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"followers_url": "https://api.github.com/users/wesm/... | 7 | 2013-03-07T01:55:44Z | 2014-06-19T14:50:06Z | 2013-03-28T05:40:01Z | CONTRIBUTOR | null | This commit should fix [issue 2781](https://github.com/pydata/pandas/issues/2781). The Period object couldn't be sorted because it didn't have any **le**, **gt**, **lt**, **ge** or **cmp** methods.
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https://api.github.com/repos/pandas-dev/pandas/issues/2988 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2988/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2988/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2988/events | https://github.com/pandas-dev/pandas/issues/2988 | 11,747,709 | MDU6SXNzdWUxMTc0NzcwOQ== | 2,988 | timedelta64[ns] : argmin() failing | {
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``` python
In [22]: diff = ts - ts.shift()
In [23]: diff
Out[23]:
0 NaT
1 00:00:00.128001
2 00:00:00.127999
3 00:00:00.128001
4 00:00:00.127999
5 00:00:00.128001
6 00:00:00.127999
7 00:00:00.128001
8 00:00:00.127999
9 00:00:0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2989/events | https://github.com/pandas-dev/pandas/issues/2989 | 11,747,853 | MDU6SXNzdWUxMTc0Nzg1Mw== | 2,989 | timedelta64[ns]: max() and min() return numpy.int64 | {
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``` python
In [28]: diff.head()
Out[28]:
0 NaT
1 00:00:00.128001
2 00:00:00.127999
3 00:00:00.128001
4 00:00:00.127999
Dtype: timedelta64[ns]
In [29]: diff.max()
Out[29]: 1934745001000
In [30]: diff.max().dtype
Out[30]: dtype('int64'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2990 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2990/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2990/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2990/events | https://github.com/pandas-dev/pandas/pull/2990 | 11,759,251 | MDExOlB1bGxSZXF1ZXN0NDQ5MzMzNA== | 2,990 | ENH: support min/max on timedelta64[ns] Series | {
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```
In [168]: df = DataFrame(dict(A = s - Timestamp('20120101')-timedelta(minutes=5,seconds=5),
.....: B = s - Series(date_range('2012-1-2', periods=3, freq='D'))))
.....:
In [169]: df
Out[169]:
A B
0 -00:05:05 -1 days, 00:00:00
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https://api.github.com/repos/pandas-dev/pandas/issues/2991 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2991/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2991/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2991/events | https://github.com/pandas-dev/pandas/pull/2991 | 11,775,527 | MDExOlB1bGxSZXF1ZXN0NDUwMTQwOQ== | 2,991 | ENH/BUG: add tz argument to to_timestamp of Period GH2877 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2992 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2992/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2992/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2992/events | https://github.com/pandas-dev/pandas/pull/2992 | 11,784,541 | MDExOlB1bGxSZXF1ZXN0NDUwNjMwNQ== | 2,992 | DOC: doc updates/formatting in basics,indexing,10min | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2993 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2993/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2993/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2993/events | https://github.com/pandas-dev/pandas/issues/2993 | 11,830,989 | MDU6SXNzdWUxMTgzMDk4OQ== | 2,993 | Timestamp doesn't correctly localize datetime.date | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-03-09T00:09:17Z | 2013-03-21T07:58:54Z | 2013-03-21T07:58:54Z | NONE | null | In 0.10.1, Timestamp constructor seems to have an issue localizing datetime.date objects.
```
In [19]: import datetime
In [20]: datetime.date(2013, 3, 9)
Out[20]: datetime.date(2013, 3, 9)
In [21]: d=datetime.date(2013, 3, 9)
In [22]: Timestamp(d, tz='US/Pacific') == Timestamp(d).tz_localize('US/Pacific')
Out[22]: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2994 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2994/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2994/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2994/events | https://github.com/pandas-dev/pandas/issues/2994 | 11,831,024 | MDU6SXNzdWUxMTgzMTAyNA== | 2,994 | DataFrame.replace(<scalar>, ...) is not handled | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2013-03-09T00:11:01Z | 2013-03-21T12:31:46Z | 2013-03-21T12:31:46Z | MEMBER | null | I would expect like `df.replace(0, df.mean())` to work. And it does not
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https://api.github.com/repos/pandas-dev/pandas/issues/2995 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2995/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2995/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2995/events | https://github.com/pandas-dev/pandas/issues/2995 | 11,832,451 | MDU6SXNzdWUxMTgzMjQ1MQ== | 2,995 | Partial Selection on Hierarichal Index Not Working as Expected Beyond 2 Levels | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2996 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2996/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2996/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2996/events | https://github.com/pandas-dev/pandas/issues/2996 | 11,837,819 | MDU6SXNzdWUxMTgzNzgxOQ== | 2,996 | DataFrame self-joins | {
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| area | point | test | value |
| --- | --- | --- | --- |
| A | 11 | 0 | 1234234 |
| A | 11 | 1 | 12341234 |
| A | 16 | 0 | 234234 |
| A | 16 | 1 | 2343 |
| A | 16 | 2 | 234234 |
| C | 4 | 0 | 234234 |
| C | 4 | 1 | 234234 |
it would be nice if there were a way of grouping say columns `a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2997 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2997/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2997/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2997/events | https://github.com/pandas-dev/pandas/issues/2997 | 11,842,447 | MDU6SXNzdWUxMTg0MjQ0Nw== | 2,997 | inconsistent behaviour with .ix and setting values | {
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"followers_url": "https://api.github.com/users/wesm... | 8 | 2013-03-09T19:00:00Z | 2013-09-26T02:42:49Z | 2013-09-26T02:42:49Z | CONTRIBUTOR | null | Using .ix twice and assigning a value as in `df.ix[...].ix[...] = b` works in the first case and not in the second:
``` python
>>> # from http://stackoverflow.com/questions/15200598/partial-update-to-dataframe-with-multi-index-index-with-integer-labels/15213525#15213525
>>> print df
bucket start stop ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2998 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2998/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2998/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2998/events | https://github.com/pandas-dev/pandas/issues/2998 | 11,845,992 | MDU6SXNzdWUxMTg0NTk5Mg== | 2,998 | Series append function shifts index if timezone information exist | {
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rng1 = pd.date_range('1/1/2011 01:00', periods=10, freq='H')
rng2 = pd.date_range('1/1/2011 02:00', periods=10, freq='H')
ts1 = pd.Series(np.random.randn(len(rng1)), index=rng1).tz_localize('CET')
ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2).tz_localize('CET')
ts_result = ts1.append(ts2)
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https://api.github.com/repos/pandas-dev/pandas/issues/2999 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2999/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2999/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2999/events | https://github.com/pandas-dev/pandas/pull/2999 | 11,846,740 | MDExOlB1bGxSZXF1ZXN0NDUzMzMyMg== | 2,999 | DOC: added recommended dependencies section in install.rst | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3000 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3000/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3000/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3000/events | https://github.com/pandas-dev/pandas/issues/3000 | 11,847,968 | MDU6SXNzdWUxMTg0Nzk2OA== | 3,000 | DataFrame.from_records should be less strict about dict like types | {
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"closed_at": "2013-05-10T10:57:28Z",
"closed_issues": 340,
"created_at": "2013-01-18T03:58:28Z",
"creator": {
"avatar_url": "https://avatars.githubusercontent.com/u/329591?v=4",
"events_url": "https://api.github.com/users/wesm/events{/privacy}",
"followers_url": "https://api.github.com/users/wesm/... | 2 | 2013-03-10T03:47:40Z | 2013-03-15T20:44:14Z | 2013-03-15T20:44:14Z | NONE | null | Currently if you try `DataFrame.from_records(emails, columns=['Subject', 'From', 'Date'])` it'll fail. Emails are of type `email.message.Message`. However if you convert the email to dictionaries - the above code works.
The problem seems to stem from `_to_arrays` function in `frame.py` where the check is `elif instanc... | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/issues/3000/reactions"
} | https://api.github.com/repos/pandas-dev/pandas/issues/3000/timeline | null | null | null |
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