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https://api.github.com/repos/pandas-dev/pandas/issues/4601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4601/events | https://github.com/pandas-dev/pandas/issues/4601 | 18,209,640 | MDU6SXNzdWUxODIwOTY0MA== | 4,601 | Type inference code coerces float column into datetime | {
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In [10]: ix = [-352.737091, 183.575577]
...: df=pd.DataFrame([0,1],index=ix)
...: df.to_csv("/tmp/1.csv")
...: df2=pd.DataFrame.from_csv("/tmp/1.csv",parse_dates=True)
...: print df
...: print df2
0
-352.737091 0
183.575577 1
0
2105-11-21 22:43:41.1286... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4602/events | https://github.com/pandas-dev/pandas/pull/4602 | 18,210,986 | MDExOlB1bGxSZXF1ZXN0NzY4MzU5NA== | 4,602 | Fixes issue with TextFileReader using python engine and thousands != "," | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4603/events | https://github.com/pandas-dev/pandas/issues/4603 | 18,211,594 | MDU6SXNzdWUxODIxMTU5NA== | 4,603 | BUG: python - c “import pandas” crashing python.exe on exit | {
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However, I am not able to use pandas from cmd line: for instance the below crashes python after half a second (it crashes when exiting):
> python -c "import pandas"
This appli... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4604/events | https://github.com/pandas-dev/pandas/issues/4604 | 18,216,392 | MDU6SXNzdWUxODIxNjM5Mg== | 4,604 | BUG: reindex strange behaviour | {
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-08-18T22:45:56Z | 2013-08-21T12:44:43Z | 2013-08-21T12:44:43Z | CONTRIBUTOR | null | When I try to reindex a Series object I get a really weird result.
``` python
>>> s = pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
>>> s.reindex(['a', 'g', 'c', 'f'], method='ffill')
a 1
g 5
c NaN
f 5
dtype: float64
```
According to the documentation I should have got something like:
``` pyt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4605/events | https://github.com/pandas-dev/pandas/issues/4605 | 18,243,829 | MDU6SXNzdWUxODI0MzgyOQ== | 4,605 | Inconsistent behaviour of rename for MultiIndexed DataFrames and Series | {
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"followers_url": "https://api.github.com/users/wesm... | 12 | 2013-08-19T14:53:41Z | 2013-08-21T23:46:11Z | 2013-08-21T23:46:11Z | CONTRIBUTOR | null | If I try to rename a MultiIndexed DataFrame, everything works correctly:
``` python
In [3]: pd.DataFrame([11,21,31],
index=pd.MultiIndex.from_tuples([("A",x) for x in ["a","B","c"]])).rename(str.lower)
Out[3]:
0
a a 11
b 21
c 31
```
But if I do the same thing with the Series, it doesn't work... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4606/events | https://github.com/pandas-dev/pandas/issues/4606 | 18,252,484 | MDU6SXNzdWUxODI1MjQ4NA== | 4,606 | API: unpickle problems with pd.NaT | {
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Example
``` python
In [1]: import pandas as pd
In [2]: test_val = pd.NaT
In [3]: from cPickle import loads, dumps
In [4]: loads(dumps(test_val))
```
```
---------------------------------------------------------------------------
TypeError ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4607/events | https://github.com/pandas-dev/pandas/issues/4607 | 18,260,489 | MDU6SXNzdWUxODI2MDQ4OQ== | 4,607 | Add Clarification in Docs about Business DateOffset | {
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... | 5 | 2013-08-19T20:05:44Z | 2020-05-26T13:07:03Z | 2020-05-26T13:07:03Z | CONTRIBUTOR | null | Add clarification in docs about business `DateOffset` (for example BMonthEnd and BQuarterEnd).
How are these `DateOffset`s defined?
The docstring is not particularly helpful:
> DateOffset increments between business EOM dates
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2013-08-19T21:13:21Z | 2013-09-27T21:29:12Z | 2013-09-27T21:29:12Z | MEMBER | null | ```
In [164]: df = DataFrame(randn(10, 1))
In [165]: concat([df[0], df], axis=1)
```
throws an `AttributeError`. It should just say something about incompatible types in `concat`.
I wonder if it's worth allowing mixed types where it makes sense. E.g., `concat`-ing a `Series` and a `DataFrame`. It might be more consi... | {
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-08-19T21:56:10Z | 2013-08-24T00:11:41Z | 2013-08-24T00:11:41Z | CONTRIBUTOR | null | I believe that many of the `DateOffset`s that extend `CacheableOffset` do so incorrectly.
Looking at `pandas.tseries.offsets`, there are some subclasses of `DateOffset` are declared as `class Foo(DateOffset, CacheableOffset)` and some as `class Foo(CacheableOffset, DateOffset)`.
All that `CacheableOffset` does is to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4610/events | https://github.com/pandas-dev/pandas/pull/4610 | 18,267,236 | MDExOlB1bGxSZXF1ZXN0NzcxMTIyNw== | 4,610 | CLN: refactor Series.reindex to core/generic | {
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Incorrect results
```
In [1]: s = pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
In [2]: s.reindex(['a', 'g', 'c', 'f'], method='ffill')
Out[2]:
a 1
g 5
c NaN
f 5
dtype: float64
```
Fixed (and happens to automatically cast as well back to the original dtype)
(this w... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4611/events | https://github.com/pandas-dev/pandas/issues/4611 | 18,269,735 | MDU6SXNzdWUxODI2OTczNQ== | 4,611 | Examples of SQL -> pandas | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4612/events | https://github.com/pandas-dev/pandas/issues/4612 | 18,275,158 | MDU6SXNzdWUxODI3NTE1OA== | 4,612 | Cache repeated mapping and apply | {
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"labels_url": "htt... | 6 | 2013-08-20T02:48:56Z | 2014-02-14T13:11:12Z | 2014-02-14T13:11:12Z | NONE | null | I'm not sure if this functionality exists but it is boiler plate which I need to use quite often.
Typically I need to work with very large datasets, e.g. 10mil rows or more.
On these datasets performing a calculations such as taking a date in a specific string format can be sped up incredibly by assessing what needs t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4613/events | https://github.com/pandas-dev/pandas/pull/4613 | 18,275,865 | MDExOlB1bGxSZXF1ZXN0NzcxNTg1NQ== | 4,613 | CLN: Refactor series datetime arithmetic | {
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"followers_url": "https://api.github.com/users/wesm... | 15 | 2013-08-20T03:15:21Z | 2014-06-12T13:19:05Z | 2013-08-27T13:39:59Z | CONTRIBUTOR | null | This is a prelude to the larger refactoring of arithmetic into core/ops,
etc. It doesn't change any external behavior (and I'm relatively confident that
the test suite covers all the branches).
The complexity of the time arithmetic code was making it hard to tweak,
so I decided to change it up. It's not that complicat... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4614/events | https://github.com/pandas-dev/pandas/pull/4614 | 18,276,933 | MDExOlB1bGxSZXF1ZXN0NzcxNjMyNw== | 4,614 | Fixes usage of CacheableOffset | {
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Closes #4609
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https://api.github.com/repos/pandas-dev/pandas/issues/4615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4615/events | https://github.com/pandas-dev/pandas/issues/4615 | 18,279,376 | MDU6SXNzdWUxODI3OTM3Ng== | 4,615 | Inconsistent comparisons between Series and 2-D ndarray | {
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... | 9 | 2013-08-20T06:08:16Z | 2021-04-11T03:05:37Z | 2021-04-11T03:05:37Z | MEMBER | null | related: #4585
```
In [5]: randn(10, 2) == Series(randn(2))
Out[5]:
array([[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False]], dtype=bool)
```
bu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4616/events | https://github.com/pandas-dev/pandas/issues/4616 | 18,302,926 | MDU6SXNzdWUxODMwMjkyNg== | 4,616 | index where, first_index_where, first_value_where, first_valid_value | {
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"f... | 2 | 2013-08-20T15:35:22Z | 2021-04-11T03:06:25Z | 2021-04-11T03:06:25Z | CONTRIBUTOR | null | I've thrown in a few functions which I think are similar (but I've seen a few times asked about). e.g. this [SO question](http://stackoverflow.com/questions/18327624/find-elements-index-in-pandas-series).
User wants to get the index where a criteria is true:
```
import pandas as pd
myseries = pd.Series([1,4,0,7,5], i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4617 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4617/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4617/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4617/events | https://github.com/pandas-dev/pandas/issues/4617 | 18,307,097 | MDU6SXNzdWUxODMwNzA5Nw== | 4,617 | Set difference in Pandas | {
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"f... | 15 | 2013-08-20T16:49:05Z | 2014-05-29T07:57:59Z | 2014-05-29T07:57:59Z | NONE | null | I posted this question <a href="http://stackoverflow.com/questions/18180763/set-difference-for-pandas/18187648?noredirect=1">set difference for pandas</a> in SO but the answer I received produces some loss of precision.
The basic idea is that if you have two dataframes df1, df2, you should be able to get the rows in ... | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2013-08-21T04:09:09Z | 2013-08-21T12:45:22Z | 2013-08-21T12:45:22Z | NONE | null | ``` python
import pandas as pd
# '0.11.0'
print pd.__version__
df = pd.DataFrame(index=range(0,5), columns=['mybool'])
df.mybool = False
store = pd.HDFStore('shiftedbool.h5')
# this works
store.append('noshiftedbool', df)
# now add a shifted bool, backfilled
df['shiftedbool'] = df.mybool.shift(1).fillna(method='bfi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4619/events | https://github.com/pandas-dev/pandas/issues/4619 | 18,336,072 | MDU6SXNzdWUxODMzNjA3Mg== | 4,619 | Unexpected result/segmentation fault with .ix/.loc (0.12.0) | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-08-21T04:54:02Z | 2013-08-21T18:22:24Z | 2013-08-21T18:22:24Z | CONTRIBUTOR | null | Hitting a seg fault on a specific usage of .ix/.loc with release 0.12.0 (and current master). Any help is greatly appreciated. Let me know if I can provide any more info.
Here's an example produced with pandas 0.12.0 and numpy 1.7.1.
```
>>> df = pd.DataFrame({"A": [0, 1, 2]})
>>> print df.ix[[0,8,0]] # unexpected... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4620/events | https://github.com/pandas-dev/pandas/issues/4620 | 18,361,750 | MDU6SXNzdWUxODM2MTc1MA== | 4,620 | Groupby filter doesn't work with repeated index | {
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"followers_url": "https://api.github.com/users/wesm... | 6 | 2013-08-21T15:33:38Z | 2013-11-01T18:34:35Z | 2013-11-01T18:34:35Z | CONTRIBUTOR | null | Example:
```
In [1]: data = pandas.DataFrame(
{'pid' : [1,1,1,2,2,3,3,3],
'tag' : [23,45,62,24,45,34,25,62],
}, index=[0] * 8)
In [2]: g = data.groupby('tag')
In [3]: g.filter(lambda x: len(x) > 1)
Exception: Reindexing only valid with uniquely valued Index objects
```
cc #3680
_(I had thought there ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4621/events | https://github.com/pandas-dev/pandas/issues/4621 | 18,362,038 | MDU6SXNzdWUxODM2MjAzOA== | 4,621 | Groupby filter changes ordering | {
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Example:
```
In [1]: data = pd.DataFrame(
{'pid' : [1,1,1,2,2,3,3,3],
'tag' : [23,45,62,24,45,34,25,62],
})
In [2]: g = data.groupby('tag')
In [3]: g.filter(lambda x: len(x) > 1)
Out[3]:
pid tag
1 1 45
4 2 45
2 1 62
7 3 62
```
If ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4622/events | https://github.com/pandas-dev/pandas/pull/4622 | 18,363,360 | MDExOlB1bGxSZXF1ZXN0Nzc2MjM3MA== | 4,622 | BUG: Fix selection with ``ix/loc`` and non_unique selectors (GH4619) | {
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```
In [7]: df = DataFrame({"A": [0, 1, 2]})
In [8]: df
Out[8]:
A
0 0
1 1
2 2
In [9]: df.ix[[0,8,0]]
Out[9]:
A
0 0
8 NaN
0 0
In [10]: df = DataFrame({"A": list('abc')})
In [12]: df
Out[12]:
A
0 a
1 b
2 c
In [11]: df.ix[[0,8,0]]
Out[11]:
A
0 a
8 NaN
0 a
```
and th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4623/events | https://github.com/pandas-dev/pandas/issues/4623 | 18,378,509 | MDU6SXNzdWUxODM3ODUwOQ== | 4,623 | first line comments on a read_csv | {
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"closed_at": "2016-10-03T08:52:13Z",
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"followers_url": "https://api.github.com/users/jr... | 5 | 2013-08-21T20:31:20Z | 2016-08-03T10:15:47Z | 2016-08-03T10:15:47Z | CONTRIBUTOR | null | related #4505
It seems that commenting on the first line is a little buggy (or perhaps not well-defined):
```
In [11]: s1 = '# notes\na,b,c\n# more notes\n1,2,3'
In [12]: s2 = 'a,b,c\n# more notes\n1,2,3'
In [13]: pd.read_csv(StringIO(s1), comment='#')
Out[13]:
Unnamed: 0
a b c
NaN NaN ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4624/events | https://github.com/pandas-dev/pandas/pull/4624 | 18,378,974 | MDExOlB1bGxSZXF1ZXN0Nzc3MTM1Mg== | 4,624 | API/CLN: setitem in Series now consistent with DataFrame | {
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-08-21T20:40:34Z | 2014-06-16T23:53:39Z | 2013-08-21T23:15:01Z | CONTRIBUTOR | null | closes #4312
setitem in a frame that can change dtype
setitem_with_indexer now uses the Block.setitem machinery
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https://api.github.com/repos/pandas-dev/pandas/issues/4625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4625/events | https://github.com/pandas-dev/pandas/issues/4625 | 18,381,434 | MDU6SXNzdWUxODM4MTQzNA== | 4,625 | API: HDFStore will drop a row that is completely all-nan | {
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"followers_url": "https://api.github.com/users/wesm... | 0 | 2013-08-21T21:24:48Z | 2013-09-20T18:05:33Z | 2013-08-31T14:09:35Z | CONTRIBUTOR | null | maybe `dropna='` as default (current behavior), and allow values: `None`,`all`,False, where `all` is equivalent of `None` (current behavior), maybe settable by a `set_option` as well
related: https://github.com/pydata/pandas/pull/3013
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https://api.github.com/repos/pandas-dev/pandas/issues/4626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4626/events | https://github.com/pandas-dev/pandas/issues/4626 | 18,383,120 | MDU6SXNzdWUxODM4MzEyMA== | 4,626 | BUG: read_stata ignoring encoding? | {
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"followers_url": "https://api.github.com/users/wesm... | 0 | 2013-08-21T21:57:28Z | 2013-08-26T15:21:56Z | 2013-08-26T15:21:56Z | CONTRIBUTOR | null | I don't have time to debug right now, and maybe my expectations are just off, but it looks like `read_stata` doesn't respect the encoding keyword. I'm also not sure it's needed. AFAIK, Stata doesn't (and likely won't) support unicode. It always uses latin-1, so we can always use the latin-1 encoding for strings (maybe ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4627/events | https://github.com/pandas-dev/pandas/pull/4627 | 18,385,543 | MDExOlB1bGxSZXF1ZXN0Nzc3NTE3Mg== | 4,627 | API/CLN: make rename and copy consistent across NDFrame | {
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closes #4605
TST: add tests/test_generic.py
API/CLN: Refactor rename methods to core/generic.py; fixes Series.rename for (GH4605), and adds rename
with the same signature for `Panel`
API/CLN: Refactor Series.copy to core/generic.py; removes the order argument from Series.copy (inconsistent)
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https://api.github.com/repos/pandas-dev/pandas/issues/4628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4628/events | https://github.com/pandas-dev/pandas/issues/4628 | 18,385,801 | MDU6SXNzdWUxODM4NTgwMQ== | 4,628 | ENH: Support rename on MultiIndex | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2013-08-21T23:00:22Z | 2013-09-17T03:24:43Z | 2013-09-01T01:21:37Z | CONTRIBUTOR | null | Change up `MultiIndex.rename` to be an alias for `MultiIndex.set_names`.
- `Index.rename` : takes single object
- `MultiIndex.set_names`, `Index.set_names` - same type signature: take list of names [Index.set_names can only take iterable of length 1)
- `MultiIndex.rename` - synonym for `set_names`
So if you want to d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4629/events | https://github.com/pandas-dev/pandas/issues/4629 | 18,385,857 | MDU6SXNzdWUxODM4NTg1Nw== | 4,629 | API: arithmetic ops could accept an index as appropriate | {
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"followers_url": "https://api.github.com/users/wesm... | 6 | 2013-08-21T23:01:57Z | 2013-08-24T00:15:29Z | 2013-08-24T00:15:29Z | CONTRIBUTOR | null | Not sure if this will cause weirdness but haveing the arithmetic operations able to accept an index (when appropriate type), e.g. a datecolumn should be able to interact with a DatetimeIndex, but prob should prohibit an int column from interacting with a IntIndex?
e.g. in below `df['date']-df.index` should work
This ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4630/events | https://github.com/pandas-dev/pandas/issues/4630 | 18,386,444 | MDU6SXNzdWUxODM4NjQ0NA== | 4,630 | DOC: Add Clarification to Groupby.Filter for DataFrames | {
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I'd like to suggest adding (or amending the third example) to include a case in which the initia... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4631 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4631/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4631/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4631/events | https://github.com/pandas-dev/pandas/pull/4631 | 18,387,310 | MDExOlB1bGxSZXF1ZXN0Nzc3NjE4Mw== | 4,631 | excel reader & skip row between data & header & docs | {
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This is a continuation of:
https://github.com/pydata/pandas/pull/4404
Because:
- we hit aparently a GitHub bug
- the branches diverged too much.
I shall fix:
- in the excel.py there is a fix enabling reading xlsx files with both
datemodes: (see #4332)
- in the parsers.py t... | {
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"followers_url": "https://api.github.com/users/... | 2 | 2013-08-22T01:32:19Z | 2014-07-30T21:47:46Z | 2014-01-01T03:41:02Z | CONTRIBUTOR | null | Date converters used for testing the resolution of #4332
See also: #4631
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```
In [57]: a, b = Series(rand(10) > 0.5), Series(rand(10) > 0.5)
In [58]: a
Out[58]:
0 True
1 True
2 False
3 False
4 True
5 False
6 False
7 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4634/events | https://github.com/pandas-dev/pandas/pull/4634 | 18,390,918 | MDExOlB1bGxSZXF1ZXN0Nzc3ODA5MQ== | 4,634 | now sectionwise: excel / date_parser #4332 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4635/events | https://github.com/pandas-dev/pandas/issues/4635 | 18,393,493 | MDU6SXNzdWUxODM5MzQ5Mw== | 4,635 | Exception: tables cannot write this data -> | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4636/events | https://github.com/pandas-dev/pandas/issues/4636 | 18,404,545 | MDU6SXNzdWUxODQwNDU0NQ== | 4,636 | Cannot update boxplot via axes | {
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``` python
df = pd.DataFrame({'A':[1,3,2,5,4,7,6], 'B':[1,1,1,1,2,2,2]})
fig, ax = plt.subplots()
df.boxplot('A', 'B', ax=ax)
```
I cannnot change the plot anymore via `ax`, e.g. `ax.set_ylim(0,20)` or `ax.set_ylabel("Something")` has no effect on the figure.
C... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4637/events | https://github.com/pandas-dev/pandas/issues/4637 | 18,409,360 | MDU6SXNzdWUxODQwOTM2MA== | 4,637 | LastWeekOfMonth DateOffset | {
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"followers_url": "https://api.github.com/users/wesm... | 5 | 2013-08-22T12:18:49Z | 2015-04-13T19:05:36Z | 2014-02-18T22:42:42Z | CONTRIBUTOR | null | Create a `DateOffset` that represents the a weekday in last week of the month, such as "the last Tuesday of each month"
Currently there is a `WeekOfMonth` `DateOffset` but that class only takes `week : {0, 1, 2, 3, ...}`. What we actually want is `week=-1`
I will submit a PR.
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https://api.github.com/repos/pandas-dev/pandas/issues/4638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4638/events | https://github.com/pandas-dev/pandas/issues/4638 | 18,409,535 | MDU6SXNzdWUxODQwOTUzNQ== | 4,638 | REPR: kwds in DateOffset | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2013-08-22T12:23:31Z | 2013-09-27T03:50:38Z | 2013-09-27T03:50:38Z | CONTRIBUTOR | null | Currently the signature for constructing most `DateOffset` is `(n, **kwds)`.
Optional ags are then read using `get(key)` from the `kwds` and required args using `[key]`. This is fine, but it leaves what was extracted from `kwds` still in `kwds` leading to a redundant `str` or `repr`:
```
Week(weekday=0)
<1 Week: kw... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4639/events | https://github.com/pandas-dev/pandas/pull/4639 | 18,409,748 | MDExOlB1bGxSZXF1ZXN0Nzc4ODAxNA== | 4,639 | PERF: optimize __getitem__ in DataFrame for columns lookup | {
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In [1]: df2 = DataFrame(randn(3000,1),columns=['A'])
In [2]: df3 = DataFrame(randn(3000,1))
In [3]: def h():
...: for i in xrange(10000):
...: df2['A']
...:
In [4]: def j():
...: for i in xrange(10000):
...: df3[0]
...:
```
0.12
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https://api.github.com/repos/pandas-dev/pandas/issues/4640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4640/events | https://github.com/pandas-dev/pandas/issues/4640 | 18,413,030 | MDU6SXNzdWUxODQxMzAzMA== | 4,640 | DOC: tslib.monthrange | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2013-08-22T13:37:09Z | 2015-03-03T01:25:30Z | 2015-03-03T01:25:30Z | CONTRIBUTOR | null | Does Pandas have its own `tslib.monthrange` rather than using `calendar.monthrange` for performance reasons?
If so, I will toss some comments on it indicating such.
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https://api.github.com/repos/pandas-dev/pandas/issues/4641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4641/events | https://github.com/pandas-dev/pandas/pull/4641 | 18,419,884 | MDExOlB1bGxSZXF1ZXN0Nzc5MzI4OQ== | 4,641 | ENH: (GH4629) Support for using a DatetimeIndex/PeriodsIndex directly in a datelike calculation | {
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```
In [1]: s = Series(date_range('20130101',periods=5),index=date_range('20130102',periods=5))
In [2]: s
Out[2]:
2013-01-02 2013-01-01 00:00:00
2013-01-03 2013-01-02 00:00:00
2013-01-04 2013-01-03 00:00:00
2013-01-05 2013-01-04 00:00:00
2013-01-06 2013-01-05 00:00:00
Freq: D, dtype: datetime6... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4645 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4645/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4645/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4645/events | https://github.com/pandas-dev/pandas/pull/4645 | 18,443,155 | MDExOlB1bGxSZXF1ZXN0NzgwNTU4Mw== | 4,645 | API: remove the table keyword, replaced by fmt='s|t' | {
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the same defaults as prior < 0.13.0 remain, e.g. `put` implies 's' (Storer) format
and `append` imples 't' (Table) format
closes #4584 as well
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But it's quite an often used thing to create a column which enumerates the items in each group / counts their occurrences.
You can hack it:
```
In [1]: df = pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]])
In [2]: g = df.groupby(0)
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-08-23T00:06:53Z | 2013-08-23T00:09:33Z | 2013-08-23T00:09:33Z | CONTRIBUTOR | null | if passing a `dtype= { 'column_that_is_a_timedelta' : lambda x: the_td64_parser_of_doom(x) }`
this could provide a specific dtype parser for a column
alternatively, maybe provide a `parse_timedelta` option?
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**Note:** I've only tested with `head`
```
In [7]: df = pd.DataFrame({'item_id': ['b', 'b', 'a', 'c', 'a', 'b'], 'user_id': [1,2,1,1,3,1], 'time': ra
nge(6)})
In [8]: df.groupby('user_id', as_index=False).sum()
Out[8]:
user_id time
0 1 10
1 ... | {
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closes #3417
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https://api.github.com/repos/pandas-dev/pandas/issues/4650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4650/events | https://github.com/pandas-dev/pandas/issues/4650 | 18,449,913 | MDU6SXNzdWUxODQ0OTkxMw== | 4,650 | API: add parse_timedelta kw for read_csv | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2013-08-23T01:41:53Z | 2014-09-06T20:40:40Z | 2014-09-06T20:40:40Z | CONTRIBUTOR | null | similar to `parse_dates`
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https://api.github.com/repos/pandas-dev/pandas/issues/4651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4651/events | https://github.com/pandas-dev/pandas/issues/4651 | 18,450,049 | MDU6SXNzdWUxODQ1MDA0OQ== | 4,651 | REPR: empty series not printing name in repr | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-08-23T01:48:08Z | 2013-11-02T20:58:35Z | 2013-11-02T20:58:35Z | CONTRIBUTOR | null | related #4236
maybe should print as:
`Series([], name: foo, dtype: int64)` ?
```
In [9]: s = Series([], dtype=np.int64, name='foo')
In [10]: s
Out[10]: Series([], dtype: int64)
```
```
In [7]: s2 = Series([1,2],name='foo')
In [8]: s2
Out[8]:
0 1
1 2
Name: foo, dtype: int64
```
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import pandas as pd
import numpy as np
s = pd.Series(range(10))
wh = np.where(s, s > 5, 0)
```
In 12.0 this returns a pd.Series. In master it returns an ndarray.
Is the old behavior not possible now that Series doesn't subclass ndarray?
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https://api.github.com/repos/pandas-dev/pandas/issues/4653 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4653/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4653/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4653/events | https://github.com/pandas-dev/pandas/issues/4653 | 18,458,607 | MDU6SXNzdWUxODQ1ODYwNw== | 4,653 | Inconsistent slicing with hierarchical indexing | {
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``` python
import pandas as pd
frame = pd.DataFrame(np.arange(6*6).reshape((6,6)),\
index=[['a', 'a', 'b', 'b', 'c', 'c'], [1, 2, 1, 2, 1, 2]],\
columns=[['a', 'a', 'b', 'b', 'c', 'c'], [1, 2, 1, 2,1,2]])
print frame['a']
print frame['a':'b']
print ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4654 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4654/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4654/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4654/events | https://github.com/pandas-dev/pandas/issues/4654 | 18,460,424 | MDU6SXNzdWUxODQ2MDQyNA== | 4,654 | broken generation of short revision hashes in test_perf.py | {
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Examples:
```
0958d5f7fff105a593108aeab81413551e5e63c2 -> 0958d5f (7 characters)
8625aaeefdc89549b9a1017efcee4abb68840b06 -> 8625aaee (8 characters)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4655 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4655/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4655/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4655/events | https://github.com/pandas-dev/pandas/pull/4655 | 18,460,878 | MDExOlB1bGxSZXF1ZXN0NzgxNDkzMw== | 4,655 | fix generation of short revision hashes in test_perf | {
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However I get this error from the `_repr_footer` method of `TimeSeries`. By comparing this method with `_repr_footer` of `Series` I saw that Series uses `com.pprint_thing` to get `namestr`, while TimeSeries uses `str`.
Chan... | {
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this is a revert to #1073/#1069
now a call to `__nonzero__` raises `ValueError` ALWAYS
The following is the behavior
```
In [17]: s = Series(randn(4))
In [18]: df = DataFrame(randn(10,2))
In [19]: s_empty = Series()
In [20]: df_empty = DataFrame()
In [5]: bool(s)
Out[5]: ValueError: The truth value... | {
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This just adds a release note to #4236: Boolean indexing on an empty series loses index names.
Actually that commit really just adds tests for this, since they now already pass in master. It also, fixes behaviour of `infer_dtype` with empty arrays.
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I just fixed the call to ndarray.reshape with order as a positional argument, which is wrong. Please note that I had to remove a test.
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writer = pd.ExcelWriter(filename)
" File "C:\Anaconda\lib\site-packages\pandas\io\excel.py", line 350, in **init**
from openpyxl.workbook import Workbook
ImportError: No module named openpyxl.workbook"
I'm using Python 2.7 and updated Pandas to 0.12.0
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"f... | 0 | 2013-08-23T21:39:42Z | 2021-04-11T03:07:37Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/18411110/python-pandas-timeseries-resample-giving-unexpected-results/18411516?noredirect=1#comment27046758_18411516
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```
t1 = pd.Series(pd.date_range('1/1/2011', periods=10, freq='H'))
t2 = pd.Series(pd.date_range('1/1/2011 01:02:03', periods=10, freq='H'))
x = t2-t1
x.astype('m8[ms]')
```
works well in pandas-0.11.0 (numpy-1.7.1), however raises an error in pandas-0.1... | {
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"f... | 6 | 2013-08-24T18:55:24Z | 2021-06-24T04:43:41Z | 2021-06-24T04:43:40Z | MEMBER | null | related: https://github.com/pydata/pandas/issues/3513
[related SO question](http://stackoverflow.com/questions/18419657/dataframe-merge-in-pandas)
would probably have to write our own nan-handling dot since multiply-and-sum is inefficient for large frames.
cc @hayd
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2013-08-25T00:04:15Z | 2014-07-03T14:52:51Z | 2013-08-26T15:18:22Z | CONTRIBUTOR | null | ``` python
s = pd.Series(range(10)).astype(float)
s[s > 8] = None
s
```
```
/Users/datacaliber/Dropbox/Projects/trading/python/externals/pandas/pandas/core/internals.py in create_block(v, m, n, item, reshape)
596 # change the dtype
597 if nv is None:
--> 598 ... | {
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"f... | 20 | 2013-08-25T05:14:47Z | 2021-04-11T03:09:18Z | 2021-04-11T03:09:17Z | MEMBER | null | - [ ] date formatting #2583, PR #4313, #6797
- [ ] string spacing (justification?) #4158
- [ ] float format #2502, #2069
- [ ] int format, #6502
- [ ] timedelta fromat #6783
[SO question](http://stackoverflow.com/questions/18425400/printing-to-csv-file-with-automatic-width)
something like
``` python
df.to_csv(forma... | {
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```
frame = pd.DataFrame({
'home' : ['CHI', 'ATL', 'SEA', 'DET', 'STL','HOU' ,'CHI','CHI'],
'away' : ['DET', 'CHI', 'HOU', 'TOR', 'DAL', 'STL', 'MIA', 'SEA'... | {
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closes #3417
closes #4649 (pr)
```
In [1]: df = pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=list('abcde'))
In [2]: g = df.groupby(0, as_index=False)
In [3]: g.apply(lambda x: x)
Out[3]:
0 1
a 1 2
b 2 3
c 1 4
d 1 5
e 2 6
In [4]: g.head(2)
Out[4]:
0 1
a 1 2
c 1 4
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``` python
from pandas import DataFrame
import numpy as np
dat_dtype = {
'names' : ('column_one', 'col_two'),
'formats' : ('i', 'd')}
dat = np.zeros(4, dat_dtype)
dat['column_one'] = 10**(np.arange(4))
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"followers_url": "https://api.github.com/users/... | 3 | 2013-08-26T02:56:51Z | 2013-09-28T19:39:59Z | 2013-09-28T19:39:59Z | CONTRIBUTOR | null | Create method `get_effective_cell` that takes `sheet`, `cellx`, `rowx` and returns the Excel `cell` at that location taking in to account merged cells.
I will submit PR
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"f... | 39 | 2013-08-26T13:59:28Z | 2019-12-28T05:07:47Z | null | CONTRIBUTOR | null | Create a class / method for number parsing. This would be a generalization of the code that remove the thousands separators from numbers (see: https://github.com/cancan101/pandas/blob/1703ef44cd6b98e17c785c9120e29bbeefdefd1c/pandas/io/parsers.py#L1502).
Other items include:
1. Alternative decimal point
2. Using parent... | {
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```
In [1]: s = '06.02.2013;13:00;1.000,215;0,215;0,185;0,205;0,00'
In [2]: pd.read_csv(StringIO(s), sep=';', header=None, parse_dates={'Dates': [0, 1]}, index_col=0, decimal=',', thousands='.')
Out[2]:
2 3 ... | {
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Add keyword argument to ExcelFile parser to take an integer / list for the rows to interpret as header rows. If more than one, interpret as hierarchical columns / MultiIndex.
Presumably this would also allow you to round-trip Data Frame with hierarchical columns.
Basically, if you have something spann... | {
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The logic of `get_schema` seems simple enough at the moment (though it still doesn't support Oracle). Should it be replaced by SQLAlchemy integration?
ref: https://git... | {
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The same item occurs in one column of dtype object with different types. This can cause problems in later operations that rely on the same item having the same type for all its occurrences, for example, groupby.
In the example below, nothing in the file data.csv is quoted. I have been trying to reproduc... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4682 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4682/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4682/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4682/events | https://github.com/pandas-dev/pandas/issues/4682 | 18,577,971 | MDU6SXNzdWUxODU3Nzk3MQ== | 4,682 | ENH: HTML - parse multiple rows in header as MultiIndex columns | {
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Presumably this would also allow you to round-trip Data Frame with hierarchical columns.
I don't think this already exists.
related #4468
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the trailing ")" and the leading "$" are in different columns (aka td's / cells) from the number..
There should be an option to merge all column under a given heading (#4679).
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- ENH: GH3371 support timedelta fillna
- BUG: add support for datetime64 ffill/bfill
- INT: add TimeDeltaBlock support in internals
```
In [3]: s = Series([Timestamp('20130101'),Timestamp('20130101'),
Timestamp('20130102'),Timestamp('20130103 9:01:01')])
In [4]: td = s.diff()
In [5]: t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4685/events | https://github.com/pandas-dev/pandas/issues/4685 | 18,588,075 | MDU6SXNzdWUxODU4ODA3NQ== | 4,685 | ENH: More convenient regex? | {
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"followers_url": "https://api.github.com/users/wesm... | 9 | 2013-08-27T04:57:44Z | 2013-09-28T17:26:33Z | 2013-09-20T14:37:39Z | CONTRIBUTOR | null | How about circumventing annoying match objects by providing an additional, somewhat redundant string method?
```
s.str.extract(pattern)
```
as a shortcut for
```
s.str.match(pattern).str.get(0)
```
If the pattern contains multiple groups, a DataFrame should be returned. Thoughts? Maybe @hayd would be into this one.... | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-08-27T06:06:56Z | 2013-09-10T19:54:07Z | 2013-09-10T19:54:07Z | CONTRIBUTOR | null | Hitting an exception with the following code on 0.12.0 (and current master). Copied 0.11.0 behavior for reference, but feel free to reject if assignment to non-contiguous columns of a MultiIndex using a partial label is illegal/unsupported.
```
import numpy as np
import pandas as pd
df = pd.DataFrame(
np.ones((1... | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2013-08-27T09:53:05Z | 2016-10-12T23:04:09Z | 2013-09-28T19:55:36Z | NONE | null | The following code
```
import numpy as np
import pandas as pd
hor= [str(k) for k in range(3)]
columns=[ 'A','B','C', 'D' ]
ind1=np.repeat(hor, len(columns))
ind2=np.tile(columns,4)
df=pd.DataFrame(index=[ind1,ind2],columns=columns)
print(df)
print(df.xs('A',level=1))
```
generates an error when trying to tryi... | {
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"f... | 7 | 2013-08-27T15:05:21Z | 2014-06-04T12:46:42Z | 2014-06-04T12:46:42Z | CONTRIBUTOR | null | This enhancement allows Pandas to use `dateutil` timezones as well as the default `pytz` ones. The `Timestamp`'s would be inter-operable.
It has required a bit of coding in `tslib.pyx` to discriminate between `pytz` and `dateutil` tzinfo objects and load the appropriate data into the cache.
I'll create PR for this.
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There is a fair amount of tricky coding here in `tslib.pyx` as it is accessing private member... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4690 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4690/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4690/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4690/events | https://github.com/pandas-dev/pandas/issues/4690 | 18,620,759 | MDU6SXNzdWUxODYyMDc1OQ== | 4,690 | DatetimeIndex.intersection should preserve timezone | {
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```
In [75]: a = pd.date_range('2012-01-01', '2012-01-20', freq='D', tz='CET')
In [76]: b = pd.date_range('2012-01-01', '2012-01-19', freq='D', tz='CET')
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"followers_url": "https://api.github.com/users/wesm... | 4 | 2013-08-27T17:13:45Z | 2013-09-28T19:26:14Z | 2013-09-28T19:26:14Z | CONTRIBUTOR | null | Large tsv files with columns that are mixed integers and strings are not consistently converted to the same data type. Some of the integers are interpreted as integers and identical entries are interpreted as strings. Transition seems to happen at power of 2 indices indicating something is being done in chunks but no... | {
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```
import pandas as pd
df = pd.DataFrame({'a':['1']*100000 + ['X']*100000 + ['1']*100000, 'b':['b']*300000})
df.to_csv('test', sep='\t', index=False, na_rep='NA')
df2 = p... | {
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-08-27T18:30:20Z | 2013-08-28T16:49:33Z | 2013-08-28T16:49:33Z | NONE | null | I'm using Python 2.7.5 (64-bit)
Pandas 0.12.0
on Windows 8
I'm trying to access the boolean value 'containsSampledData' per Google documentation here:
https://developers.google.com/analytics/devguides/reporting/core/v3/reference#sampling
so I run:
```
from pandas.io.ga import GAnalytics
gAnalytics = GAnalytics()
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"f... | 11 | 2013-08-27T19:11:12Z | 2020-02-15T02:41:45Z | null | CONTRIBUTOR | null | related #2802
It seems that str[1] is significantly slower than `.apply(lambda x: x[1])`
See this So answer http://stackoverflow.com/a/18473330/1240268
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https://api.github.com/repos/pandas-dev/pandas/issues/4695 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4695/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4695/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4695/events | https://github.com/pandas-dev/pandas/issues/4695 | 18,629,265 | MDU6SXNzdWUxODYyOTI2NQ== | 4,695 | wrong error diagnostic message | {
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> pd.io.parsers.ExcelFile.parse(xl)
where xl is of type pd.ExcelFile()
I get the message: _TypeError_: unbound method parse() must be called with ExcelFile instance as first argument (got ExcelFile instance instead)
and the message should read: unbound met... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4696/events | https://github.com/pandas-dev/pandas/pull/4696 | 18,630,067 | MDExOlB1bGxSZXF1ZXN0Nzg5OTcwNw== | 4,696 | ENH: Series.str.extract returns regex matches more conveniently | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4697/events | https://github.com/pandas-dev/pandas/issues/4697 | 18,632,403 | MDU6SXNzdWUxODYzMjQwMw== | 4,697 | Refactor read_html to use TextParser | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2013-08-27T20:36:23Z | 2013-10-03T02:26:05Z | 2013-10-03T02:26:05Z | MEMBER | null | related: #4679
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https://api.github.com/repos/pandas-dev/pandas/issues/4698 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4698/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4698/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4698/events | https://github.com/pandas-dev/pandas/pull/4698 | 18,641,521 | MDExOlB1bGxSZXF1ZXN0NzkwNjQxOA== | 4,698 | HDFStore.append_to_multiple doesn't write rows that are all np.nan | {
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"followers_url": "https://api.github.com/users/wesm... | 19 | 2013-08-27T23:52:04Z | 2014-06-13T04:34:46Z | 2013-09-03T11:08:14Z | CONTRIBUTOR | null | Hi,
Using HDFStore.append_to_multiple, if an entire row written to any one table consists entirely of np.nan, the row is not written to the table, but is written to the other tables. The following code reproduces and fixes the issue.
I would prefer that append_to_multiple maintain synchronized rows across tables, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4699/events | https://github.com/pandas-dev/pandas/issues/4699 | 18,660,653 | MDU6SXNzdWUxODY2MDY1Mw== | 4,699 | Period() docstring inconsistent with code when freq has a mult != 1 | {
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"followers_url": "https://api.github.com/users/... | 2 | 2013-08-28T10:33:28Z | 2013-10-04T12:50:04Z | 2013-10-04T12:50:04Z | CONTRIBUTOR | null | (I love Pandas! Great work! One small issue...)
The docstring for the `Period` constructor reads:
```
freq : str, default None
e.g., 'B' for businessday, ('T', 5) or '5T' for 5 minutes
```
i.e. the docstring says that `freq` can be multiples of a period code (e.g. 5 minutes).
But if we try to use `mult != 1` ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/4700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4700/events | https://github.com/pandas-dev/pandas/issues/4700 | 18,665,960 | MDU6SXNzdWUxODY2NTk2MA== | 4,700 | inconsistency of header argument of read_html with other read_* functions | {
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