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https://api.github.com/repos/pandas-dev/pandas/issues/7401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7401/events | https://github.com/pandas-dev/pandas/issues/7401 | 35,279,505 | MDU6SXNzdWUzNTI3OTUwNQ== | 7,401 | unstack with DatetimeIndex with NaN gives "ValueError: cannot convert float NaN to integer" | {
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```
df = pd.DataFrame({'A': list('aaaaabbbbb'),
'B':pd.date_range('2012-01-01', periods=5).tolist()*2,
'C':np.zeros(10)})
df.iloc[3,1] = np.NaN
df = df.set_index(['A', 'B'])
```
unstacking gives:
```
In [6]: df.unstack()
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https://api.github.com/repos/pandas-dev/pandas/issues/7402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7402/events | https://github.com/pandas-dev/pandas/pull/7402 | 35,281,981 | MDExOlB1bGxSZXF1ZXN0MTY4ODM0ODg= | 7,402 | BUG: ix should return a Series for duplicate indices (GH7150) | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2014-06-09T12:54:30Z | 2014-06-15T23:54:11Z | 2014-06-09T13:16:42Z | CONTRIBUTOR | null | closes #7150
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https://api.github.com/repos/pandas-dev/pandas/issues/7403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7403/events | https://github.com/pandas-dev/pandas/issues/7403 | 35,283,431 | MDU6SXNzdWUzNTI4MzQzMQ== | 7,403 | BUG: incorrect unstacking with NaNs in the index | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-09T13:17:35Z | 2016-10-12T23:04:56Z | 2015-01-26T01:29:07Z | MEMBER | null | Related to #7401, but another issue I think. Some more strange behaviour with NaNs in the index when unstacking (but now not specifically to datetime).
First case:
```
In [9]: df = pd.DataFrame({'A': list('aaaabbbb'),
...: 'B':range(8),
...: 'C':range(8)})
In [10]: df.set_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7404 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7404/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7404/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7404/events | https://github.com/pandas-dev/pandas/pull/7404 | 35,283,558 | MDExOlB1bGxSZXF1ZXN0MTY4ODQzNjg= | 7,404 | BUG: mi indexing bugs (GH7399,GH7400) | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2014-06-09T13:20:02Z | 2014-06-15T23:53:41Z | 2014-06-09T15:47:18Z | CONTRIBUTOR | null | closes #7399
closes #7400
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https://api.github.com/repos/pandas-dev/pandas/issues/7405 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7405/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7405/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7405/events | https://github.com/pandas-dev/pandas/issues/7405 | 35,287,154 | MDU6SXNzdWUzNTI4NzE1NA== | 7,405 | BUG: unstacking with partial selection and mixed dtype gives "ValueError: shape of passed values ..." | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-09T14:07:12Z | 2016-10-12T23:04:56Z | 2015-01-26T01:29:07Z | MEMBER | null | And yet another unstacking bug (but now not related to NaNs as in #7403). Getting a `ValueError: Shape of passed values is (2, 3), indices imply (2, 5)` (where 3 is the correct one, 5 is the original number of rows) in some specific conditions with mixed dtype and selection of the rows:
```
In [23]: df = pd.DataFrame(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7406 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7406/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7406/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7406/events | https://github.com/pandas-dev/pandas/issues/7406 | 35,288,458 | MDU6SXNzdWUzNTI4ODQ1OA== | 7,406 | repr of MultiIndexed frame with NaT as the first element of level 0 of the index raises during repr | {
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```
In [145]: idx = pd.to_datetime([pd.NaT] + pd.date_range('20130101', periods=2).tolist())
In [146]: idx
Out[146]:
<class 'pandas.tseries.index.DatetimeIndex'>
[NaT, ..., 2013-01-02]
Length: 3, Freq: None, Timezone: None
In [149]: df = DataFrame({'X': range(len(idx)... | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2014-06-09T15:03:32Z | 2014-07-01T16:02:12Z | 2014-07-01T16:02:12Z | CONTRIBUTOR | null | from ML: https://groups.google.com/forum/#!topic/pydata/CDnF9cNR2ho
```
In [10]: df=pd.DataFrame({'i':[0]*10, 'b':[False]*10})
In [11]: vc_i=df.i.value_counts()
In [12]: vc_i.get(99,default='Missing')
Out[12]: 'Missing'
In [13]: vc_b=df.b.value_counts()
In [14]: vc_b
Out[14]:
False 10
dtype: int64
In [15]: vc... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-09T15:06:18Z | 2014-06-09T22:35:29Z | 2014-06-09T22:35:29Z | MEMBER | null | got a fix coming in a minute or so just wanted to track the issue
related #7406
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https://api.github.com/repos/pandas-dev/pandas/issues/7410 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7410/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7410/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7410/events | https://github.com/pandas-dev/pandas/pull/7410 | 35,292,765 | MDExOlB1bGxSZXF1ZXN0MTY4ODk4MjI= | 7,410 | BUG: fix repring of nat multiindex and fix neg indexing in datetimeindex | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7411 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7411/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7411/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7411/events | https://github.com/pandas-dev/pandas/issues/7411 | 35,299,910 | MDU6SXNzdWUzNTI5OTkxMA== | 7,411 | BUG: Series/Dataframe has bad repr for Right-To-Left (RTL) data | {
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... | 16 | 2014-06-09T16:29:29Z | 2021-03-09T12:57:57Z | null | NONE | null | This code
``` python
df=pd.DataFrame([['שלום','עליכם', 'מלאכי השלום', 'ברוכים הבאים']])
df.columns=['A','B','C','D']
df
```
does this:
See that the data row is close to the right?
Half a workaround is to pre... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7412/events | https://github.com/pandas-dev/pandas/issues/7412 | 35,302,134 | MDU6SXNzdWUzNTMwMjEzNA== | 7,412 | Option to keep left/right join columns (or add _merge column) to merge() and concat() | {
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"followers_url": "https://api.github.... | 9 | 2014-06-09T16:54:12Z | 2016-03-25T01:13:39Z | 2014-07-23T03:18:11Z | CONTRIBUTOR | null | Hello!
I just heard from a colleague that they're looking for the analogue of STATA's merge command (http://www.stata.com/help.cgi?merge) which generates a `_merge` column that includes a code which specifies in an outer join whether the row existed in the right table, the left table or both. I know you can hack your ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7413 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7413/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7413/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7413/events | https://github.com/pandas-dev/pandas/issues/7413 | 35,346,273 | MDU6SXNzdWUzNTM0NjI3Mw== | 7,413 | DOC: clarify the behavior of the .as_matrix method | {
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"followers_url": "https://api.github.com/users/jr... | 15 | 2014-06-10T03:06:27Z | 2016-10-12T23:04:56Z | 2014-06-14T08:36:47Z | CONTRIBUTOR | null | When using `df.as_matrix()` method, rows and columns do not render as 1xN or Nx1 matricies, rather as 1xN arrays.
```
In [4]: df.ix['foo']=[5,3]
In [5]: df.ix['bar']=[2,6]
In [6]: df
Out[6]:
A B
foo 5 3
bar 2 6
[2 rows x 2 columns]
In [7]: df['A'].as_matrix()
Out[7]: array([ 5., 2.])
In [8]: df.ix['foo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7414 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7414/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7414/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7414/events | https://github.com/pandas-dev/pandas/pull/7414 | 35,370,894 | MDExOlB1bGxSZXF1ZXN0MTY5MzM0ODA= | 7,414 | TST7337: Fix more tests in windows | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-10T10:58:11Z | 2014-06-12T20:12:48Z | 2014-06-10T12:45:03Z | CONTRIBUTOR | null | Hopefully final attempt at fixing the windows test issues from dateutil timezone work. Related discussion on previous pull request [here](https://github.com/pydata/pandas/pull/7362).
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https://api.github.com/repos/pandas-dev/pandas/issues/7415 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7415/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7415/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7415/events | https://github.com/pandas-dev/pandas/issues/7415 | 35,373,206 | MDU6SXNzdWUzNTM3MzIwNg== | 7,415 | BUG: nanops._ensure_numeric can't handle object dtypes containing complex objects | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-10T11:36:08Z | 2014-06-22T15:29:59Z | 2014-06-22T15:29:36Z | CONTRIBUTOR | null | `nanops._ensure_numeric` is used to make sure ndarrays are a numeric dtype. However, it can't handle the case where the dtype is `object` and one or more of the axes is `complex`:
``` Python
>>> from pandas.core.nanops import nanmean
>>> import numpy as np
>>>
>>> value = np.vstack([np.array(np.nan).astype('O'), np.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7417/events | https://github.com/pandas-dev/pandas/pull/7417 | 35,395,216 | MDExOlB1bGxSZXF1ZXN0MTY5NDc1MzM= | 7,417 | DOC: update df.as_matrix and df.values. Closes gh7413 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7418/events | https://github.com/pandas-dev/pandas/issues/7418 | 35,397,318 | MDU6SXNzdWUzNTM5NzMxOA== | 7,418 | BUG: hour/hours (and other plurals) should the same as singulars | {
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... | 9 | 2014-06-10T16:01:20Z | 2021-04-11T04:46:21Z | null | CONTRIBUTOR | null | discovered in #5292
This seems odd to me, documented anywhere?
```
In [2]: t = pd.Timestamp('2014-06-10 8am')
In [3]: t + pd.DateOffset(hour=9)
Out[3]: Timestamp('2014-06-10 09:00:00', tz=None)
In [4]: t + pd.DateOffset(hours=9)
Out[4]: Timestamp('2014-06-10 17:00:00', tz=None)
```
note that
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7419 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7419/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7419/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7419/events | https://github.com/pandas-dev/pandas/issues/7419 | 35,419,028 | MDU6SXNzdWUzNTQxOTAyOA== | 7,419 | openpyxl version issue for things that depend on pandas | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7420 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7420/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7420/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7420/events | https://github.com/pandas-dev/pandas/issues/7420 | 35,420,873 | MDU6SXNzdWUzNTQyMDg3Mw== | 7,420 | TST: test failures on 3.4/windows for timezones | {
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======================================================================
ERROR: test_string_index_alias_tz_aware (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Doc... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7421/events | https://github.com/pandas-dev/pandas/pull/7421 | 35,423,338 | MDExOlB1bGxSZXF1ZXN0MTY5NjQzNzc= | 7,421 | PERF: Series.transform speedups (GH6496) | {
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turns out indexing into an array rather than building it up as a list
using `concat` is faster (but have to be careful of type changes).
```
# this PR
In [11]: %timeit df['signal'].groupby(g).transform(np.mean)
10 loops, best of 3: 158 ms per loop
# master
In [11]: %timeit df['signal'].groupby(g).transf... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7422/events | https://github.com/pandas-dev/pandas/issues/7422 | 35,423,895 | MDU6SXNzdWUzNTQyMzg5NQ== | 7,422 | to_sql if_exists argument with SQL server and other schema | {
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For example, while using SQL Server with my default schema set to `test`, `to_sql` inserts the table into `test.t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7423 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7423/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7423/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7423/events | https://github.com/pandas-dev/pandas/issues/7423 | 35,438,278 | MDU6SXNzdWUzNTQzODI3OA== | 7,423 | value counts NaT doesn't count as NaN | {
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```
dt = pd.to_datetime(['NaT', '2014-01-01'])
value_counts(dt) # should have one entry
```
(Actually there is an issue with timedeltas too (you can't create an timedelta index.)
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https://api.github.com/repos/pandas-dev/pandas/issues/7424 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7424/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7424/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7424/events | https://github.com/pandas-dev/pandas/pull/7424 | 35,438,408 | MDExOlB1bGxSZXF1ZXN0MTY5NzMzNzQ= | 7,424 | FIX value_counts should skip NaT | {
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fixes #5569.
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"followers_url": "https://api.github.com/users/jr... | 5 | 2014-06-11T00:03:45Z | 2014-06-11T07:15:21Z | 2014-06-11T07:15:21Z | CONTRIBUTOR | null | You can't seem to create a time delta index, even if you force object dtype:
```
In [4]: td = pd.Series([np.timedelta64(10000), pd.NaT], dtype='timedelta64[ns]')
In [5]: pd.Index(td)
Out[5]: Int64Index([10000, -9223372036854775808], dtype='int64')
In [6]: pd.Index(td, dtype= 'timedelta64[ns]')
Out[6]: Int64Index([10... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7426 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7426/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7426/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7426/events | https://github.com/pandas-dev/pandas/issues/7426 | 35,444,020 | MDU6SXNzdWUzNTQ0NDAyMA== | 7,426 | ENH: Create dataframes using every combination of given values, like R's expand.grid() | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-11T02:04:08Z | 2014-06-25T05:01:27Z | 2014-06-25T05:01:27Z | CONTRIBUTOR | null | I find R's `expand.grid()` function quite useful for quick creation of example datasets. For example:
``` R
expand.grid(height = seq(60, 70, 5), weight = seq(100, 180, 40), sex = c("Male","Female"))
```
```
height weight sex
1 60 100 Male
2 65 100 Male
3 70 100 Male
4 60 14... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7427 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7427/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7427/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7427/events | https://github.com/pandas-dev/pandas/pull/7427 | 35,471,942 | MDExOlB1bGxSZXF1ZXN0MTY5OTA5NTE= | 7,427 | add unit tests for nanops | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-11T10:55:32Z | 2014-06-20T14:42:51Z | 2014-06-11T12:30:46Z | CONTRIBUTOR | null | This adds unit tests for `pandas.core.nanops`. It also moves existing nanops tests from `test_common` to `test_nanops`.
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https://api.github.com/repos/pandas-dev/pandas/issues/7428 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7428/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7428/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7428/events | https://github.com/pandas-dev/pandas/pull/7428 | 35,481,845 | MDExOlB1bGxSZXF1ZXN0MTY5OTY3OTE= | 7,428 | make nanops._maybe_null_out work with complex numbers ( issue #7353 ) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7429/events | https://github.com/pandas-dev/pandas/issues/7429 | 35,482,245 | MDU6SXNzdWUzNTQ4MjI0NQ== | 7,429 | BUG: multi-index slicing buggy with datetime selectors | {
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```
dates = pd.DatetimeIndex([datetime.datetime(2012,1,1,12,12,12)+datetime.timedelta(days = i) for i in range(6)])
freq = [1,2]
iterables = [dates, freq]
index = pd.MultiIndex.from_product(iterables, names=['date','freque... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7430 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7430/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7430/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7430/events | https://github.com/pandas-dev/pandas/pull/7430 | 35,486,137 | MDExOlB1bGxSZXF1ZXN0MTY5OTkyNDM= | 7,430 | BUG: Bug in multi-index slicing with datetimelike ranges (strings and Timestamps) (GH7429) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7431/events | https://github.com/pandas-dev/pandas/issues/7431 | 35,494,089 | MDU6SXNzdWUzNTQ5NDA4OQ== | 7,431 | series created with DatetimeIndex when index is [None] | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-11T15:16:39Z | 2014-06-13T15:22:05Z | 2014-06-13T15:22:05Z | CONTRIBUTOR | null | This looks like a change from 0.13.1 to HEAD. If you create a series like this:
``` python
pd.Series(index=np.array([None]))
```
Then in 0.13.1 the result is:
``` python
Out[16]:
NaN NaN
dtype: float64
In [19]: x.index.dtype
Out[19]: dtype('O')
In [20]: type(x.index)
Out[20]: pandas.core.index.Index
```
but in... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7432 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7432/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7432/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7432/events | https://github.com/pandas-dev/pandas/issues/7432 | 35,494,459 | MDU6SXNzdWUzNTQ5NDQ1OQ== | 7,432 | Confusing error message on frame.__set_item__ with duplicate index entries | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-11T15:20:09Z | 2014-06-13T19:34:50Z | 2014-06-13T19:34:50Z | CONTRIBUTOR | null | Struggled with this for a while to work out why I was getting `TypeError: incompatible index of inserted column with frame index` when doing df['new_col'] = my_series.
Turns out the the series had duplicates in its index. This occured because I was importing a CSV and then setting the index. Perhaps we need a warning ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7433/events | https://github.com/pandas-dev/pandas/issues/7433 | 35,506,846 | MDU6SXNzdWUzNTUwNjg0Ng== | 7,433 | SQLAlchemy MySQL df.to_sql() creates 32 bit int MySQL columns for int64 df cols | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-11T17:30:26Z | 2014-07-05T13:48:39Z | 2014-07-05T13:48:39Z | CONTRIBUTOR | null | Using SQLAlchemy engine for MySQL connection and DataFrame.to_sql(), MySQLdb throws a DataError as a result of 64 bit integer out-of-range as a 32bit integer was created for the table schema.
```
engine = create_engine('mysql://mysql@localhost/db')
df = pd.DataFrame(data={'i64':2**62},index=[1])
df.info()
<class 'pand... | {
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examples:
```
In [8]: paste
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0),
'e': [True, False, True],
'f': [False, True, False],
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https://api.github.com/repos/pandas-dev/pandas/issues/7435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7435/events | https://github.com/pandas-dev/pandas/pull/7435 | 35,514,350 | MDExOlB1bGxSZXF1ZXN0MTcwMTU5OTY= | 7,435 | API: Improved inference of datetime/timedelta with mixed null objects. (GH7431) | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-11T18:51:00Z | 2014-06-13T15:26:37Z | 2014-06-13T15:22:05Z | CONTRIBUTOR | null | - regression from 0.13.1 in interpretation of an object Index
- additional tests to validate datetimelike inferences
closes #7431
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https://api.github.com/repos/pandas-dev/pandas/issues/7436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7436/events | https://github.com/pandas-dev/pandas/issues/7436 | 35,515,423 | MDU6SXNzdWUzNTUxNTQyMw== | 7,436 | tm.TestCase.assert_numpy_array_equal(...) and np.nan | {
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``` python
# In a testcase:
exp = np.array([1,2,4,np.nan])
self.assert_numpy_array_equal(exp, exp)
Traceback (most recent call last):
File "C:\data\external\pandas\pandas\tests\test_categorical.py", line 490, in test_groupby_categorical_no_compress
self.assert_numpy_array_equal(exp, exp)
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https://api.github.com/repos/pandas-dev/pandas/issues/7437 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7437/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7437/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7437/events | https://github.com/pandas-dev/pandas/pull/7437 | 35,574,570 | MDExOlB1bGxSZXF1ZXN0MTcwNDkwNzA= | 7,437 | make nanops work when ndim==1 and axis==0 ( issue #7354 ) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7438/events | https://github.com/pandas-dev/pandas/issues/7438 | 35,574,767 | MDU6SXNzdWUzNTU3NDc2Nw== | 7,438 | Arithmetic by DataFrame index | {
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import pandas as pd
```
import pandas as pd
rng = pd.date_range('1/1/2011', periods=4,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7439 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7439/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7439/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7439/events | https://github.com/pandas-dev/pandas/issues/7439 | 35,574,909 | MDU6SXNzdWUzNTU3NDkwOQ== | 7,439 | Arithmetic by DataFrame index | {
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import pandas as pd
```
import pandas as pd
rng = pd.date_range('1/1/2011', periods=4,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7440/events | https://github.com/pandas-dev/pandas/pull/7440 | 35,582,648 | MDExOlB1bGxSZXF1ZXN0MTcwNTQwNzE= | 7,440 | support axis=None for nanmedian ( issue #7352 ) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7441/events | https://github.com/pandas-dev/pandas/issues/7441 | 35,584,398 | MDU6SXNzdWUzNTU4NDM5OA== | 7,441 | [ENH] Allow passing MetaData (or meta kwargs) to high-level SQL functions | {
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... | 16 | 2014-06-12T13:40:50Z | 2020-06-20T19:41:24Z | null | CONTRIBUTOR | null | Use case: I want to query an Oracle table but I'm not the owner, so `meta.reflect(engine)` would do nothing and always return an empty list of tables rendering `read_sql_table` useless. This works: `meta.reflect(engine, schema='the_real_owner')`, however there's no way of passing the metadata / schema to the high-level... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7442 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7442/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7442/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7442/events | https://github.com/pandas-dev/pandas/pull/7442 | 35,600,430 | MDExOlB1bGxSZXF1ZXN0MTcwNjUwOTM= | 7,442 | _sanitize_column now reports proper duplicate error | {
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A very simple fix for GH7432. `internals.py` (line 3240) actually raises the correct exception (`ValueError: cannot reindex from a duplicate axis`) but the except: doesn't allow it to be raised. Here I've just allowed the exception itself to be raised since it's more useful to the user than the weird `Type... | {
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import pandas as pd
import numpy as np
%matplotlib
dt_rng = pd.date_range(start='2014-01-01', end='2014-01-31', freq='1min')
df = pd.DataFrame(np.random.randn(len(dt_rng)), index=dt_rng)
ax = df.plot()
The DataFrame is plotted correctly. However, a mouse-over shows wrong dates start... | {
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TODO List: now in #7217
## The Docs (updated 1st july, 4pm CEST)
<div class="section" id="categorical">
<span id="id1"></span><h1>Categorical<a class="headerlink" href="#categorical" title="Permalin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7445 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7445/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7445/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7445/events | https://github.com/pandas-dev/pandas/issues/7445 | 35,623,283 | MDU6SXNzdWUzNTYyMzI4Mw== | 7,445 | Column information lost in to_json() if data frame is empty | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-12T21:05:10Z | 2016-12-16T11:22:01Z | 2016-12-16T11:22:01Z | NONE | null | Create empty dataframe with a column and write it out with to_json(), then read it back in:
```
In [55]: s = pandas.DataFrame({'test': [] }, index=[] ).to_json(orient='columns')
In [56]: s
Out[56]: '{}'
In [57]: str( pandas.read_json(s, orient='columns') )
Out[57]: 'Empty DataFrame\nColumns: []\nIndex: []'
```
I thin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7446/events | https://github.com/pandas-dev/pandas/issues/7446 | 35,632,594 | MDU6SXNzdWUzNTYzMjU5NA== | 7,446 | Cannot use np.round or np.around on Float64Index | {
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In our application, we are getting floating point digit discrepancies when doing subtraction and other floating point operations on these indicies, and I believe rounding woul... | {
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we've decided to simply return `self.values.searchsorted`, since that's what was happening before, and because dealing with non-monotonic indices is a PITA and yields marginal benefit when you can just use `s.iloc[s.searchsorted(...)]`
- [x] ~~`timedelta64` tests~~ #8464
- [x] ~~more edge case testing~~ ... | {
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... | 8 | 2014-06-13T16:59:28Z | 2015-03-06T00:34:30Z | null | MEMBER | null | Use case: I would like to be able to rollback a datetime Series or Index, mapping each value to the first day of each month.
I suppose this will probably need Cython to be fast.
Example:
``` python
import pandas as pd
dates = pd.date_range('2000-01-01', periods=100)
offset = pd.tseries.offsets.MonthBegin()
print ... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-13T20:23:23Z | 2014-06-16T12:52:01Z | 2014-06-16T12:51:56Z | CONTRIBUTOR | null | Fixes a bug which prevented files containing fixed width string data from being read. Stata 13 files also allow variable length strings, which are not supported in the current version, and an explicit exception regarding this type is now given. Added tests which cover these cases, and Stata 13 format files.
fixes #... | {
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- Remove some unnecessary import
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-06-13T21:58:09Z | 2014-06-14T21:52:32Z | 2014-06-14T19:01:04Z | MEMBER | null | Even though `CustomBusinessDay.apply` can handle `np.datetime64`, most of other offsets cannot accept `datetime64` and raises `ApplyTypeError`. The fix allows all offsets `apply`, `rollforward` and `rollback` to handle `np.datetime64` properly.
```
import pandas as pd
import numpy as np
t = np.datetime64('2011-01-01 0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7453 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7453/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7453/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7453/events | https://github.com/pandas-dev/pandas/issues/7453 | 35,718,698 | MDU6SXNzdWUzNTcxODY5OA== | 7,453 | BUG: Unable to aggregate TimeGrouper | {
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"description": "pandas testing functions... | closed | false | null | [] | {
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##### 1. var, std, mean
var/std/mean raises `ValueError` when group key contains `NaT`.
```
import pandas as pd
import numpy as np
data = np.random.randn(20, 4)
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['dt'] = [date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7454 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7454/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7454/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7454/events | https://github.com/pandas-dev/pandas/issues/7454 | 35,721,560 | MDU6SXNzdWUzNTcyMTU2MA== | 7,454 | Add "season" as a time/date component to DatetimeIndex | {
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"f... | 2 | 2014-06-14T05:10:38Z | 2015-03-08T16:54:12Z | null | MEMBER | null | To help pandas to get better traction in the Earth sciences, it would be awesome to add support for climatological "season" as a [Time/Date Component](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-date-components) of `pandas.DatetimeIndex`. Season comes up in geoscience at least as often as "quarter"... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7455 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7455/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7455/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7455/events | https://github.com/pandas-dev/pandas/pull/7455 | 35,721,767 | MDExOlB1bGxSZXF1ZXN0MTcxNDA1MTQ= | 7,455 | DOC: Add how to calculate month-start to cookbook | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7456 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7456/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7456/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7456/events | https://github.com/pandas-dev/pandas/pull/7456 | 35,725,787 | MDExOlB1bGxSZXF1ZXN0MTcxNDIyNjg= | 7,456 | FIX integer column names for older sqlalchemy version GH6340, GH7330 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7457 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7457/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7457/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7457/events | https://github.com/pandas-dev/pandas/pull/7457 | 35,726,376 | MDExOlB1bGxSZXF1ZXN0MTcxNDI0OTc= | 7,457 | BUG: Better axis label handling for partial layout | {
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Handle `ticklabels` and `labels` based on below rules. Currently `labels` are always displayed even if `ticklabels` are hidden and causes confusion.
- If `sharex` is `True`, display only most bottom `xticklabels` on each columns. (Because #7035 hides the bottom-right axes). Hide `xlabel` as the same mann... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7458 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7458/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7458/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7458/events | https://github.com/pandas-dev/pandas/pull/7458 | 35,726,976 | MDExOlB1bGxSZXF1ZXN0MTcxNDI3NDI= | 7,458 | BUG: DTI.intersection doesnt preserve tz | {
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Also, found and fixed a bug which non-monotonic `Index.union` incorrectly preserves `name` when `Index` have different names.
```
# monotonic
idx1 = pd.Index([1, 2, 3, 4, 5], name='idx1')
idx2 = pd.Index([4, 5, 6, 7, 8], name='other')
idx1.intersection(idx2).name
# None (Expected)
# non-monotonic
idx1 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7459 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7459/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7459/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7459/events | https://github.com/pandas-dev/pandas/pull/7459 | 35,727,343 | MDExOlB1bGxSZXF1ZXN0MTcxNDI5MTM= | 7,459 | BUG/CLN: LinePlot uses incorrect xlim when secondary_y=True | {
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Also, refactored `secondary_y` to be handled only by `MPLPlot`, because it will be required in future fix to pass multiple axes to `df.plot`, like #7069 (I'm willing to work soon..).
```
df = pd.DataFrame(np.random.randn(5, 5), co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7460/events | https://github.com/pandas-dev/pandas/issues/7460 | 35,728,509 | MDU6SXNzdWUzNTcyODUwOQ== | 7,460 | New index created by 2 weeks resampling is 1 week off | {
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pd.Series( index=pd.date_range('2014-06-01', periods=20 ), data=range(20) ).resample( '2W-SAT', how='sum' )
Out[23]:
2014-06-07 21
2014-06-21 169
Freq: 2W-SAT, dtype: int64
```
The first entry of a 2 weeks resampling corresponds to the first week of the data.
The expected output should rather be
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https://api.github.com/repos/pandas-dev/pandas/issues/7463 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7463/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7463/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7463/events | https://github.com/pandas-dev/pandas/pull/7463 | 35,735,747 | MDExOlB1bGxSZXF1ZXN0MTcxNDY2MzE= | 7,463 | PERF: performance gains in DataFrame groupby.transform for ufuncs (GH7383) | {
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closes #7383
`DataFrame.groupby(...).transform(np.max)`
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-14T21:42:43Z | 2014-06-17T14:38:59Z | 2014-06-17T11:57:36Z | MEMBER | null | There are some offsets which cannot handle input with `tz` properly
```
pd.offsets.Day().apply(pd.Timestamp('2010-01-01 9:00', tz='US/Eastern'))
#2010-01-02 09:00:00-05:00 (Expected)
pd.offsets.CustomBusinessDay().apply(pd.Timestamp('2010-01-01 9:00', tz='US/Eastern'))
#2010-01-04 09:00:00 (tzinfo lost)
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This test
``` python
def test_pivot_index_with_nan(self):
# GH 3588
nan = np.nan
df = DataFrame({"a":['R1', 'R2', nan, 'R4'], 'b':["C1", "C2", "C3" , "C4"], "c":[10, 15, nan , 20]})
result = df.pivot('a','b','c')
expected = DataFrame([[nan,nan,nan,nan],[nan,10,nan,nan],
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https://api.github.com/repos/pandas-dev/pandas/issues/7468 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7468/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7468/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7468/events | https://github.com/pandas-dev/pandas/pull/7468 | 35,760,148 | MDExOlB1bGxSZXF1ZXN0MTcxNTcxMTQ= | 7,468 | ENH: Cast ndarray-like datetime64 arrays to Index properly | {
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not being properly cast to an `Index`, because -- due to a bug with
`np.datetime64` -- calling `np.asarray(x, dtype=object)` if x is an
ndarray of type `datetime64` results in an _integer_ array.
This PR adds tests and a work around to `pd.Index.__ne... | {
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http://nbviewer.ipython.org/urls/gist.githubusercontent.com/colindickson/850d3b5172f0320f204a/raw/62e4dfd450964326b8e3c2635c5c3f4c098a7352/panel.ipynb
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https://api.github.com/repos/pandas-dev/pandas/issues/7471 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7471/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7471/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7471/events | https://github.com/pandas-dev/pandas/issues/7471 | 35,770,444 | MDU6SXNzdWUzNTc3MDQ0NA== | 7,471 | kind='area' plot not showing | {
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`df.plot()`
but when I take the option
`df.plot(kind='area')`, I just get
``` Out[69]:
<matplotlib.axes.AxesSubp... | {
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] | closed | false | null | [] | null | 3 | 2014-06-16T10:48:34Z | 2015-10-20T22:42:26Z | 2015-10-20T22:42:26Z | MEMBER | null | Didn't test it out myself, but it appears already for a while in the doc building on Travis. Is this due to our implemenation of the example? Or something with the cython version?
@hayd I think you added this example?
```
reading sources... [ 30%] enhancingperf
In file included from /home/travis/virtualenv/python2.7... | {
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"f... | 6 | 2014-06-16T12:30:58Z | 2021-07-21T04:37:41Z | 2021-07-21T04:37:41Z | NONE | null | In pandas, unlike SQL, the rows seemed to be joining on null values. Is this a bug?
related SO: http://stackoverflow.com/questions/23940181/pandas-merging-with-missing-values/23940686#23940686
Code snippet
``` python
import pandas as pd
import numpy as np
df1 = pd.DataFrame(
[[1, None],
[2, 'y']],
colum... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7475 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7475/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7475/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7475/events | https://github.com/pandas-dev/pandas/issues/7475 | 35,809,283 | MDU6SXNzdWUzNTgwOTI4Mw== | 7,475 | ENH/BUG: support multi-index slicing assignment expansions | {
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... | 0 | 2014-06-16T15:50:33Z | 2021-04-11T04:48:45Z | null | CONTRIBUTOR | null | From SO:
http://stackoverflow.com/questions/24247255/idiomatic-multiindex-column-assignment-in-pandas/24247513#24247513
http://stackoverflow.com/questions/24258781/advanced-cross-section-with-multi-index-in-pandas
```
ix = pd.MultiIndex.from_tuples(list(enumerate(np.random.choice(['A', 'B'], 5))))
df = pd.DataFrame({... | {
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... | 2 | 2014-06-16T18:27:51Z | 2016-06-08T11:25:44Z | 2016-06-08T11:25:44Z | CONTRIBUTOR | null | not sure of the purpose of `memory_map` (e.g. whether its needed anymore or not). might be an older option. If it IS current, then needs to be documented.
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On the alt_methods branch in interpolate_1d, make copies if x, y, and new_x don't have their `writeable` attribute set. It seems a little weird that we need to do this, but it'll work around the issue so that the test can pass, anyway.
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https://api.github.com/repos/pandas-dev/pandas/issues/7480 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7480/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7480/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7480/events | https://github.com/pandas-dev/pandas/issues/7480 | 35,852,479 | MDU6SXNzdWUzNTg1MjQ3OQ== | 7,480 | Conditional Join (merge) in pandas | {
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"f... | 2 | 2014-06-17T02:03:46Z | 2021-04-11T05:52:53Z | 2021-04-11T05:52:52Z | NONE | null | I wonder if it possible to implement conditional join (merge) between pandas dataframes. Basically, I am thinking some conditional SQL-like joins:
```
select a.id, a.date, a.var1, a.var2,
b.var3
from data1 as a left join data2 as b
on (a.id<b.key+2 and a.id>b.key-3) and (a.date>b.date-10 and a.d... | {
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- [ ] vbench
- [ ] make sure there isn't a better way to do this
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C:\Users\aaa\AppData\Local\Enthought\Canopy32\User\lib\site-packages\pandas\io\excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.
.format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))
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Removed from release.rst, changed example strike price for AAPL, fixed backticks.
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`Period` and `PeriodIndex` now can contain `NaT` using `iNaT` as its internal value.
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https://api.github.com/repos/pandas-dev/pandas/issues/7488 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7488/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7488/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7488/events | https://github.com/pandas-dev/pandas/pull/7488 | 35,945,165 | MDExOlB1bGxSZXF1ZXN0MTcyNjk5MDg= | 7,488 | Switch if to elif in test_frame.test_to_csv_moar._do_test | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7489 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7489/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7489/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7489/events | https://github.com/pandas-dev/pandas/pull/7489 | 35,947,991 | MDExOlB1bGxSZXF1ZXN0MTcyNzE2MTg= | 7,489 | [doc fix] minor docstring update to fix df.to_latex() usepackage syntax | {
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... | [] | closed | false | null | [] | null | 1 | 2014-06-18T01:40:13Z | 2014-06-18T06:49:43Z | 2014-06-18T06:49:39Z | NONE | null | Minor docstring fix for option `DF.to_latex()` for `\\usepackage{booktabs}`
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https://api.github.com/repos/pandas-dev/pandas/issues/7490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7490/events | https://github.com/pandas-dev/pandas/issues/7490 | 35,956,340 | MDU6SXNzdWUzNTk1NjM0MA== | 7,490 | API: support multiple indexers for .iloc with a MultiIndex | {
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It would be nice to mirror this functionality with `.iloc`.
To my understanding, until this change, `loc` and `iloc` had a mirror syntax, where if you replaced all of your index labels with arrays of 0-indexed integers, they were equivalent, e.g., for t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7491/events | https://github.com/pandas-dev/pandas/issues/7491 | 35,961,607 | MDU6SXNzdWUzNTk2MTYwNw== | 7,491 | DOC: latex symbols/equations not rendering in stable docs | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-18T07:49:27Z | 2014-07-06T12:16:12Z | 2014-07-06T12:16:12Z | MEMBER | null | Seems somehing went wrong when I built the docs for the 0.14 version, eg http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions.
Have to look into it, reporting it here as an issue to not forget it for 0.14.1
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https://api.github.com/repos/pandas-dev/pandas/issues/7492 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7492/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7492/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7492/events | https://github.com/pandas-dev/pandas/issues/7492 | 35,964,363 | MDU6SXNzdWUzNTk2NDM2Mw== | 7,492 | Assigning datetime array to column fails with OutOfBoundsDatetime when having NaT and other unit as [ns] | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-18T08:35:24Z | 2016-12-17T23:13:43Z | 2016-12-17T23:13:43Z | MEMBER | null | Assigning an array with datetime64[ns] values including a NaT just works:
```
In [85]: a = np.array([1, 'nat'], dtype='datetime64[ns]')
In [86]: pd.Series(a)
Out[86]:
0 1970-01-01 00:00:00.000000001
1 NaT
dtype: datetime64[ns]
In [88]: df = pd.Series(a).to_frame()
In [89]: df['new'] =... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7493 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7493/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7493/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7493/events | https://github.com/pandas-dev/pandas/issues/7493 | 35,972,762 | MDU6SXNzdWUzNTk3Mjc2Mg== | 7,493 | Performance degradation in pandas 0.13/0.14 | {
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"f... | 10 | 2014-06-18T10:40:22Z | 2015-09-20T20:41:21Z | 2015-09-20T20:41:21Z | NONE | null | I reported previously on issue #7208. It was noted that .ix was slower as of pandas 0.13 but that this should be only noticeable in non-vectorized code. I sometimes however have trouble vectorizing everything. Please consider the following code in which I have a rather big correlation matrix, using multi-indexed column... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7494 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7494/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7494/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7494/events | https://github.com/pandas-dev/pandas/issues/7494 | 35,979,717 | MDU6SXNzdWUzNTk3OTcxNw== | 7,494 | Series.shift and DataFrame.shift do not preserve dtype | {
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] | closed | false | null | [] | null | 3 | 2014-06-18T12:32:22Z | 2014-07-07T15:44:35Z | 2014-07-07T15:44:35Z | NONE | null | shift() method converts int32 to float64, and you need an extra step to convert it back
``` py
s = Series(range(100), dtype=np.int32)
print s.dtype, s.shift(1).dtype
```
Version 0.14
The documentation says otherwise:
Returns : shifted : same type as caller
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https://api.github.com/repos/pandas-dev/pandas/issues/7495 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7495/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7495/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7495/events | https://github.com/pandas-dev/pandas/pull/7495 | 35,992,040 | MDExOlB1bGxSZXF1ZXN0MTcyOTcxNzc= | 7,495 | DOC: Fix index error for remote data docs. | {
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... | [] | closed | false | null | [] | null | 4 | 2014-06-18T14:40:36Z | 2014-06-18T16:23:14Z | 2014-06-18T16:23:09Z | CONTRIBUTOR | null | Fixes the index error caused by #7484. Also removes duplicate example in v0.14.1.txt
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https://api.github.com/repos/pandas-dev/pandas/issues/7496 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7496/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7496/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7496/events | https://github.com/pandas-dev/pandas/issues/7496 | 35,994,959 | MDU6SXNzdWUzNTk5NDk1OQ== | 7,496 | BUG: strange behavior of .loc indexer: falling back to integer-based indexing with list | {
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Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> a = pandas.Series()
>>> a.loc[1] = 1
>>> a.loc['a'] = 2
>>> a.loc[[-1, -2]]
1 1
a 2
dtype: int64
```
This is ok:
```
>>> a.loc[-1]
Traceback (most... | {
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```
In [4]: s = Series()
In [5]: s.loc[1] = 1
In [6]: s.loc['a'] = 2
In [7]: s.loc[-1]
KeyError: 'the label [-1] is not in the [index]'
In [8]: s.loc[[-1, -2]]
Out[8]:
-1 NaN
-2 NaN
dtype: float64
In [9]: s.loc[['4']]
Out[9]:
4 NaN
dtype: float64
In [10]: s.loc[-1] = 3
In [11]: s.loc[[-1,... | {
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Previously, bar plots looked like this:
However, the same code under '0.14.0-205-gbbde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7499/events | https://github.com/pandas-dev/pandas/pull/7499 | 36,016,539 | MDExOlB1bGxSZXF1ZXN0MTczMTIzMzY= | 7,499 | Deprecate detection of IPython frontends | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-06-18T18:58:40Z | 2014-06-24T22:01:53Z | 2014-06-24T22:01:49Z | CONTRIBUTOR | null | The check for the Qt console won't be necessary in IPython 3 and above: we decided that the Qt console's display of HTML reprs was so bad that it shouldn't attempt to show them. We also want to remove the `parent_appname` config value (ipython/ipython#4980), which will break both of these functions (making them always ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7500/events | https://github.com/pandas-dev/pandas/issues/7500 | 36,017,989 | MDU6SXNzdWUzNjAxNzk4OQ== | 7,500 | Possible Bug - math on like-indexed datetime series doesn't work as expected | {
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I have two series that are like-indexed datetimes. I'm trying to do simple mat... | {
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