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https://api.github.com/repos/pandas-dev/pandas/issues/8201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8201/events | https://github.com/pandas-dev/pandas/pull/8201 | 42,129,560 | MDExOlB1bGxSZXF1ZXN0MjA4NTQ1MjI= | 8,201 | ERR: pass original error message in read_sql_query (GH7730) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8202/events | https://github.com/pandas-dev/pandas/pull/8202 | 42,138,601 | MDExOlB1bGxSZXF1ZXN0MjA4NTg0OTQ= | 8,202 | PERF: Switch to try/except in extract() rather than instance check | {
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Timing improvement is there, but it's small.
```
In [20]: %timeit test.str.match(pattern2)
10 loops, best of 3: 170 ms per loop
In [21]: %timeit test.str.extract(pattern2)
10 loops, best of 3: 317 ms per loop
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https://api.github.com/repos/pandas-dev/pandas/issues/8203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8203/events | https://github.com/pandas-dev/pandas/pull/8203 | 42,138,899 | MDExOlB1bGxSZXF1ZXN0MjA4NTg2MjI= | 8,203 | BUG: Fix mismatched logic error in compatibility check | {
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doing the opposite. Not worth merging if we accept #7565 , but just wanted this
here as a ping if we don't so we're internally consistent.
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https://api.github.com/repos/pandas-dev/pandas/issues/8204 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8204/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8204/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8204/events | https://github.com/pandas-dev/pandas/pull/8204 | 42,144,845 | MDExOlB1bGxSZXF1ZXN0MjA4NjEyMDg= | 8,204 | DOC: reorg indexing docs | {
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DOC: add internals section (GH4082)
Mostly just splitting up the indexing docs, but I reorded / revised the Multiindex sections
a bit to avoid duplication / make more clear.
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https://api.github.com/repos/pandas-dev/pandas/issues/8205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8205/events | https://github.com/pandas-dev/pandas/pull/8205 | 42,151,025 | MDExOlB1bGxSZXF1ZXN0MjA4NjM5Mzg= | 8,205 | ENH: sql support for Timestamp (GH7103) | {
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---
Closes #7103, #7936
This adds a `pandas.io.sql.Timestamp` class to handle `Timestamp`s in `to_sql`. This basically converts it ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8206/events | https://github.com/pandas-dev/pandas/issues/8206 | 42,151,400 | MDU6SXNzdWU0MjE1MTQwMA== | 8,206 | lineterminator in to_csv doesn't work | {
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"closed_at": "2016-10-03T08:52:13Z",
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In [3]: df = pd.DataFrame({'a': ['3.4', '34.400'], 'b': ['5,5', '10,200']})
In [4]: df
Out[4]:
a b
0 3.4 5,5
1 34.400 10,200
In [5]: df.to_csv(lineterminator='-')
Out[5]: ',a,b\n0,3.4,"5,5"\n1,34.400,"10,200"\n'
```
I would've expected
```
Out[5]: ',a,b-0,3.4,"5.5"-1,34.400,"10,200"-'
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https://api.github.com/repos/pandas-dev/pandas/issues/8207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8207/events | https://github.com/pandas-dev/pandas/issues/8207 | 42,151,803 | MDU6SXNzdWU0MjE1MTgwMw== | 8,207 | CI: updates builds | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-07T20:46:49Z | 2014-10-04T14:32:22Z | 2014-10-04T14:32:22Z | CONTRIBUTOR | null | - [ ] numpy 1.9.0
- [ ] matplotlib 1.4.0
maybe 3.4 build
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https://api.github.com/repos/pandas-dev/pandas/issues/8208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8208/events | https://github.com/pandas-dev/pandas/pull/8208 | 42,152,507 | MDExOlB1bGxSZXF1ZXN0MjA4NjQ1NzQ= | 8,208 | ENH: sql support for writing NaN + datetime64 values (GH2754, GH7103) | {
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Closes #7103, #7936 (supercedes PR #8205 for this)
Closes #7567
Database drivers expect `None` to translate this to `NULL`.
Therefore, dataframe is cast to object dtype to convert `NaN` to `None`.
To check: performance impact. In principle should also not needed for integer colums (can't have NaNs)
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https://api.github.com/repos/pandas-dev/pandas/issues/8209 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8209/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8209/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8209/events | https://github.com/pandas-dev/pandas/issues/8209 | 42,157,364 | MDU6SXNzdWU0MjE1NzM2NA== | 8,209 | AttributeError on datetime.timedelta assignment after reindex | {
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A bug? Am I doing something wrong?
``` python
In [106]: import pandas as pd
In [107]: import datetime as dt
In [108]: s = pd.Series([])
In [109]: s
Out[109]: Series([], dtype: fl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8210 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8210/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8210/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8210/events | https://github.com/pandas-dev/pandas/pull/8210 | 42,160,343 | MDExOlB1bGxSZXF1ZXN0MjA4NjgyODQ= | 8,210 | BUG: Fix groupby duplicate column error message | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2014-09-08T02:41:35Z | 2014-09-14T14:30:10Z | 2014-09-10T19:50:50Z | CONTRIBUTOR | null | closes #7511
Though having duplicated column names in a dataframe is never a good idea, it may happen, and that shouldn't confuse groupby() with a meaningless message. Currently
``` python
>>> obj=pd.DataFrame(columns=["A","B","A", "C"], data=[range(4), range(2,6), range(0, 8, 2)])
>>> obj.groupby("A").mean()
Traceba... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8211 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8211/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8211/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8211/events | https://github.com/pandas-dev/pandas/issues/8211 | 42,165,801 | MDU6SXNzdWU0MjE2NTgwMQ== | 8,211 | Integers coerced to float on Series construction from dictionary. | {
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"color": "005... | closed | false | null | [] | null | 4 | 2014-09-08T05:34:48Z | 2014-09-08T14:44:56Z | 2014-09-08T11:07:41Z | CONTRIBUTOR | null | When constructing a Series from a dictionary, if the dictionary contains floats, all integer values in the dictionary are coerced to `float` **unless** the dictionary contains an integer value > `sys.maxint`.
Minimal repro case:
```
import pandas as pd
import sys
if __name__ == '__main__':
pd.show_versions()
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https://api.github.com/repos/pandas-dev/pandas/issues/8212 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8212/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8212/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8212/events | https://github.com/pandas-dev/pandas/issues/8212 | 42,170,106 | MDU6SXNzdWU0MjE3MDEwNg== | 8,212 | ENH: read_excel dtypes and converts | {
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... | 8 | 2014-09-08T07:14:30Z | 2016-12-03T12:37:32Z | 2016-12-03T12:37:32Z | NONE | null | Dear all,
Here from the mailing list: https://groups.google.com/forum/#!topic/pydata/jKiPOvYUQ1c
I have an excel table about family ages like this
| Family | People | Mean size [cm] |
| --- | --- | --- |
| Foo | 5 | 173.0 |
| Bar | 3 | 189.0 |
and I would like to use read_excel to parse it into Python. I would like... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8213/events | https://github.com/pandas-dev/pandas/issues/8213 | 42,209,475 | MDU6SXNzdWU0MjIwOTQ3NQ== | 8,213 | Template for variables in to_latex() | {
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] | closed | false | null | [] | null | 3 | 2014-09-08T15:32:54Z | 2014-09-09T13:32:45Z | 2014-09-09T13:32:45Z | NONE | null | Me and several of my colleagues typically create output for variables that have greek letters. Inside pandas, we then call them "epsilon", "sBar" or similar. Right now we have to run a string replace on the output of to_latex() and then manually save it to a file.
It'd be nice to be able to give to_latex() a template... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8214/events | https://github.com/pandas-dev/pandas/issues/8214 | 42,210,183 | MDU6SXNzdWU0MjIxMDE4Mw== | 8,214 | TST: skipping test_timedelta tests in sql after (GH8184) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8215/events | https://github.com/pandas-dev/pandas/issues/8215 | 42,210,797 | MDU6SXNzdWU0MjIxMDc5Nw== | 8,215 | Series/Dataframe to_csv inconsistency when path=None [0.14.1] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-09-08T15:44:51Z | 2014-09-09T16:58:14Z | 2014-09-09T16:58:14Z | CONTRIBUTOR | null | It looks like `Series.to_csv` and `DataFrame.to_csv` differ in their handling of `path=None`:
``` python
In [1]: import pandas as pd
In [2]: print pd.Series([1, 1, 1], index=[1, 2, 3]).to_csv(None)
None
In [3]: print pd.DataFrame([1, 1, 1], index=[1, 2, 3]).to_csv(None)
,0
1,1
2,1
3,1
```
I haven't gone digging ye... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8216 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8216/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8216/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8216/events | https://github.com/pandas-dev/pandas/issues/8216 | 42,227,469 | MDU6SXNzdWU0MjIyNzQ2OQ== | 8,216 | BUG: data frame plot hangs when logx True and some x are zero | {
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In [1]: import pandas as pd
In [2]: import matplotlib.pyplot as plt
In [3]: import numpy as np
In [8]: pandas.version.version
Out[8]: '0.14.1'
In [9]: x = [1,0]
In [10]: y= [2,3]
In [11]: df = pd.DataFrame()
In [12]: df['x'] = x
In [13]: df['y'] = y
In [14]: df
Out[14]:
x y
0 1 2
1 0 3
In [15]: df.plot('x... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8217 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8217/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8217/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8217/events | https://github.com/pandas-dev/pandas/issues/8217 | 42,236,202 | MDU6SXNzdWU0MjIzNjIwMg== | 8,217 | View returned when using squeeze=True with read_csv? | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-08T19:52:55Z | 2014-09-08T23:40:54Z | 2014-09-08T23:40:44Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd, StringIO
In [2]: csv = StringIO.StringIO('time,data\n0,10\n1,11\n2,12\n4,14\n5,15\n3,13')
In [3]: df = pd.read_csv(csv, index_col='time')
In [4]: df
Out[4]:
data
time
0 10
1 11
2 12
4 14
5 15
3 13
In [5]: df.sort(inplace=True)
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8218/events | https://github.com/pandas-dev/pandas/pull/8218 | 42,253,909 | MDExOlB1bGxSZXF1ZXN0MjA5MjQyOTM= | 8,218 | BUG: Bug in read_csv where squeeze=True would return a view (GH8217) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8219 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8219/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8219/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8219/events | https://github.com/pandas-dev/pandas/pull/8219 | 42,271,330 | MDExOlB1bGxSZXF1ZXN0MjA5MzQxNjc= | 8,219 | ENH to_latex mi index will use & sep for levels | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-09-09T05:11:14Z | 2014-09-20T22:12:29Z | 2014-09-14T14:13:51Z | CONTRIBUTOR | null | follow up of #7982
_One question I had was the use of `{ }` (is that a convention? I removed it and it seems to compile ok.)_
Note: should test MI cols and MI index. Ah cripes... the names of the column Index don't print, not did they before...
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https://api.github.com/repos/pandas-dev/pandas/issues/8220 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8220/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8220/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8220/events | https://github.com/pandas-dev/pandas/pull/8220 | 42,285,390 | MDExOlB1bGxSZXF1ZXN0MjA5NDIxMTU= | 8,220 | API: Series.to_csv(path=None) should return string to match DataFrame.to_csv() | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-09-09T09:08:06Z | 2014-09-09T11:29:36Z | 2014-09-09T11:29:21Z | CONTRIBUTOR | null | Addresses #8215. Currently, `Series.to_csv(path=None)` returns `None`. The `DataFrame` method returns a string, and already had this behaviour mentioned in the docstring, so it should be OK to make the `Series` method match this.
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https://api.github.com/repos/pandas-dev/pandas/issues/8221 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8221/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8221/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8221/events | https://github.com/pandas-dev/pandas/pull/8221 | 42,309,513 | MDExOlB1bGxSZXF1ZXN0MjA5NTcwNDE= | 8,221 | DOC: numexpr is now on github | {
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``` python
>>> pd.DataFrame([]).applymap(round)
Series([], dtype: float64)
```
full example
```
In [1]: df = pandas.DataFrame({ 'x' : [], 'y' : [], 'z' : []})
In [2]: df
Out[2]:
Empty DataFrame
Columns: [x, y, z]
Index: []
In [3]: df.applymap(lambda x: x)
Out[3]:
x ... | {
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Before:
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https://api.github.com/repos/pandas-dev/pandas/issues/8224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8224/events | https://github.com/pandas-dev/pandas/issues/8224 | 42,315,313 | MDU6SXNzdWU0MjMxNTMxMw== | 8,224 | saving Panel with PeriodIndex to HDF in table format loses Period information | {
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... | closed | false | null | [] | null | 2 | 2014-09-09T14:57:03Z | 2014-09-09T15:02:58Z | 2014-09-09T15:02:12Z | CONTRIBUTOR | null | ```
(Pdb) print pd.__version__
0.14.0-403-ge131df1
(Pdb) print data
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 35 (major_axis) x 21 (minor_axis)
Items axis: Constant Prices to Current Prices
Major_axis axis: Andaman & Nicobar to West Bengal
Minor_axis axis: 1994 to 2014
(Pdb) print data.minor_axis
<class... | {
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... | 21 | 2014-09-09T18:29:09Z | 2015-10-30T14:58:28Z | 2015-10-30T14:58:28Z | CONTRIBUTOR | null | Original post, edited to correct ISO 8601 formats. The originally titled bug does not exist. There is, however, some unexpected behavior:
> It looks like short-form ISO 8601 parsing uses the right sign after all:
>
> ``` python
> In [1]: import pandas as pd
>
> In [2]: t = pd.Timestamp('2013-11-03 1:30:00', tz='Am... | {
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so this type of syntax
`index1 + index2`
would be replaced by
`index1.union(index2)`
and
`index1 - index2`
by
`index1.diff(index2)`
Their is some internal code to fix but not a lot.
This would go a long ways toward simplifying the user API for index ops. I think.
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-09-09T22:31:37Z | 2014-10-06T14:28:34Z | 2014-09-11T15:44:57Z | CONTRIBUTOR | null | closes #8226
- ToDo
- [x] currently a raise `TypeError` (need a better message), possibly could deprecate and just warn
- [x] API example
Combining indexes via `+` now requires `.union()`
Diffing indexes via `-` now requires `.diff()`
These make the Indexes use setops (`&`,`|`,`^`) ONLY
numeric ops will work on... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8228 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8228/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8228/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8228/events | https://github.com/pandas-dev/pandas/issues/8228 | 42,365,421 | MDU6SXNzdWU0MjM2NTQyMQ== | 8,228 | read_csv() little problem with file_path containing accentuated characters | {
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"description... | closed | false | null | [] | null | 3 | 2014-09-10T00:11:53Z | 2015-10-20T22:41:31Z | 2015-10-20T22:41:31Z | NONE | null | Bonjour,
The pd.read_csv() function crash when common latin1 characters (ex: éàèç) occurs in the file path. We can use the following workaround in python 2.7 (Note: I use pandas 0.14 and from **future** import unicode_literals):
> > > import locale
> > > os_codec = locale.getdefaultlocale()[1] # this is 'cp1252' on... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8229 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8229/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8229/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8229/events | https://github.com/pandas-dev/pandas/issues/8229 | 42,368,102 | MDU6SXNzdWU0MjM2ODEwMg== | 8,229 | groupby objects can't be pickled | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Groupby"
},
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"default": false,
"description": "pandas objects ... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2014-09-10T01:00:38Z | 2018-07-06T22:31:32Z | 2018-07-06T22:31:27Z | NONE | null | From this discussion: https://groups.google.com/d/msg/pydata/AExc0ITFm94/Q5k5hcwZQFMJ
It appears that groupby object cannot be unpickled. The following code reproduces the problem for me:
```
import pandas as pd
import pickle
df = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9]])
gr = df.groupby(0)
b = pickle.dumps(gr)
gr2 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8230 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8230/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8230/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8230/events | https://github.com/pandas-dev/pandas/issues/8230 | 42,368,235 | MDU6SXNzdWU0MjM2ODIzNQ== | 8,230 | DataFrame.to_string ignores col_space when header=False | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-10T01:03:51Z | 2014-09-10T18:09:15Z | 2014-09-10T18:09:15Z | NONE | null | ```
import pandas as pd
df = pd.DataFrame([[0,1,2]], index=['Sum'], columns=['A','B','C'])
print(df.to_string(col_space=20))
print(df.to_string(col_space=20, header=False))
```
Output:
```
A B C
Sum 0 1 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8231 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8231/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8231/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8231/events | https://github.com/pandas-dev/pandas/pull/8231 | 42,371,470 | MDExOlB1bGxSZXF1ZXN0MjA5OTU3MDA= | 8,231 | Groupbydocs | {
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"id": 233160... | closed | false | null | [] | null | 10 | 2014-09-10T02:17:00Z | 2014-09-15T03:47:10Z | 2014-09-14T16:50:10Z | CONTRIBUTOR | null | Addresses #6944, by defining whitelisted methods in the `DataFrameGroupBy` and `SeriesGroupBy` class definitions. After this change, users of the whitelisted methods now get useful results from `help`, argument name completion, and auto-generated api docs. These `DataFrameGroupBy` and `SeriesGroupBy` methods are adde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8232/events | https://github.com/pandas-dev/pandas/pull/8232 | 42,376,362 | MDExOlB1bGxSZXF1ZXN0MjA5OTg1Mjk= | 8,232 | Fix to merge code from `_create_table_statement` and `get_schema` | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2014-09-10T04:16:17Z | 2014-09-15T21:50:33Z | 2014-09-15T21:43:01Z | CONTRIBUTOR | null | Closes #8097
Reorganized a lot of the SQL schema creation code. Now database table is not dropped / create when `PandasSQLTable` object is instantiated, but instead when `insert` method is called on it. This allow us to `get_schema` to generate `CREATE TABLE` code without actually creating the table.
(Includes updat... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8233 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8233/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8233/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8233/events | https://github.com/pandas-dev/pandas/pull/8233 | 42,394,501 | MDExOlB1bGxSZXF1ZXN0MjEwMDkwNjI= | 8,233 | BUG: DataFrame.to_string ignores col_space when header=False | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-09-10T09:37:14Z | 2014-09-10T18:09:15Z | 2014-09-10T18:09:05Z | CONTRIBUTOR | null | Fixes #8230. I think this is a pretty easy fix, `col_space` wasn't being used at all when `header` was `False` so just had to add the minimum width.
Results:
``` python
import pandas as pd
df = pd.DataFrame([[0,1,2]], index=['Sum'], columns=['A','B','C'])
print(df.to_string(col_space=20))
# Output:
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8234/events | https://github.com/pandas-dev/pandas/issues/8234 | 42,405,850 | MDU6SXNzdWU0MjQwNTg1MA== | 8,234 | DOC: some errors in the vis docs regarding multiple axes | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-09-10T12:10:57Z | 2015-02-12T14:22:59Z | 2015-02-12T14:22:59Z | MEMBER | null | Opening an issue to not forget this, see https://github.com/pydata/pandas/pull/7736#issuecomment-53970872
@sinhrks
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https://api.github.com/repos/pandas-dev/pandas/issues/8235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8235/events | https://github.com/pandas-dev/pandas/pull/8235 | 42,424,680 | MDExOlB1bGxSZXF1ZXN0MjEwMjgxNDI= | 8,235 | BUG: interpolate with no nans and limit | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-10T15:21:35Z | 2014-09-10T17:45:37Z | 2014-09-10T17:45:32Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7173
I was assuming that there would be at least one NaN when I was adjusting for the limits. Pretty simple fix.
Here's the intended behavior:
``` python
In [1]: s = pd.Series([1., 2, 3])
In [2]: s.interpolate()
Out[2]:
0 1
1 2
2 3
dtype: float64
In [3]: s.i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8236/events | https://github.com/pandas-dev/pandas/issues/8236 | 42,430,714 | MDU6SXNzdWU0MjQzMDcxNA== | 8,236 | CustomBusinessDay slow for increments <0 and >1 | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-10T16:13:30Z | 2014-10-04T13:37:55Z | 2014-10-04T13:37:55Z | CONTRIBUTOR | null | For rolling back or for moving forward more than one step, `DateOffset`s create a new instance setting `n` [accordingly](https://github.com/bjonen/pandas/blob/master/pandas/tseries/offsets.py#L312). The `CustomBusinessDay` constructor can be very slow, however, when a large list of `holidays` is passed. The holidays ar... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8237/events | https://github.com/pandas-dev/pandas/pull/8237 | 42,430,853 | MDExOlB1bGxSZXF1ZXN0MjEwMzE5NDA= | 8,237 | BUG: Bug in putting a PeriodIndex into a Series would convert to int64 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8238/events | https://github.com/pandas-dev/pandas/pull/8238 | 42,465,599 | MDExOlB1bGxSZXF1ZXN0MjEwNTMxODM= | 8,238 | BUG: normalize rolling_window() weights, remove scikits-timeseries dependency for testing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8239 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8239/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8239/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8239/events | https://github.com/pandas-dev/pandas/issues/8239 | 42,478,427 | MDU6SXNzdWU0MjQ3ODQyNw== | 8,239 | API: unified sorting | {
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... | 9 | 2014-09-11T00:49:44Z | 2015-08-02T23:01:30Z | 2015-08-02T23:01:29Z | CONTRIBUTOR | null | originally #5190
xref #9816
xref #3942
This issue is for creating a unified API to Series & DataFrame sorting methods. Panels are not addressed (yet) but a unified API should be easy to extend to them. Related are #2094, #5190, #6847, #7121, #2615. As discussion proceeds, this post will be edited.
For reference, t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8240/events | https://github.com/pandas-dev/pandas/pull/8240 | 42,483,813 | MDExOlB1bGxSZXF1ZXN0MjEwNjM3NDA= | 8,240 | BUG: boxplot fails when one column is all NaNs | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2014-09-11T02:50:00Z | 2014-09-27T03:17:58Z | 2014-09-17T19:32:26Z | CONTRIBUTOR | null | Fixes #8181. Currently the boxplot fails when trying to compute quantiles on an empty array, which numpy can't deal with. Works okay if we use an array with a single `np.nan` instead, the empty column is just left out of the plot as expected.
I guess that might be a bit of a hack, relying on how numpy deals with a si... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8241/events | https://github.com/pandas-dev/pandas/issues/8241 | 42,496,620 | MDU6SXNzdWU0MjQ5NjYyMA== | 8,241 | BUG: reindex on level of multi-index does not respect order of new index ? | {
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```
In [69]: s = pd.Series([0,1,2,3,4,5,6,7], index=pd.MultiIndex.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8242 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8242/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8242/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8242/events | https://github.com/pandas-dev/pandas/issues/8242 | 42,513,987 | MDU6SXNzdWU0MjUxMzk4Nw== | 8,242 | Multiple calls to .plot(): colors not correctly cycled | {
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Pandas 0.14.1
Matplotlib: 1.4.0
Reproduce:
Run:
``` python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
for i in range(20):
pd.Series(np.random.rand(20)).plot(label=i)
plt.legend()
```
Output:
![index](https://cloud.githubusercontent.com/assets/674200/4233081/13884414-39a6-11... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8243 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8243/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8243/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8243/events | https://github.com/pandas-dev/pandas/issues/8243 | 42,517,276 | MDU6SXNzdWU0MjUxNzI3Ng== | 8,243 | pandas.stats.ols.MovingOLS._calc_betas incompatibility issue with newer numpy | {
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pandas version 0.14.1
In pandas/stats/ols.py:821, we have:
mask = -np.isnan(betas).any(axis=1)
This causes problem with newer numpy which deprecated negative unary operator (-) on boolean arrays. I got this following error when running into this line:
DeprecationWarning: numpy boolean nega... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8244/events | https://github.com/pandas-dev/pandas/issues/8244 | 42,540,036 | MDU6SXNzdWU0MjU0MDAzNg== | 8,244 | ENH/VIS: Pass DataFrame column to size argument in df.scatter | {
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"followers_url": "https://api.github.com/users/jorisva... | 8 | 2014-09-11T15:53:59Z | 2018-07-06T22:30:32Z | 2018-07-06T22:30:27Z | CONTRIBUTOR | null | You can already kind of do this by passing in the numpy array
```
In [83]: df = pd.DataFrame(np.random.randn(100, 2))
In [84]: df['z'] = np.random.uniform(size=(100))
In [85]: df.plot(kind='scatter', x=0, y=1, s=df.z.values * 1000)
Out[85]: <matplotlib.axes._subplots.AxesSubplot at 0x11a81df60>
```
![p](https://clo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8245 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8245/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8245/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8245/events | https://github.com/pandas-dev/pandas/issues/8245 | 42,540,960 | MDU6SXNzdWU0MjU0MDk2MA== | 8,245 | DataFrame.index.asof() bug | {
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``` python
2 Inn: df = P.DataFrame(index=P.date_range('2014-9-6 1:30', '2014-9-9', freq='6H'))
3 Inn: df
3 Out:
2014-09-06 01:30:00
2014-09-06 07:30:00
2014-09-06 13:30:00
2014-09-06 19:30:00
2014-09-07 01:30:00
2014-09-07 07:30:00
2014-09-07 13:30:00
2014-09-07 19:30:00
2014-09-08 01:30:00
2014... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8246 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8246/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8246/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8246/events | https://github.com/pandas-dev/pandas/pull/8246 | 42,549,871 | MDExOlB1bGxSZXF1ZXN0MjExMDM3NTg= | 8,246 | BUG: DatetimeIndex.asof matches partial dates | {
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current master:
``` python
In [6]: idx.asof('2010-02')
Out[6]: Timestamp('2010-02-01 00:00:00')
```
fixed:
``` python
In [3]: idx.asof('2010-02')
Out[3]: Timestamp('2010-01-31 00:00:00', offset='M')
```
Will try to do a test_perf on this later. I've been working ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8247 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8247/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8247/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8247/events | https://github.com/pandas-dev/pandas/pull/8247 | 42,563,043 | MDExOlB1bGxSZXF1ZXN0MjExMTIyNTk= | 8,247 | DOC: Correct Series.nonzero docstring | {
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matches `numpy.nonzero`.
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https://api.github.com/repos/pandas-dev/pandas/issues/8248 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8248/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8248/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8248/events | https://github.com/pandas-dev/pandas/issues/8248 | 42,577,365 | MDU6SXNzdWU0MjU3NzM2NQ== | 8,248 | Incorrect unit conversion in `to_timedelta` when unit='h' or unit='m' | {
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Using Pandas 0.14.0: `pd.to_timedelta` seems to ignore the units when `unit` is `h` or `m`:
```
In [263]: pd.to_timedelta(16, unit='h')
Out[263]: numpy.timedelta64(16,'ns')
In [264]: pd.to_timedelta(16, unit='m')
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https://api.github.com/repos/pandas-dev/pandas/issues/8249 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8249/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8249/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8249/events | https://github.com/pandas-dev/pandas/pull/8249 | 42,585,133 | MDExOlB1bGxSZXF1ZXN0MjExMjY1NDQ= | 8,249 | BUG: plot methods modified rcParams | {
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Just made a copy of the rcParams when we get it.
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] | closed | false | null | [] | null | 1 | 2014-09-12T01:17:53Z | 2014-09-12T12:00:55Z | 2014-09-12T12:00:55Z | NONE | null | I have written the code
import pandas as pd
import numpy as np
from scipy.optimize import minimize
pd.set_option('display.mpl_style', 'default')
"""Read the input file into the dataframe""" """T1 file contains the decision variables, corresponding investments and their upper and lower bounds"""
df = pd.DataFrame.from... | {
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```
import pandas as pd
from pandas.util.testing import makePanel
#
# Demo A: We want to fill along axis 0
#
p = makePanel().transpose(1,0,2)
p.iloc[10:20,0,0] = np.nan
# Demonstrate we have a gap
p.iloc[:,0,0].plot()
# Demonstrate th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8253/events | https://github.com/pandas-dev/pandas/pull/8253 | 42,613,699 | MDExOlB1bGxSZXF1ZXN0MjExNDMyMzM= | 8,253 | PERF: Improve performance CustmBusinessDay | {
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`CustomBusinessDay` is now fast with holiday calendar and incrementing in different ways.
import pandas as pd
import numpy as np
import datetime as dt
date = pd.Timestamp('20120101')
cbday = pd.offsets.CustomBusinessDay()
date + cbday
%timeit date + cbday
10000 loops, best of 3: 17.2 µs per loop
hdays... | {
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"followers_url": "https://api.github.com/users/jr... | 17 | 2014-09-12T13:32:57Z | 2016-09-01T08:01:28Z | 2016-08-29T12:42:43Z | CONTRIBUTOR | null | see https://github.com/pydata/pandas/pull/8184
related #8185
`Index.to_datetime` currently exists but really no need for this as `pd.to_datetime` subsumes this functionaility (and is more general). and just pollutes the interface.
so remove:
- [x] `Timestamp.to_datetime`
- [x] `DatetimeIndex.to_datetime` #14096
- [x... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8255/events | https://github.com/pandas-dev/pandas/issues/8255 | 42,655,736 | MDU6SXNzdWU0MjY1NTczNg== | 8,255 | AsIs class in R dataframe conversion prevents easy plotting w/ ggplot2 | {
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] | closed | false | null | [] | null | 1 | 2014-09-12T18:14:53Z | 2015-03-08T14:40:09Z | 2015-03-08T14:40:09Z | NONE | null | When using pandas.rpy.common.convert_to_r_dataframe to convert pandas DataFrame --> R data.frame, columns get assigned two classes: AsIs, some data type
ggplot2 sees AsIs and refuses to deal with the data type properly. It would be nice to have the AsIs automatically stripped like here:
http://stackoverflow.com/quest... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8256 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8256/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8256/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8256/events | https://github.com/pandas-dev/pandas/issues/8256 | 42,659,984 | MDU6SXNzdWU0MjY1OTk4NA== | 8,256 | hist(by=) breaks when the output does not fill all rows and columns | {
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import pandas as pd
pd.Series(randn(1000)).hist(by=randint(0, 6, 1000), sharex=True, sharey=True)
pd.Series(randn(1000)).hist(by=randint(0, 5, 1000), sharex=True, sharey=True)
![2](https://cloud.githubus... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8257 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8257/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8257/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8257/events | https://github.com/pandas-dev/pandas/issues/8257 | 42,664,894 | MDU6SXNzdWU0MjY2NDg5NA== | 8,257 | compare two series objects ignores index | {
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2014-09-12T19:58:50Z | 2016-11-11T10:33:49Z | 2016-11-11T10:33:42Z | NONE | null | It seems that when comparing two Series objects, index is ignored, here is an trivial example
```
In [177]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-0.bpo.4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LA... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8258/events | https://github.com/pandas-dev/pandas/issues/8258 | 42,666,469 | MDU6SXNzdWU0MjY2NjQ2OQ== | 8,258 | BUG: alignment bug when setting a subset of rows and attempting to switch columns | {
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http://stackoverflow.com/questions/25811529/setting-values-on-a-subset-of-rows-indexing-boolean-setting/25812825#25812825
Jeff suggested posting here also.
``` python
df = DataFrame({'L': ['left', 'right', 'left', 'right'],
'R': ['right', 'left', 'right', 'left']... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8259/events | https://github.com/pandas-dev/pandas/issues/8259 | 42,677,843 | MDU6SXNzdWU0MjY3Nzg0Mw== | 8,259 | datetime not properly converted from Series; timezone dropped | {
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To reproduce:
```
In [83]: import pytz
In [85]: d = datetime.datetime(2014, 9, 3, 2, 40, 16, tzinfo=pytz.utc)
In [88]: pd.to_datetime(pd.Series([d]))[0]
Out[88]: Timestamp('2014-09-03 02:40:16', tz=None)
In [89]: pd.to_datetime(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8260/events | https://github.com/pandas-dev/pandas/issues/8260 | 42,680,629 | MDU6SXNzdWU0MjY4MDYyOQ== | 8,260 | INT: support DatetimeBlock with timezones | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-09-12T23:43:59Z | 2015-09-05T16:17:23Z | 2015-09-05T16:17:23Z | CONTRIBUTOR | null | to allow real support / conversions of timezone aware Series.
xref #9560; `DatetimeIndex.to_series()` can be changed to preserve the tz always
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https://api.github.com/repos/pandas-dev/pandas/issues/8261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8261/events | https://github.com/pandas-dev/pandas/issues/8261 | 42,682,358 | MDU6SXNzdWU0MjY4MjM1OA== | 8,261 | .ix slicing doesn't work on a dataframe obtained by groupby.get_group() | {
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] | closed | false | null | [] | null | 24 | 2014-09-13T00:24:41Z | 2014-09-13T14:14:47Z | 2014-09-13T02:32:23Z | CONTRIBUTOR | null | I have a dataframe with a column "actor"; I created a groupby object as such:
a = df.groupby('actor')
Then I create a new DataFrame as follows:
b = a.get_group(a.groups.keys()[0])
Now, b.ix[0,0] gives an error:
KeyError: 0L
I can still use .ix to slice by index and column, but not by position.
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https://api.github.com/repos/pandas-dev/pandas/issues/8262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8262/events | https://github.com/pandas-dev/pandas/pull/8262 | 42,696,771 | MDExOlB1bGxSZXF1ZXN0MjExOTMzNTE= | 8,262 | Clarification to .ix functionality | {
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in the case where the corresponding dimension has an
integer index.
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https://api.github.com/repos/pandas-dev/pandas/issues/8263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8263/events | https://github.com/pandas-dev/pandas/issues/8263 | 42,702,460 | MDU6SXNzdWU0MjcwMjQ2MA== | 8,263 | ENH: set multi-index names as NamedTuples | {
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s1=PD.Series((1,4,9,16), name=dict(a=1,b=2))
s2=s1*2
s2.name=dict(c=5,d=7)
s1
0 1
1 4
2 9
3 16
Name: {'a': 1, 'b': 2}, dtype: int32
df = PD.concat((s1,s2), axis=1) # this should raise ValueError as the resulting df is broken
df.shape # ok, this works...
(4, 2)
df # But this doesn't.
<... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8264/events | https://github.com/pandas-dev/pandas/pull/8264 | 42,708,207 | MDExOlB1bGxSZXF1ZXN0MjExOTg1MTM= | 8,264 | BUG: Bug in inference in a MultiIndex with datetime.date inputs (GH7888) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8265/events | https://github.com/pandas-dev/pandas/issues/8265 | 42,710,756 | MDU6SXNzdWU0MjcxMDc1Ng== | 8,265 | HDF5 index corruption | {
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... | closed | false | null | [] | null | 14 | 2014-09-14T02:27:16Z | 2015-04-18T19:00:14Z | 2015-04-18T19:00:14Z | CONTRIBUTOR | null | I generated a multindexed DataFrame and wrote it to hdf5 using to_hdf. It uses zlib level 5 compression. The file was written all at once. The file is located here: https://www.dropbox.com/s/122q55g5ubcf4fl/indexIssue.h5?dl=0
The below methods should be identical but the former select with a where clause has 2892 r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8266 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8266/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8266/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8266/events | https://github.com/pandas-dev/pandas/pull/8266 | 42,713,848 | MDExOlB1bGxSZXF1ZXN0MjEyMDA4NjE= | 8,266 | DOC: Add notes explaining params is db driver dep. GH7573 | {
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] | closed | false | null | [] | null | 3 | 2014-09-14T05:49:51Z | 2014-10-08T05:31:22Z | 2014-09-16T06:14:26Z | CONTRIBUTOR | null | This is my first PR. It's just an update to two doc strings.
Closes #7573
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https://api.github.com/repos/pandas-dev/pandas/issues/8268 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8268/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8268/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8268/events | https://github.com/pandas-dev/pandas/pull/8268 | 42,721,511 | MDExOlB1bGxSZXF1ZXN0MjEyMDQzOTg= | 8,268 | TST: tests for reduction of series with nan timedelta (GH7661) | {
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just tests for coverage
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on master:
```
In [3]: np.random.seed(2718281)
In [4]: xs = np.random.rand(10)
In [5]: pd.stats.moments.rolling_skew(xs, 4) - pd.stats.moments.roll... | {
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produce an exactly zero value of the variance when all entries are
identical. Related to the discussion in #7900
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"color": "006b7... | closed | false | null | [] | null | 4 | 2014-09-15T13:15:38Z | 2015-10-20T22:41:46Z | 2015-10-20T22:41:46Z | NONE | null | The error is:
list index out of range
Exception Location: /home/pietro/lod4statenv/local/lib/python2.7/site-packages/pandas/core/format.py in _write_body, line 923
In order to fix it I have replaced line 922 from:
for i in range(len(self.frame)):
to:
for i in range(min(len(self.frame), self.max_rows)):
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https://api.github.com/repos/pandas-dev/pandas/issues/8274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8274/events | https://github.com/pandas-dev/pandas/issues/8274 | 42,785,484 | MDU6SXNzdWU0Mjc4NTQ4NA== | 8,274 | to_hdf:[unicode] is not implemented as a table column | {
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I want to store data use DataFrame.to_hdf() function.
when i use df600000.to_hdf(path_or_buf='H:\store.h5',key='SH600000',format='f',mode='r+'),it works good,since i change format='t' will raise TypeError,why?
detail as followed:
## In [55]:df600000.to_hdf(path_or_buf='H:\store.h5',key=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8275 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8275/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8275/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8275/events | https://github.com/pandas-dev/pandas/pull/8275 | 42,803,139 | MDExOlB1bGxSZXF1ZXN0MjEyNTA2NTc= | 8,275 | BUG: rolling_apply(..., center=True) should not append NaNs | {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2014-09-15T18:25:33Z | 2014-09-17T13:24:32Z | 2014-09-17T13:07:02Z | CONTRIBUTOR | null | Closes #8269.
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https://api.github.com/repos/pandas-dev/pandas/issues/8276 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8276/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8276/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8276/events | https://github.com/pandas-dev/pandas/issues/8276 | 42,809,108 | MDU6SXNzdWU0MjgwOTEwOA== | 8,276 | SparseDataFrame.isnull raises an error | {
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"closed_at": "2016-10-03T08:52:13Z",
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-15T19:25:56Z | 2016-08-31T07:57:34Z | 2016-08-31T07:57:34Z | NONE | null | pandas version 0.14.0:
SparseDataFrame.isnull method raises an error
```
>>> import pandas as pd
>>> from numpy.random import rand
>>> a = pd.SparseDataFrame(rand(5,5))
>>> a.isnull()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/isofer/anaconda/lib/python2.7/site-packages/pan... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8277/events | https://github.com/pandas-dev/pandas/issues/8277 | 42,822,946 | MDU6SXNzdWU0MjgyMjk0Ng== | 8,277 | SQL: use of connection context manager not supported by mysql.connector | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-15T21:49:48Z | 2014-09-17T09:06:27Z | 2014-09-17T09:06:27Z | MEMBER | null | Coming from discussion in #8232, current master breaks with `mysql.connector` as the MySQL driver in the fallback modus (deprecated) due to the use of context managers.
This was introduced in #8062, to ensure that the different chunks would be inserted in one transaction (when using the `chunksize` argument).
The con... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8278 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8278/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8278/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8278/events | https://github.com/pandas-dev/pandas/pull/8278 | 42,837,851 | MDExOlB1bGxSZXF1ZXN0MjEyNzI3NDQ= | 8,278 | Eliminating contextmanager based transaction-handling | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2014-09-16T02:04:25Z | 2014-09-17T09:06:48Z | 2014-09-17T09:06:27Z | CONTRIBUTOR | null | To close #8277
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https://api.github.com/repos/pandas-dev/pandas/issues/8279 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8279/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8279/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8279/events | https://github.com/pandas-dev/pandas/issues/8279 | 42,840,160 | MDU6SXNzdWU0Mjg0MDE2MA== | 8,279 | API: add ddof=1 argument to expanding/rolling_cov() | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-16T03:01:14Z | 2014-09-17T18:03:54Z | 2014-09-17T18:03:54Z | CONTRIBUTOR | null | I think `expanding/rolling_cov()` should support a `ddof` argument, the way `expanding/rolling_std/var()` do (though the latter is not documented; see #8064). So long as `ddof` defaults to `1`, it will be backwards compatible, so I don't see any downsides. This is very easy/straightforward.
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https://api.github.com/repos/pandas-dev/pandas/issues/8280 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8280/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8280/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8280/events | https://github.com/pandas-dev/pandas/pull/8280 | 42,840,191 | MDExOlB1bGxSZXF1ZXN0MjEyNzQxMTQ= | 8,280 | API: add ddof to expanding/rolling_cov() | {
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closes #8064
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https://api.github.com/repos/pandas-dev/pandas/issues/8281 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8281/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8281/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8281/events | https://github.com/pandas-dev/pandas/pull/8281 | 42,855,777 | MDExOlB1bGxSZXF1ZXN0MjEyODI4NjM= | 8,281 | ENH groupby: more user-friendly error message (GH7511) | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-16T08:24:32Z | 2014-10-02T02:35:07Z | 2014-09-16T14:43:54Z | CONTRIBUTOR | null | better error message for groupy on duplicated columns name. (Followup for branch GH8210 (Bug GH7511)). E.g.
```
ValueError: Grouper for 'A' not 1-dimensional
ValueError: Grouper for '<class '__main__.gr'>' not 1-dimensional
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8282/events | https://github.com/pandas-dev/pandas/pull/8282 | 42,894,180 | MDExOlB1bGxSZXF1ZXN0MjEzMDUzNDk= | 8,282 | BUG: make sure that the multi-index is lex-sorted before passing to _lexsort_indexer (GH8017) | {
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closes #8017
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xref #8239 (this issue won't touch the API per se)
expand extend: `core/generic.py/NDFrame.sort_index`: https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L1606
with the frame version.
https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L2689
needs generic docs (e.g. look ho... | {
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``` python
import pandas as pd
data = {'ints':pd.Series([1,2,3], index=index), 'Timestamps': pd.Series([pd.Timestamp('2014-1-1 12:00', tz='UTC'), pd.Timestamp('20... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-09-16T22:57:04Z | 2015-03-14T01:31:43Z | 2015-03-14T01:31:43Z | CONTRIBUTOR | null | Is it accidental that the Series and DataFrame constructors accept a single number for `data`, but the Panel and Panel4D constructors do not?
```
In [182]: from pandas import Series, DataFrame, Panel, Panel4D
In [183]: Series(1., index=range(2))
Out[183]:
0 1
1 1
dtype: float64
In [184]: DataFrame(1., index=ra... | {
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I also noticed that line 965 (in master) has a list comprehension that iterates through a non-instantia... | {
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```
In [1]: df = pd.DataFrame(np.random.randn(10, 4))
In [3]: df.to_hdf('test.h5', 'df')
In [4]: with pd.get_store('test.h5') as store:
...: df_ = store.select('df', start=0, stop=2)
...:
In [5]: df_
Out[5]:
0 1 2 3
0 1.504814 0.209394 -0.3... | {
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I'll squash it all into one commit when I think I'm done and ready for final review. Feel free to make suggestions as I go, but consider it all a draft until I sq... | {
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... | 1 | 2014-09-17T11:19:57Z | 2015-05-09T16:00:51Z | 2015-05-09T16:00:51Z | CONTRIBUTOR | null | This addresses #8273, but it doesn't actually fully fix the issue, since the same bug still occurs when we have a MultiIndex. I've got a fix for the single-index case, and I'm not sure if I have time to decipher what's happening with hierarchical indexes right now. I'll open a new issue for the MultiIndex case if that'... | {
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``` python
In [1]: pd.__version__
Out[1]: '0.14.1-388-g54678dd'
In [2]: sp_series = pd.Series([0,0,1]).to_sparse(fill_value=0)
In [3]: sp_series.dtype
Out[3]: dtype('int64')
In [4]: sp_series[::-1].dtype
Out[4]: dtype('float64')
In [5]: sp_series[::2].dtype
Out[5]: dtype('float64')
In [6]: sp_se... | {
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CustomBusinessDay is now fast with holiday calendar and incrementing in different ways. `busdaycalendar` is cached in self.kwds.
```
In [15]:
date = pd.Timestamp('20120101')
cbday = pd.offsets.CustomBusinessDay()
date + cbday
%timeit date + cbday
100000 loops, best of 3: 14.9 µs per loop
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``` python
In [1]: import pandas as pd
In [2]: pd.DataFrame.from_records([(0, 1), (0, 1), (0, 1)], index=[0, 1],
...: columns=['A', 'B'])
--------... | {
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e.g.
Instead of
`pd.concat([df,DataFrame([[1,2,3,4]],columns=list('ABCD'),index=df.index)],axis=1)`
allow as a convience feature
`df.append(DataFrame([[1,2,3,4]],columns=list('ABCD'),inde... | {
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```
nosetests test_graphics:TestDataFramePlots.test_boxplot test_graphics:TestDataFramePlots.test_boxplot_vertical
FF
======================================================================
FAIL: test_boxplot (test_graphics.TestDataFramePlots)
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``` python
In [1]: df = pd.DataFrame(np.random.randn(20, 3))
In [2]: df.plot(subplots=True, layout=(-1, 2))
Out[2]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116b134e0>,
<matplotlib.axes._subplots.AxesSubplot object at... | {
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closes #7729
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"followers_url": "https://api.github.com/users/jr... | 10 | 2014-09-17T19:42:01Z | 2014-09-23T15:39:55Z | 2014-09-23T15:35:39Z | CONTRIBUTOR | null | This PR suppresses expected warnings in test_moments.py, which are otherwise distracting.
Note that the changes are not nearly as extensive as they look. I simply indented blocks of code under newly added `with warnings.catch_warnings():`.
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https://api.github.com/repos/pandas-dev/pandas/issues/8300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8300/events | https://github.com/pandas-dev/pandas/issues/8300 | 43,054,641 | MDU6SXNzdWU0MzA1NDY0MQ== | 8,300 | BUG: columns misaligned in repr when having >10 columns with integer index | {
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"f... | 11 | 2014-09-17T19:49:11Z | 2019-10-21T19:34:16Z | null | MEMBER | null | On master, with integer index, with more than 10 columns, the numbers of one char are misaligned:
```
In [37]: df = pd.DataFrame(np.random.randn(10,20))
In [38]: pd.options.display.max_columns = 4
In [39]: df
Out[39]:
0 1 ... 18 19
0 -0.112066 -0.315377 ... 1.579197 -0.71... | {
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