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... | 2 | 2015-02-17T03:31:11Z | 2021-04-12T05:19:04Z | null | NONE | null | This is a repost of #9359 with some new fancy code that handles lots of different kinds of edge cases. Please take a look at the code:
https://gist.github.com/cloudformdesign/13278001b1a0b0cde647
Basically this allows automatic loading of nested dictionaries, whether those dictionaries are a "list of dictionaries" or... | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-02-17T04:10:15Z | 2017-02-17T13:50:19Z | 2017-02-17T13:50:19Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/28541302/pandas-to-csv-with-quoting-3-quote-nonnumeric-doesnt-work
remove the actual numbers and show the usage of the csv module constants
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I can break any of these commits into separate PRs if needed.
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``` python
import pandas as pd
df_with_ts = pd.DataFrame(data=[['BL', pd.Timestamp('2015-02-01')], ['TH', pd.Timestamp('2015-02-02')]], columns=['item', 'date'])
res = df_with_ts.groupby(['date']).apply(lambda x: pd.Series(x['item'].unique()[0]))
In []: res ... | {
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... | 1 | 2015-02-17T18:29:12Z | 2019-12-23T04:38:16Z | null | NONE | null | When using apply on a series, pandas is returning a series of int64 even though the target values were uint64.
``` python
import numpy as np
import pandas as pd
uintDF = pd.DataFrame(np.uint64([1,2,3,4,5]),columns=['Numbers'])
indexDF = pd.DataFrame([2,3,2,1,2],columns=['Indices'])
def retrieve(targetRow,targetDF):... | {
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import pandas as pd
from datetime import datetime
pd.datetools.thisYearBegin(datetime(2015,2,1))
```
This returns Timestamp('2016-01-01 00:00:00'), but should return 201**5**-01-01. Should thisYearBegin() just be removed? I don't think it's documented.
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... | 1 | 2015-02-18T03:24:55Z | 2021-04-12T05:21:10Z | null | MEMBER | null | Recently, I've been working on adding a 'nearest' method to reindexing: https://github.com/pydata/pandas/pull/9258
It occurs to me that we could easily extend reindexing/get_indexer methods to work with unordered indexes if we were willing to do a sort operation on the index if necessary. This would probably entail sa... | {
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Cython 0.14+ comes with `cygdb`, but `setup.py` must use `cythonize` on extensions in order to use it. I will implement this if I have a need for it, but PRs welcome :grin:.
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https://api.github.com/repos/pandas-dev/pandas/issues/9512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9512/events | https://github.com/pandas-dev/pandas/issues/9512 | 58,109,953 | MDU6SXNzdWU1ODEwOTk1Mw== | 9,512 | Non-monotonic-increasing DatetimeIndex claims not to __contain__ duplicate entries | {
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] | closed | false | null | [] | null | 4 | 2015-02-18T18:47:30Z | 2015-02-20T00:04:45Z | 2015-02-20T00:04:45Z | CONTRIBUTOR | null | This was fun to debug.
``` python
In [1]: import pandas as pd
In [2]: 0 in pd.Int64Index([0, 0, 1])
Out[2]: True
In [3]: 0 in pd.Int64Index([0, 1, 0])
Out[3]: True
In [4]: 0 in pd.Int64Index([0, 0, -1])
Out[4]: True
In [5]: pd.Timestamp(0) in pd.DatetimeIndex([0, 1, -1])
Out[5]: True
In [6]: pd.Timestamp(0) in pd... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9513/events | https://github.com/pandas-dev/pandas/issues/9513 | 58,122,153 | MDU6SXNzdWU1ODEyMjE1Mw== | 9,513 | pd.NaT.date() returns datetime.date(1, 255, 255) | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2015-02-18T20:19:01Z | 2015-07-17T17:21:03Z | 2015-07-17T17:21:03Z | CONTRIBUTOR | null | Hello,
`pd.NaT.date()` returns `datetime.date(1, 255, 255)`
that's an odd results
I was expecting `None` (or something else) because that's really strange to have
date of year "1" in an Excel file
Kind regards
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https://api.github.com/repos/pandas-dev/pandas/issues/9514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9514/events | https://github.com/pandas-dev/pandas/issues/9514 | 58,136,265 | MDU6SXNzdWU1ODEzNjI2NQ== | 9,514 | BUG: DataFrame.unstack() does not properly sort list of levels | {
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... | 6 | 2015-02-18T22:06:57Z | 2020-07-19T18:56:44Z | null | CONTRIBUTOR | null | In 0.15.2 (and I believe this remains the case), the docstring for `DataFrame.unstack()` states `The level involved will automatically get sorted.`. This is not necessarily the case when `level` is a list of levels.
```
In [40]: df = pd.DataFrame(np.arange(8).reshape((4, 2)),
index=pd.MultiI... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9515/events | https://github.com/pandas-dev/pandas/pull/9515 | 58,136,754 | MDExOlB1bGxSZXF1ZXN0Mjk1Njg0NTE= | 9,515 | FIX: Fix some instances where idx[0] not in idx | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-18T22:10:59Z | 2015-02-20T16:11:04Z | 2015-02-20T00:04:45Z | CONTRIBUTOR | null | `DatetimeIndex.__contains__` and `TimedeltaIndex.__contains__` were failing to see duplicated elements in some circumstances.
Fixes #9512
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https://api.github.com/repos/pandas-dev/pandas/issues/9516 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9516/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9516/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9516/events | https://github.com/pandas-dev/pandas/issues/9516 | 58,142,485 | MDU6SXNzdWU1ODE0MjQ4NQ== | 9,516 | DateTimeIndex appending with loc inconsistency in handling numpy datetime64 | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-02-18T22:55:23Z | 2015-02-20T00:25:06Z | 2015-02-19T13:19:45Z | CONTRIBUTOR | null | This is in Pandas `0.15.2` (but I also tried it on '0.15.2-148-g484f668'):
``` python
df = pd.DataFrame()
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100
df.loc[np.datetime64(datetime.datetime.now()),'one'] = 100
```
This is the output:
``` text
one
2015-02-18 14:50:05.60651... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9517 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9517/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9517/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9517/events | https://github.com/pandas-dev/pandas/pull/9517 | 58,161,648 | MDExOlB1bGxSZXF1ZXN0Mjk1ODM2NTg= | 9,517 | BUG: multiple level unstack with nulls | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-19T02:50:53Z | 2015-03-06T02:52:19Z | 2015-03-05T23:25:34Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9497
```
>>> mi
jim joe
1st 2nd 3rd
1 2014-02-01 -1 days 100 -20.87
2 NaT NaT 101 5.76
1 2014-02-03 1 days 102 -4.94
2 NaT 2 days 103 -0.79
1 2014-02-05 NaT 104 -12.51
2 2014-02-06 4 days 105... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9518 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9518/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9518/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9518/events | https://github.com/pandas-dev/pandas/issues/9518 | 58,168,500 | MDU6SXNzdWU1ODE2ODUwMA== | 9,518 | BLD: pandas should generate PEP 440 compliant dev versions | {
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`UserWarning: The version specified ('0.15.2-224-geadfd92') is an invalid version, this may not work as expected with newer versions of setuptools, pip, and PyPI. Please see PEP 440 for more details.`
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https://api.github.com/repos/pandas-dev/pandas/issues/9519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9519/events | https://github.com/pandas-dev/pandas/issues/9519 | 58,176,167 | MDU6SXNzdWU1ODE3NjE2Nw== | 9,519 | API: Please make ".loc" return type depend on index, not on specific labels | {
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... | 10 | 2015-02-19T07:28:54Z | 2021-04-12T05:25:06Z | null | MEMBER | null | I already mentioned this in https://github.com/pydata/pandas/issues/9466 but I think it deserves its own bug report:
```
In [2]: s = pd.Series([1, 2, 3], index=(1,1,2))
In [3]: s
Out[3]:
1 1
1 2
2 3
dtype: int64
In [4]: s.loc[1]
Out[4]:
1 1
1 2
dtype: int64
In [5]: type(s.loc[1])
Out[5]: pandas.cor... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9520 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9520/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9520/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9520/events | https://github.com/pandas-dev/pandas/pull/9520 | 58,187,381 | MDExOlB1bGxSZXF1ZXN0Mjk1OTgzNDE= | 9,520 | Update tslib.pyx | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9521/events | https://github.com/pandas-dev/pandas/issues/9521 | 58,200,517 | MDU6SXNzdWU1ODIwMDUxNw== | 9,521 | API/PERF/BUG: infer dtypes when enlarging | {
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... | 1 | 2015-02-19T12:12:12Z | 2015-02-19T12:13:14Z | 2015-02-19T12:13:14Z | CONTRIBUTOR | null | So this can be fixed by inferring after the set. We need to do this because we first set the value to a null-type (nan/NaT), then set the value. This works fine for datetime/timedelta/floats/strings, but not for integers which get set as `float`.
However, this _can_ be an expensive operation as potentially the entire ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9522/events | https://github.com/pandas-dev/pandas/pull/9522 | 58,201,625 | MDExOlB1bGxSZXF1ZXN0Mjk2MDcwNzk= | 9,522 | BUG: Bug in .loc partial setting with a np.datetime64 (GH9516) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9523/events | https://github.com/pandas-dev/pandas/pull/9523 | 58,238,761 | MDExOlB1bGxSZXF1ZXN0Mjk2MzAwODE= | 9,523 | Update tslib.pyx | {
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... | 2 | 2015-02-19T17:16:47Z | 2015-05-09T16:01:43Z | 2015-05-09T16:01:43Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9513
pd.NaT.date() was returning datetime.date(1, 255, 255)
it raises now a ValueError
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https://api.github.com/repos/pandas-dev/pandas/issues/9524 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9524/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9524/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9524/events | https://github.com/pandas-dev/pandas/issues/9524 | 58,241,545 | MDU6SXNzdWU1ODI0MTU0NQ== | 9,524 | Bad x-axis dates when plotting two time-series with different indexes (start, end, and freq) | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-02-19T17:36:57Z | 2015-08-16T13:50:46Z | 2015-08-16T13:50:01Z | NONE | null | The two DataFrames are from 2014, but from the figure you can see that pandas throws one of them to the year 2050!
I am using pandas 0.15.2 and the notebook below describes the problem and show a few work... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9525/events | https://github.com/pandas-dev/pandas/pull/9525 | 58,268,753 | MDExOlB1bGxSZXF1ZXN0Mjk2NDgyMzA= | 9,525 | Squashed version of #9515. | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-19T20:58:07Z | 2015-02-20T00:04:47Z | 2015-02-20T00:04:45Z | MEMBER | null | Fixes #9512
Closes #9515
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https://api.github.com/repos/pandas-dev/pandas/issues/9526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9526/events | https://github.com/pandas-dev/pandas/pull/9526 | 58,273,425 | MDExOlB1bGxSZXF1ZXN0Mjk2NTExNTE= | 9,526 | BUG: declare and use self.unique_check | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9527/events | https://github.com/pandas-dev/pandas/issues/9527 | 58,274,272 | MDU6SXNzdWU1ODI3NDI3Mg== | 9,527 | BUG: Resample downsampling return NaN | {
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"i... | closed | false | null | [] | null | 2 | 2015-02-19T21:41:41Z | 2015-02-19T22:50:53Z | 2015-02-19T22:16:02Z | NONE | null | Pandas resample bugs when trying to resample a time serie with same size splits :
I have a time serie of **size 10**:
```
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
```
`print(ts)`
```
2013-01-01 00:00:00 -1.811999
2013-01-01 00:01:00 -0.890837
2013-01... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9528 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9528/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9528/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9528/events | https://github.com/pandas-dev/pandas/issues/9528 | 58,283,934 | MDU6SXNzdWU1ODI4MzkzNA== | 9,528 | BUG: Resample upsampling return NaNs | {
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"i... | open | false | null | [] | null | 6 | 2015-02-19T22:57:28Z | 2021-04-12T05:33:19Z | null | NONE | null | Pandas resample bugs when upsampling a time serie with same size splits :
For instance, I have a time serie of **size 10**:
```
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
```
`print(ts)`
```
2013-01-01 00:00:00 -1.811999
2013-01-01 00:01:00 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9529/events | https://github.com/pandas-dev/pandas/pull/9529 | 58,304,445 | MDExOlB1bGxSZXF1ZXN0Mjk2Njk4NTY= | 9,529 | Allows for more options when getting data from dictionaries. | {
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... | 9 | 2015-02-20T02:57:32Z | 2015-05-09T16:02:27Z | 2015-05-09T16:02:27Z | NONE | null | Fixes #9502
See the [cloudtb tests/test_pandas.py](https://github.com/cloudformdesign/cloudtb/blob/f/pandas/tests/test_pandas.py) for more examples of use.
Once it is approved to go forward, I will add tests to show more about how
it can be used and make sure that every edge case if functional.
Has no effect on uni... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9530/events | https://github.com/pandas-dev/pandas/issues/9530 | 58,311,555 | MDU6SXNzdWU1ODMxMTU1NQ== | 9,530 | Allow for near misses when doing alignment with Float64Index? | {
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"description": "Related ... | open | false | null | [] | null | 3 | 2015-02-20T05:21:40Z | 2021-04-12T05:34:49Z | null | MEMBER | null | The "exact match" requirement for index alignment doesn't work very well when using Float64Index if the index values may result from computations.
Here's an example of how the current behavior results in a poor user experience:
```
In [22]: s1 = pd.Series(range(3), np.arange(3.0))
In [23]: s2 = pd.Series(range(3), s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9531/events | https://github.com/pandas-dev/pandas/issues/9531 | 58,317,035 | MDU6SXNzdWU1ODMxNzAzNQ== | 9,531 | float_format string error | {
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"color": "00980... | closed | false | null | [] | null | 1 | 2015-02-20T06:59:43Z | 2015-02-20T13:06:03Z | 2015-02-20T13:06:03Z | NONE | null | The float_format string works for single values i.e. float_format='%.1f', but it does not appear to work for a formatted string of columns. For example, If I had four columns with increasing significant digits, separated with tabs:
```
fmt_string='%.1f\t%.2f\t%.3f\t%.4f'
```
Then for some DataFrame X, I should be abl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9532/events | https://github.com/pandas-dev/pandas/issues/9532 | 58,346,305 | MDU6SXNzdWU1ODM0NjMwNQ== | 9,532 | Any plans to support PyPy? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9533/events | https://github.com/pandas-dev/pandas/issues/9533 | 58,515,992 | MDU6SXNzdWU1ODUxNTk5Mg== | 9,533 | ENH/API: DataFrame.unstack() support for dropna and sequentially | {
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... | 0 | 2015-02-22T20:03:28Z | 2016-08-11T22:31:37Z | null | CONTRIBUTOR | null | Add `dropna` and `sequentially` parameters to `DataFrame.unstack()`, to match `DataFrame.stack()`. For backwards compatibility, these should default to `dropna=False` and `sequentially=False` -- which are different from the defaults for `DataFrame.stack()`.
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https://api.github.com/repos/pandas-dev/pandas/issues/9534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9534/events | https://github.com/pandas-dev/pandas/issues/9534 | 58,551,129 | MDU6SXNzdWU1ODU1MTEyOQ== | 9,534 | pivot_table(aggfunc="count") with category column raise "ValueError: Cannot convert NA to integer" | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-23T07:27:33Z | 2016-12-06T21:34:18Z | 2016-12-06T19:10:40Z | NONE | null | Here is the test code, that return the right table:
```
import pandas as pd
data = {"C1":["A", "B", "C", "C"], "C2":["a", "a", "b", "b"], "V":[1, 2, 3, 4]}
df = pd.DataFrame(data)
df.pivot_table("V", index="C1", columns="C2", aggfunc="count")
```
when convert column to category, ValueError is raised:
```
df2 = df.co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9535 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9535/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9535/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9535/events | https://github.com/pandas-dev/pandas/issues/9535 | 58,595,706 | MDU6SXNzdWU1ODU5NTcwNg== | 9,535 | read_csv(<file>, nrows=x) raises an StopIteration exception when given file doesn't contain any newlines | {
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Code:
`import pandas`
`pandas.read_csv(one_line_file, nrows=0)`
## INSTALLED VERSIONS
commit: N... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9536/events | https://github.com/pandas-dev/pandas/issues/9536 | 58,637,925 | MDU6SXNzdWU1ODYzNzkyNQ== | 9,536 | Asymmetric error bars are not supported for series (only dataframes) | {
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However, the code only seems to parse this corre... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9537 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9537/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9537/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9537/events | https://github.com/pandas-dev/pandas/issues/9537 | 58,652,753 | MDU6SXNzdWU1ODY1Mjc1Mw== | 9,537 | Nan handling with eval | {
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"color": "0052cc"... | closed | false | null | [] | null | 2 | 2015-02-23T21:37:39Z | 2015-02-24T14:57:52Z | 2015-02-24T14:57:52Z | CONTRIBUTOR | null | For a df I'd like to handle nan's with the query functionality. `df.query('~A.isnull() & ...')` doesn't work because eval doesn't support function calls (`"NotImplementedError: 'Call' nodes are not implemented"`), and `df('A != @nan')` fails in the way you'd expect it to with nan comparison (that is, not filtering anyt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9538 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9538/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9538/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9538/events | https://github.com/pandas-dev/pandas/issues/9538 | 58,663,458 | MDU6SXNzdWU1ODY2MzQ1OA== | 9,538 | attempted relative import in non-package with Pandas | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-02-23T22:53:21Z | 2015-03-02T19:07:55Z | 2015-03-02T19:07:55Z | NONE | null | I am using python 2.6.2 64 bit with windows 8.1. I am very new to python and I have downloaded some libraries such as pandas. My main focus is not python, rather it is on the integration of some simulation software that I use with python. This integration requires downloading some libraries (pandas, numpy, .. etc). So... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9539 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9539/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9539/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9539/events | https://github.com/pandas-dev/pandas/issues/9539 | 58,724,557 | MDU6SXNzdWU1ODcyNDU1Nw== | 9,539 | Unclear error when loading unsupported HDF5 file | {
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"followers_url": "https://api.github.com/users/j... | 2 | 2015-02-24T11:16:51Z | 2020-04-17T18:08:36Z | 2020-04-17T18:08:36Z | CONTRIBUTOR | null | Backstory - I created some H5 files with h5py containing large 3D arrays (not python dataframes). Then I forgot this and tried to load with Pandas. Since they didn't contain real dataframes the load understandably failed. However the error thrown was:
`TypeError: cannot create a storer if the object is not existing n... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9540 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9540/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9540/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9540/events | https://github.com/pandas-dev/pandas/issues/9540 | 58,757,941 | MDU6SXNzdWU1ODc1Nzk0MQ== | 9,540 | Using 'by' and 'weights' together with DataFrame.hist() results in ValueError: weights should have the same shape as x | {
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... | open | false | null | [] | null | 4 | 2015-02-24T15:52:37Z | 2021-04-12T05:35:42Z | null | NONE | null | Wanted to produce grouped histogram such that the heights of the bars add up to 1. The following code results in **ValueError: weights should have the same shape as x**
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(123)
n = 100
df = pd.DataFrame(np.random.randn(n), columns... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9541 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9541/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9541/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9541/events | https://github.com/pandas-dev/pandas/issues/9541 | 58,760,663 | MDU6SXNzdWU1ODc2MDY2Mw== | 9,541 | DOC: Add example of converting timedeltas to periods | {
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"descript... | closed | false | null | [] | null | 7 | 2015-02-24T16:10:57Z | 2015-02-24T23:27:54Z | 2015-02-24T21:29:10Z | CONTRIBUTOR | null | Something like this is super useful and not clear (to me). Maybe there's a more idiomatic way? If so, I didn't see it in the docs.
http://stackoverflow.com/questions/18215317/extracting-days-from-a-numpy-timedelta64-value
(Possible enhancement, adding accessors to datetime-typed columns for things like `day`, `year`,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9542 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9542/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9542/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9542/events | https://github.com/pandas-dev/pandas/issues/9542 | 58,767,143 | MDU6SXNzdWU1ODc2NzE0Mw== | 9,542 | pandas.DataFrame.plot(): Labels do not appear in legend | {
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... | closed | false | null | [] | {
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``` python
x=np.linspace(-10,10,201)
y,z=np.sin(x),np.cos(x)
x,y,z=pd.Series(x),pd.Series(y),pd.Series(z)
df=pd.concat([x,y,z],axis=1)
df.columns=['x','sin(x)','cos(x)']
df=df.set_index('x')
df.plot()
plt.show()
plt.clf();plt.close()
```
![fig... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9543 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9543/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9543/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9543/events | https://github.com/pandas-dev/pandas/issues/9543 | 58,772,112 | MDU6SXNzdWU1ODc3MjExMg== | 9,543 | String value for DataFrame.quantile() axis parameter | {
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"followers_url": "https://api.github.... | 4 | 2015-02-24T17:28:51Z | 2015-06-07T22:54:49Z | 2015-06-07T22:54:49Z | CONTRIBUTOR | null | Hi all,
I don't know if this could be an enhancement but: why do not use string values such as `'index'` or `'columns'` in the axis parameter for `DataFrame.quantile()` function? Such as in `DataFrame.div()`..
I think that it would be more straightforward to use.
Thanks
Francesco
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https://api.github.com/repos/pandas-dev/pandas/issues/9544 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9544/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9544/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9544/events | https://github.com/pandas-dev/pandas/pull/9544 | 58,806,265 | MDExOlB1bGxSZXF1ZXN0Mjk5MzYyNDA= | 9,544 | ENH + BUG: insert of new values for axis parameter on pandas.DataFrame.quantile | {
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"followers_url": "https://api.github.... | 9 | 2015-02-24T21:12:07Z | 2015-06-07T22:54:38Z | 2015-06-07T22:54:38Z | CONTRIBUTOR | null | closes #9543
I added the values "index" and "columns" for the axis parameter of the method pandas.DataFrame.quantile().
Also, there was no check on the value inserted for axis. E.g. if 2 or "foo" was inserted, no ValueError() was raised.
I added a basic check.
Thanks
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"id": 233160... | open | false | null | [] | null | 13 | 2015-02-24T21:47:41Z | 2021-10-24T00:59:05Z | null | NONE | null | groupby-apply workflows are important pandas idioms. Here's a brief example grouping on a named DataFrame column:
```
>>> df = pd.DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3], 'value': range(9)})
>>> result = df.groupby('key').apply(lambda x: x['key'])
>>> result
key
1 0 1
1 1
2 1
2 3 2... | {
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I have a dataframe with columns ['genotype_cont', 0, 2.5, 5]. Trying to call attribute `boxplot` I am getting following problem:
# Code position: index.py, line 1344:
Original line:
theDiff = sorted(set(self) - set(other))
fix:
```
def keyfuncti... | {
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] | closed | false | null | [] | null | 8 | 2015-02-24T23:56:35Z | 2015-02-26T15:39:14Z | 2015-02-26T15:39:14Z | NONE | null | Hi guys,
For certain values of start dates, the method `resample` seems to bug.
I explain with an example :
```
rng = pd.date_range('20110105 12:20:00',periods=5*60,freq='min')
tsMin=pd.Series(range(len(rng)), index=rng)
tsMin
2011-01-05 12:20:00 0
2011-01-05 12:21:00 1
2011-01-05 12:22:00 2
2011-01-05 1... | {
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the following behavior can be seen in 0.14.1 and 0.15.2:
```
pt = pd.Panel({'a': {'b': {'c': 1, 'd': 1}, 'e': {'c': 2, 'd': 2}}})
pt2 = pd.Panel({'a': {'b': {'c': 3, 'd': 5}, 'e': {'c': 4, 'd': 6}}})
pt.a, pt2.a
mask = ... | {
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... | 18 | 2015-02-25T10:54:56Z | 2017-01-12T22:57:06Z | 2017-01-12T22:57:06Z | CONTRIBUTOR | null | So I have a lot of csv's which are clean till say 7 columns and have no missing values but have strings at random places starting after column 7.
I know it is clean till only 7. So, when I say usecols and list the 7 columns, I want it to ignore the other columns, probably truncate the remaining parts in the row when r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9550 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9550/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9550/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9550/events | https://github.com/pandas-dev/pandas/issues/9550 | 58,893,419 | MDU6SXNzdWU1ODg5MzQxOQ== | 9,550 | Poor HDFStore.select performance in case of empty result | {
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For example:
```
### Preparing data
NUM = 5000000
COLS = 100
IDX = np.random.random_integers(0, 100000, size=NUM)*10
df = pd.DataFrame(np.random.randn(NUM, COLS), index=IDX)
### Save: ~4GB uncompressed
st = pd.HDFStore('t... | {
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... | 13 | 2015-02-25T12:37:31Z | 2015-03-23T10:54:50Z | 2015-03-05T23:24:41Z | CONTRIBUTOR | null | Currently the `HDFStore` in `read_hdf` is opened in `rw` mode by default, leading to permission
errors when reading a non-writeable file.
This patch changes the default mode to `r`.
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https://api.github.com/repos/pandas-dev/pandas/issues/9552 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9552/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9552/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9552/events | https://github.com/pandas-dev/pandas/issues/9552 | 58,939,087 | MDU6SXNzdWU1ODkzOTA4Nw== | 9,552 | Disable class level caching in AbstractHolidayCalendar | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-25T17:29:33Z | 2015-04-14T16:40:34Z | 2015-04-14T16:40:34Z | CONTRIBUTOR | null | By caching at the _class_ level it prevents instances from defining different rules - e.g.
``` python
import numpy as np
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
class HolidayCalendar(AbstractHolidayCalendar):
def __init__(self, rules):
self.rules = list(np.atleast_1d(rules))
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> s= pd.PeriodIndex([pd.NaT,'2014-01-01'],freq='M')
> pd.isnull(s)
>
> array([False, False], dtype=bool)
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```
import pandas as pd
df = pd.DataFrame(dict(A=np.array(range(0, 200)),
B=pd.Categorical(['a']*200, categories=['a', 'b'])))
# Setting a categorical with boolean indexing works fine for small indices
print df.loc[df.A == 5]
df.loc[df.A == 5, 'B'] = 'b'
print df.loc[df.A == 5]
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https://api.github.com/repos/pandas-dev/pandas/issues/9556 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9556/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9556/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9556/events | https://github.com/pandas-dev/pandas/issues/9556 | 59,108,278 | MDU6SXNzdWU1OTEwODI3OA== | 9,556 | DataFrame.from_csv undocumented behavior of index_col | {
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http://pandas.pydata.org/pandas-docs/de... | {
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When I execute my python script to produce an excel report,
I get a traceback error as:
Traceback (most recent call last):
File "./bin/si_under_development_iml_orders_last_120_days.py", line 285, in <module>
main()
File "./bin/si_under_de... | {
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... | 5 | 2015-02-26T19:10:15Z | 2015-10-28T03:37:05Z | 2015-10-28T03:37:05Z | CONTRIBUTOR | null | I want to use `where` to set values across a DataFrame where a Series meets a condition.
For example:
``` python
df=pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
```
But this
``` python
df.where(df['a']==2,0)
```
returns an unedited DataFrame.
Whereas this returns just the middle row, as you'd expect:
``` python
df[... | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-02-26T20:06:13Z | 2018-02-15T09:01:16Z | 2018-02-13T12:00:33Z | NONE | null | When I set the error_bad_lines=False, then the conversion of some data (most likely in bad lines) to float in csv is not performed.
pandas version '0.15.2'
numpy version '1.9.1'
Python 2.7.9 (default, Dec 10 2014, 12:24:55) [MSC v.1500 32 bit (Intel)] on win32
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
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https://api.github.com/repos/pandas-dev/pandas/issues/9560 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9560/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9560/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9560/events | https://github.com/pandas-dev/pandas/issues/9560 | 59,162,933 | MDU6SXNzdWU1OTE2MjkzMw== | 9,560 | Difference in groupby behavior between Pandas 0.13.1 and 0.15.2 | {
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... | closed | false | null | [] | null | 1 | 2015-02-26T23:30:36Z | 2015-02-27T11:54:15Z | 2015-02-27T11:51:04Z | NONE | null | Hi, I am seeing a difference in behavior on this groupby between Pandas 0.13.1 and 0.15.2. Specifically, it's like 0.15.2 is doing a cross join while 0.13.1 isn't.
```
print pandas.DataFrame([
{'a': 1, 'b': 2, 'c': 3},
{'a': 4, 'b': 5, 'c': 6}, ]).set_index(
list('ab')).groupby(level=list('ab')).mean()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9561 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9561/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9561/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9561/events | https://github.com/pandas-dev/pandas/pull/9561 | 59,180,285 | MDExOlB1bGxSZXF1ZXN0MzAxMzY5ODA= | 9,561 | BUG: Fix read_csv on S3 files for python 3 | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-02-27T02:29:26Z | 2017-04-05T02:06:14Z | 2015-03-06T03:20:52Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9452
Needed to pass along encoding parameter.
No tests... Kind of hard since it's only s3 only.
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https://api.github.com/repos/pandas-dev/pandas/issues/9562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9562/events | https://github.com/pandas-dev/pandas/issues/9562 | 59,221,598 | MDU6SXNzdWU1OTIyMTU5OA== | 9,562 | API/DOC: to_series keep_tz behaviour for UTC timezone | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-02-27T11:01:34Z | 2015-02-27T12:03:31Z | 2015-02-27T11:59:57Z | MEMBER | null | The docstring of `to_series` says for `keep_tz=True`: _"If the timezone is not set or is UTC, the resulting Series will have a datetime64[ns] dtype. Otherwise the Series will have an object dtype."_
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.to_series.html
But is seems that also for UT... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9563 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9563/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9563/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9563/events | https://github.com/pandas-dev/pandas/issues/9563 | 59,230,877 | MDU6SXNzdWU1OTIzMDg3Nw== | 9,563 | python setup.py develop failing on Mac OSX | {
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"gists_url": "https://api.g... | [] | closed | false | null | [] | null | 1 | 2015-02-27T12:35:24Z | 2015-12-13T00:29:45Z | 2015-12-13T00:29:45Z | CONTRIBUTOR | null | I am trying to build a development version of pandas off master, which I have done successfully in the past. One change is that I did try to build a development version of scipy which is apparently quite difficult (I could not get it to work) and I may have messed up my build system in the process. I have spent many ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9564 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9564/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9564/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9564/events | https://github.com/pandas-dev/pandas/issues/9564 | 59,240,139 | MDU6SXNzdWU1OTI0MDEzOQ== | 9,564 | Licenses update required? | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-02-27T14:08:07Z | 2015-03-01T11:07:21Z | 2015-02-28T14:57:43Z | CONTRIBUTOR | null | Hi,
I was just wondering if all the licenses are up to date? I only ask because I notice a dependency is python-dateutil (which is BSD licensed) but no copy of its license is in the licenses folder.
Nothing major, I have just been considering how to license my own code, that uses Pandas as a library, and my impressio... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9565 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9565/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9565/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9565/events | https://github.com/pandas-dev/pandas/issues/9565 | 59,282,252 | MDU6SXNzdWU1OTI4MjI1Mg== | 9,565 | Difference between C and Python parser engine for pandas.read_csv | {
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"followers_url": "https://api.github.... | 1 | 2015-02-27T19:28:28Z | 2015-06-09T10:49:31Z | 2015-06-09T10:49:31Z | NONE | null | In the code snippet below, I expect that both the values in the c1 and c2 column both are 4.5.
When the Python parser engine is used, this gives me 4 iso 4.5. When using the C parser engine, I get 4.5.
Is this expected behaviour?
``` python
import StringIO
import pandas
csv = """
c1,c2
45e-1,45.0e-1
"""
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https://api.github.com/repos/pandas-dev/pandas/issues/9566 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9566/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9566/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9566/events | https://github.com/pandas-dev/pandas/pull/9566 | 59,358,177 | MDExOlB1bGxSZXF1ZXN0MzAyMzA5NzQ= | 9,566 | API: consistency with .ix and .loc for getitem operations (GH8613) | {
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"followers_url": "https://api.github.com/users/jr... | 21 | 2015-02-28T21:01:15Z | 2015-03-13T17:47:35Z | 2015-03-04T20:46:25Z | CONTRIBUTOR | null | closes #8613
```
In [1]: df = DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=date_range('20130101',periods=5))
In [2]: df
Out[2]:
A B C D
2013-01-01 -1.380065 1.221596 0.279703 -0.119258
2013-01-02 1.684202 -0.202251 -0.961145 -1.595015
2013-01-03... | {
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] | closed | false | null | [] | null | 4 | 2015-02-28T23:58:04Z | 2015-03-01T21:53:43Z | 2015-03-01T21:36:24Z | NONE | null | When plotting large datasets, pandas.plot() is much slower than the direct use of Matplotlib:
``` python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(data=np.random.normal(0, 1, (1, 1000)))
%timeit df.plot()
1 loops, best of 3: 11.2 s per loop
%timeit plt.plot(df.index, d... | {
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... | 12 | 2015-03-01T12:33:52Z | 2021-04-12T05:41:09Z | null | NONE | null | method naming consistency issue #577
- [ ] `to_csv` uses line_terminator and `read_csv` uses lineterminator
- [x] `compression` kw #7615 (#11219)
- [x] `sep/delimiter` #7662
- [x] `from_csv` differs from `read_csv` #9556 (#10163)
- [x] `decimal` #8448 (https://github.com/pandas-dev/pandas/commit/671c4b3c52a02ee3... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9569 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9569/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9569/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9569/events | https://github.com/pandas-dev/pandas/issues/9569 | 59,415,807 | MDU6SXNzdWU1OTQxNTgwNw== | 9,569 | Feature Request: .rand() to call random rows to compliment .head() and .tail() | {
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It would behave something like... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9570/events | https://github.com/pandas-dev/pandas/issues/9570 | 59,513,096 | MDU6SXNzdWU1OTUxMzA5Ng== | 9,570 | timedelta string conversion requires two-digit hour value | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9571/events | https://github.com/pandas-dev/pandas/issues/9571 | 59,542,915 | MDU6SXNzdWU1OTU0MjkxNQ== | 9,571 | web.DataReader with Yahoo not getting correct data set | {
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I am not sure if this issue is related to Pandas or Yahoo Finance however whenever you try to load a stock data with anything different than a 3 at the end of the ticker it returns the wrong number of points.
For instance, if one fixes the start_date and end_date variables and set s="GOLL4.SA" it will return a di... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9572 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9572/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9572/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9572/events | https://github.com/pandas-dev/pandas/issues/9572 | 59,576,609 | MDU6SXNzdWU1OTU3NjYwOQ== | 9,572 | ENH: add days_in_month to Timestamp/DatetimeIndex/.dt | {
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xref #4640
This should be exposed as a vectorized method in tslib (e.g. take in an ndarray of i8 and return an ndarray)
```
pd.tslib.monthrange is an unadvertised / undocumented function that handles the days_in_month calculation (adj... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9573 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9573/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9573/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9573/events | https://github.com/pandas-dev/pandas/issues/9573 | 59,578,391 | MDU6SXNzdWU1OTU3ODM5MQ== | 9,573 | DataFrame.apply() with function that return category series | {
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import pandas as pd
df = pd.DataFrame({"c0":["A","A","B","B"], "c1":["C","C","D","D"]})
df.apply(lambda s:s.astype("category"))
```
the resut is a series with series as element, not a dataframe:
```
c0 [A, A, B, B]
Categories (2, object): [A < B]
c1 [C, C, D, D]
Categories (2, object): [C < D]
dtype: object... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9574 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9574/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9574/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9574/events | https://github.com/pandas-dev/pandas/pull/9574 | 59,590,669 | MDExOlB1bGxSZXF1ZXN0MzAzNDQyNDc= | 9,574 | fixing pandas.DataFrame.plot(): labels do not appear in legend and label kwd | {
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The following behavior has been tested:
- `df.plot(y='sin(x)')` -> gives a legend with label 'None' -> this should give no legend instead (as it plots one series, and then we don't automatically add a legend, see behaviour of df['sin(x)'].plot())
- `df.plot(y... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9575 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9575/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9575/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9575/events | https://github.com/pandas-dev/pandas/pull/9575 | 59,592,522 | MDExOlB1bGxSZXF1ZXN0MzAzNDUyMzM= | 9,575 | DOC: Fixup image size | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-03-03T04:06:38Z | 2017-04-05T02:06:18Z | 2015-03-03T21:15:19Z | CONTRIBUTOR | null | Should fix the image being too big: http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#new-features
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https://api.github.com/repos/pandas-dev/pandas/issues/9576 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9576/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9576/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9576/events | https://github.com/pandas-dev/pandas/issues/9576 | 59,598,319 | MDU6SXNzdWU1OTU5ODMxOQ== | 9,576 | Segfault when json serializing a 0d `numpy.array` | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-03-03T05:50:07Z | 2015-08-31T12:13:52Z | 2015-08-31T12:13:52Z | CONTRIBUTOR | null | Pandas json serializer Segfault when trying to serialize a 0d `numpy.array`. Ideally, it should able to serialize this structure as a scalar or at least raise a `TypeError`.
``` python
import numpy as np
import pandas as pd
pd.json.dumps(np.array(1)) # Segfault
pd.Series([np.array(x) for x in range(5)]).to_json() # S... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9577 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9577/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9577/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9577/events | https://github.com/pandas-dev/pandas/pull/9577 | 59,657,690 | MDExOlB1bGxSZXF1ZXN0MzAzODMwOTQ= | 9,577 | allow method=nearest for fillna. see issue #9471. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9578 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9578/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9578/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9578/events | https://github.com/pandas-dev/pandas/pull/9578 | 59,664,298 | MDExOlB1bGxSZXF1ZXN0MzAzODcxNjE= | 9,578 | BUG: Raise TypeError when serializing 0d ndarray | {
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"description": "read_json, to_json, json... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 23 | 2015-03-03T16:13:23Z | 2015-08-31T12:13:27Z | 2015-08-31T12:13:27Z | CONTRIBUTOR | null | Previously pandas ujson Segfault. see #9576
This is an initial fix. I'll try to make sens out of ujson and see if 0d array can be handle in a better way. However, At the moment raising an exception is still better than a Segfault.
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https://api.github.com/repos/pandas-dev/pandas/issues/9579 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9579/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9579/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9579/events | https://github.com/pandas-dev/pandas/pull/9579 | 59,672,502 | MDExOlB1bGxSZXF1ZXN0MzAzOTIzNzY= | 9,579 | Custom column formatters for HTML in IPython, e.g. can show np 2D-array as image | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9580 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9580/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9580/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9580/events | https://github.com/pandas-dev/pandas/issues/9580 | 59,672,769 | MDU6SXNzdWU1OTY3Mjc2OQ== | 9,580 | Behavior of Series.values when dtype is "category" is surprising | {
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"description": "Categorical Data Type... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jorisva... | 10 | 2015-03-03T17:08:56Z | 2017-02-17T13:56:41Z | 2017-02-17T13:56:41Z | CONTRIBUTOR | null | Say I make a category-type Series:
``` python
s = pd.Series(["a", "b", "c"], dtype="category")
```
I want to pass this to a function that expects a numpy array, so I use the `values` property. According to the documentation:
``` python
s.values?
```
```
Type: property
String form: <property object at 0x106d8... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9581 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9581/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9581/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9581/events | https://github.com/pandas-dev/pandas/issues/9581 | 59,696,298 | MDU6SXNzdWU1OTY5NjI5OA== | 9,581 | issubdtype(<categorical>, np.bool_) raises error | {
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In[14]: import pandas as pd
Backend Qt4Agg is interactive backend. Turning interactive mode on.
In[15]: import numpy as np
In[16]: s = pd.Series([1,2,3,1,2,3]).astype("category")
In[17]: s
Out[17]:
0 1
1 2
2 3
3 1
4 2
5 3
dtype: category
Categories (3, int64): [1 < 2 < 3]
In[18]: np.issubd... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9582 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9582/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9582/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9582/events | https://github.com/pandas-dev/pandas/pull/9582 | 59,725,490 | MDExOlB1bGxSZXF1ZXN0MzA0MjQzMDQ= | 9,582 | Deprecation for 0.16 (#6581) | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-03-03T23:34:30Z | 2015-03-04T20:50:38Z | 2015-03-04T20:50:28Z | CONTRIBUTOR | null | These are the deprecations that are to be removed in 0.16, per #6581, except for the deprecations around the boxplot function.
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https://api.github.com/repos/pandas-dev/pandas/issues/9583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9583/events | https://github.com/pandas-dev/pandas/pull/9583 | 59,726,498 | MDExOlB1bGxSZXF1ZXN0MzA0MjQ5MzI= | 9,583 | Test added and patch to fix python-version-dependent issues when len ro... | {
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"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-03-03T23:44:05Z | 2015-03-07T12:43:55Z | 2015-03-05T23:21:12Z | CONTRIBUTOR | null | ...w/col_levels is 1.
The docs for sparse to_coo methods failed to build. There was some case (row_levels len 1) that failed in python 2.7 only that I failed to test (and I have been building docs in python 3). Have added test and patched. Also needed to expand the interator (list(map(... ) in the "# squish" line as t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9584/events | https://github.com/pandas-dev/pandas/issues/9584 | 59,756,649 | MDU6SXNzdWU1OTc1NjY0OQ== | 9,584 | FutureWarnings should specify a stacklevel | {
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```
/home/njs/.user-python2.7-64bit-3/local/lib/python2.7/site-packages/pandas/core/categorical.py:443: FutureWarning: Accessing 'levels' is deprecated, use 'categories'
warn("Accessing 'levels' is deprecated, use 'categories'", FutureWarning)
/home/njs/.user-python2.7-64bit-3/local/lib... | {
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... | open | false | null | [] | null | 1 | 2015-03-04T09:24:38Z | 2021-04-12T05:42:58Z | null | MEMBER | null | See http://stackoverflow.com/questions/28833074/aggregate-group-with-multi-level-columns
So we want to have some built-in functionality for this?
The example:
```
import itertools
import pandas as pd
lev1 = ['foo', 'bar', 'baz']
lev2 = list('abc')
n = 6
df = pd.DataFrame({k: np.random.randn(n) for k in itertools.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9586 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9586/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9586/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9586/events | https://github.com/pandas-dev/pandas/issues/9586 | 59,773,841 | MDU6SXNzdWU1OTc3Mzg0MQ== | 9,586 | Resampling uses inconsistent labeling for sub-daily and super-daily frequencies | {
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"id": ... | open | false | null | [] | null | 2 | 2015-03-04T09:52:47Z | 2021-04-12T05:44:11Z | null | MEMBER | null | xref #2665
xref #5440
Resample appears to be use an inconsistent label convention depending on whether the target frequency is sub-daily/daily or super-daily:
- For sub-daily/daily frequencies, `label='left'` makes labels at the timestamp corresponding to the start of each frequency bin, and `label='right'` that make... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9587 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9587/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9587/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9587/events | https://github.com/pandas-dev/pandas/issues/9587 | 59,809,698 | MDU6SXNzdWU1OTgwOTY5OA== | 9,587 | Change in the way DataFrames are assigned / copied | {
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Let me show an example.
**In pandas 0.13.1**
``` python
>>> im... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9588 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9588/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9588/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9588/events | https://github.com/pandas-dev/pandas/pull/9588 | 59,815,909 | MDExOlB1bGxSZXF1ZXN0MzA0NzYyNDk= | 9,588 | DOC: update tutorial docs on changed sniffing feature of read_csv | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9589 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9589/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9589/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9589/events | https://github.com/pandas-dev/pandas/issues/9589 | 59,858,439 | MDU6SXNzdWU1OTg1ODQzOQ== | 9,589 | BUG: convert_objects(convert_numeric=True) fails with all strings | {
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"closed_at": "2015-10-09T18:34:35Z",
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"followers_url": "https://api.github.com/users/jr... | 32 | 2015-03-04T20:40:53Z | 2015-07-14T12:47:25Z | 2015-07-14T12:47:25Z | CONTRIBUTOR | null | ```
s=pd.Series(['a','a','a'])
s.convert_objects(convert_numeric=True)
Out[78]:
0 a
1 a
2 a
dtype: object
s[0]=1.0
s
Out[80]:
0 1
1 a
2 a
dtype: object
s.convert_objects(convert_numeric=True)
Out[81]:
0 1
1 NaN
2 NaN
dtype: float64
```
Having a single number changes behavior. Makes `c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9590 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9590/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9590/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9590/events | https://github.com/pandas-dev/pandas/issues/9590 | 59,860,276 | MDU6SXNzdWU1OTg2MDI3Ng== | 9,590 | Support for tables with unlimited columns in HDF | {
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... | 12 | 2015-03-04T20:55:36Z | 2019-10-21T20:06:54Z | null | NONE | null | I wrote a small class that will allow users to utilize HDF tables with significantly more columns than the ~2000 limit currently imposed. I work with mass spec data, and have matrices of ~20,000 x 20,000 x sample_number to deal with so I need things to be written on disk, and compressed.
I'm asking if this is somethin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9591/events | https://github.com/pandas-dev/pandas/issues/9591 | 59,871,360 | MDU6SXNzdWU1OTg3MTM2MA== | 9,591 | build problem with cython 0.21.2 | {
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```
pandas/parser.pyx:164:39: Expected ')', found '*'
building 'pandas.parser' extension
creating build/temp.linux-x86_64-2.7/pandas/src/parser
gcc -pthread -fno-strict-aliasing -I/usr/local/uvcdat/2015-02-10/Externals/include -L/usr/local/uvcdat/2015-02... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9592/events | https://github.com/pandas-dev/pandas/issues/9592 | 59,874,447 | MDU6SXNzdWU1OTg3NDQ0Nw== | 9,592 | Microsecond date formatter | {
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... | 0 | 2015-03-04T22:39:47Z | 2015-03-06T00:33:23Z | null | NONE | null | When plotting time series after importing pandas, the default locator/formatter do not support microsecond resolution handling although it is available in matplotlib 1.3.0+. Besides backwards compatibility reasons, there might not be any reason anymore to keep around the PandasAutoDateLocator/PandasAutoDateFormatter cl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9593 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9593/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9593/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9593/events | https://github.com/pandas-dev/pandas/issues/9593 | 59,877,192 | MDU6SXNzdWU1OTg3NzE5Mg== | 9,593 | groupby problem when dataframe has only one column | {
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>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(index=range(10),data=np.random.randint(0,3,10),columns=['y'])
```
Gives,
```
>>> df.y
0 2
1 0
2 2
3 1
4 0
5 0
6 0
7 1
8 2
9 1
Name: y, dtype: int32
>>> df.groupby('y').count()
Empty DataFrame
Columns: []
Index: [0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9594 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9594/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9594/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9594/events | https://github.com/pandas-dev/pandas/issues/9594 | 59,893,798 | MDU6SXNzdWU1OTg5Mzc5OA== | 9,594 | datetime optimization | {
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"default"... | closed | false | null | [] | null | 6 | 2015-03-05T02:05:50Z | 2016-03-19T02:46:05Z | 2015-03-05T17:24:36Z | NONE | null | Hi,
I noticed that datetime parsing for large sql/csv tables is really slow. Would it be acceptable to use a technique where repeated calculations are cached? For example, instead of:
``` python
def parse_date(date_str) :
return datetime.datetime.strptime(date_str,FMT)
def parse_date_col(str_col) :
return [par... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9595 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9595/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9595/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9595/events | https://github.com/pandas-dev/pandas/issues/9595 | 59,948,062 | MDU6SXNzdWU1OTk0ODA2Mg== | 9,595 | Overview of [] (__getitem__) API | {
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"color": "AD... | open | false | null | [] | null | 20 | 2015-03-05T12:58:01Z | 2021-04-12T05:45:08Z | null | MEMBER | null | some examples (on Series only) in #12890
I started making an overview of the indexing semantics with http://nbviewer.ipython.org/gist/jorisvandenbossche/7889b389a21b41bc1063 (only for series/frame, not for panel)
Conclusion: it is mess :-)
---
#### Summary for slicing
- Slicing with integer labels is:
- always _... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9596 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9596/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9596/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9596/events | https://github.com/pandas-dev/pandas/issues/9596 | 59,970,070 | MDU6SXNzdWU1OTk3MDA3MA== | 9,596 | Error when updating dataframe with empty filter | {
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df = pd.DataFrame({'a': ['1', '2', '3'],
'b': ['11', '22', '33'],
'c': ['111', '222', '333']})
df.loc[df.b.isnull(), 'a'] = df.b
```
The filtering condition is always false (b is never null) and the above code produces this error:
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https://api.github.com/repos/pandas-dev/pandas/issues/9597 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9597/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9597/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9597/events | https://github.com/pandas-dev/pandas/pull/9597 | 60,040,520 | MDExOlB1bGxSZXF1ZXN0MzA2MTA4ODQ= | 9,597 | BUG: Regression in merging Categorical and object dtypes (GH9426) | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-03-06T00:32:55Z | 2015-03-06T03:18:11Z | 2015-03-06T03:18:10Z | CONTRIBUTOR | null | closes #9426
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https://api.github.com/repos/pandas-dev/pandas/issues/9598 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9598/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9598/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9598/events | https://github.com/pandas-dev/pandas/issues/9598 | 60,045,770 | MDU6SXNzdWU2MDA0NTc3MA== | 9,598 | RLS: 0.16.0 | {
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"followers_url": "https://api.github.com/users/jr... | 24 | 2015-03-06T01:33:28Z | 2015-03-23T15:51:27Z | 2015-03-23T10:48:53Z | CONTRIBUTOR | null | planning on a release on March 8/9. If any objections. pls comment.
@jorisvandenbossche @shoyer @TomAugspurger
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https://api.github.com/repos/pandas-dev/pandas/issues/9599 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9599/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9599/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9599/events | https://github.com/pandas-dev/pandas/issues/9599 | 60,078,509 | MDU6SXNzdWU2MDA3ODUwOQ== | 9,599 | DOC: fix autodoc Documenter for Accessor | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-03-06T09:30:10Z | 2015-05-11T12:47:47Z | 2015-05-11T12:47:47Z | MEMBER | null | See my comment here: https://github.com/pydata/pandas/pull/9322#issuecomment-71385126
For the methods on `dt` and `str` I fixed the api docs (https://github.com/pydata/pandas/commit/9b5a5ea18aceddecc3e033bc21f93c12e58bd313), but not yet for `dt` and `str`. Ideally, this needs to be done for the docs for final 0.16
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https://api.github.com/repos/pandas-dev/pandas/issues/9600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9600/events | https://github.com/pandas-dev/pandas/issues/9600 | 60,079,725 | MDU6SXNzdWU2MDA3OTcyNQ== | 9,600 | groupby bug when we use the filter '==' . | {
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"id": ... | closed | false | null | [] | null | 1 | 2015-03-06T09:42:51Z | 2015-03-06T12:52:33Z | 2015-03-06T12:52:33Z | NONE | null | ```
dff = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
dff['C'] = np.arange(8)
dff.groupby('B').filter(lambda x: x['C'] == 6)
```
the result is as below:
```
A,B,C
6,6,c,6,
7,7,c,7
2 rows × 3 columns
```
the column C with the row `7,7,c,7` is not 6 but it was selected!
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9601/events | https://github.com/pandas-dev/pandas/pull/9601 | 60,096,675 | MDExOlB1bGxSZXF1ZXN0MzA2NDE1MzA= | 9,601 | Fix several stata doc issues | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-03-06T12:35:44Z | 2015-11-12T23:44:11Z | 2015-03-06T23:01:56Z | CONTRIBUTOR | null | Related to #9493
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