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https://api.github.com/repos/pandas-dev/pandas/issues/7601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7601/events | https://github.com/pandas-dev/pandas/issues/7601 | 36,724,097 | MDU6SXNzdWUzNjcyNDA5Nw== | 7,601 | API: DatetimeIndex and PeriodIndex string representation | {
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"description": "__repr__ ... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-28T12:10:47Z | 2014-06-29T20:15:53Z | 2014-06-29T20:15:53Z | MEMBER | null | `DatetimeIndex` and `PeriodIndex` have inconsistent representations. Should `PeriodIndex` behaves as the same as `DatetimeIndex`, or `DatetimeIndex` should have one-liner representation in some methods?
```
import pandas and pd
# DatetimeIndex ---------------------------------------------------------------
idx = pd.D... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7602/events | https://github.com/pandas-dev/pandas/pull/7602 | 36,726,466 | MDExOlB1bGxSZXF1ZXN0MTc3Mzg0MTE= | 7,602 | API: DatetimeIndex and PeriodIndex have same representation | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-06-28T14:48:14Z | 2014-09-02T13:21:29Z | 2014-06-29T20:15:53Z | MEMBER | null | Closes #7601. Made `PeriodIndex` to have dummy timezone representation to be consistent with `DatetimeIndex`, but this should be ommited?
### Output after the fix
```
# DatetimeIndex (No change)
pd.date_range(start='2011-01-01', periods=10, freq='M')
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2011-01-31, ..., 2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7603/events | https://github.com/pandas-dev/pandas/pull/7603 | 36,729,354 | MDExOlB1bGxSZXF1ZXN0MTc3Mzk3MDU= | 7,603 | BUG: ewma() weights incorrect when some values are missing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7604/events | https://github.com/pandas-dev/pandas/pull/7604 | 36,736,394 | MDExOlB1bGxSZXF1ZXN0MTc3NDI5NDA= | 7,604 | BUG: {expanding,rolling}_{cov,corr} don't handle arguments with different index sets properly | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7605/events | https://github.com/pandas-dev/pandas/issues/7605 | 36,736,463 | MDU6SXNzdWUzNjczNjQ2Mw== | 7,605 | to_hdf writes data that doesn't match read back | {
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```
records.to_hdf(
args.output, 'records',
mode='w', format='fixed', append=False,
complib='zlib', complevel=7, fletcher32=True)
r2 = pd.read_hdf(
path_or_buf=args.output, key='records',
encoding='utf-8', start=None, stop=None)
from pandas.util.te... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7606/events | https://github.com/pandas-dev/pandas/pull/7606 | 36,740,475 | MDExOlB1bGxSZXF1ZXN0MTc3NDQ3MTI= | 7,606 | BUG: DTI.freqstr raises AttributeError when freq is None | {
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"description": "DateOffsets",
"id": ... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-06-29T06:37:38Z | 2014-06-30T10:58:09Z | 2014-06-29T11:12:57Z | MEMBER | null | `DatetimeIndex.freqstr` raises `AttributeError` if `freq/offset` is `None`, even though docstring says "return the frequency object as a string if its set, otherwise None"
```
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04'])
idx.freqstr
# AttributeError: 'NoneType' object has no attribu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7607/events | https://github.com/pandas-dev/pandas/pull/7607 | 36,740,591 | MDExOlB1bGxSZXF1ZXN0MTc3NDQ3NjQ= | 7,607 | CLN: Simplify Period Construction / Resolution | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7608/events | https://github.com/pandas-dev/pandas/pull/7608 | 36,743,168 | MDExOlB1bGxSZXF1ZXN0MTc3NDU4Mzk= | 7,608 | BUG: DatetimeIndex.delete with tz raises ValueError | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-29T10:16:42Z | 2014-07-05T04:48:06Z | 2014-07-03T21:31:22Z | MEMBER | null | Found and fixed `DatetimeIndex.delete` (#7302) results in `ValueError` when it has `tz` and `freq` is less than daily..
```
idx = pd.date_range(start='2011-01-01 09:00', periods=40, freq='H', tz='Asia/Tokyo')
idx.delete(0)
# ValueError: Inferred frequency None from passed dates does notconform to passed frequency H
``... | {
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"description": "np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate",
"id": 2822342,
"name": "Missing-data",
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},
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"color": "006b75"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-06-29T10:26:56Z | 2014-06-30T10:58:00Z | 2014-06-29T12:43:00Z | MEMBER | null | Related to #7279. `PeriodIndex.min/max` should return `Period` ignoring `NaT`.
```
pidx = pd.PeriodIndex(['2011-01', 'NaT'], freq='M')
pidx.min()
# -9223372036854775808
pidx.max()
#492
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7610/events | https://github.com/pandas-dev/pandas/pull/7610 | 36,746,799 | MDExOlB1bGxSZXF1ZXN0MTc3NDczNDg= | 7,610 | BUG: Timestamp.__new__ doesnt preserve nanosecond | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-06-29T14:14:44Z | 2014-07-02T16:45:22Z | 2014-06-30T13:29:09Z | MEMBER | null | When `Timestamp.__new__` accepts a timestamp with nanosecond, nanosecond is not preserved properly.
```
# create Timestamp with ns (OK)
t = pd.Timestamp('2011-01-01') + pd.offsets.Nano(5)
t, t.value
# (Timestamp('2011-01-01 00:00:00.000000005'), 1293840000000000005)
# If it is passed to Timestamp.__init__, ns is not ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7611/events | https://github.com/pandas-dev/pandas/issues/7611 | 36,747,251 | MDU6SXNzdWUzNjc0NzI1MQ== | 7,611 | to_timedelta does not recognize 'h' and 'm' units | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-06-29T14:38:05Z | 2014-09-05T19:17:55Z | 2014-09-05T10:24:18Z | CONTRIBUTOR | null | 'h' and 'm' units are not correctly recognized in to_timedelta:
```
print pd.to_timedelta(1,unit='D')
print pd.to_timedelta(1,unit='h')
print pd.to_timedelta(1,unit='m')
print pd.to_timedelta(1,unit='s')
print pd.to_timedelta(1,unit='ms')
print pd.to_timedelta(1,unit='us')
print pd.to_timedelta(1,unit='ns')
```
resul... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7612/events | https://github.com/pandas-dev/pandas/issues/7612 | 36,747,825 | MDU6SXNzdWUzNjc0NzgyNQ== | 7,612 | plotting with index of objects should use `FixedLocator` | {
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... | closed | false | null | [] | {
"closed_at": "2019-07-19T00:34:29Z",
"closed_issues": 1289,
"created_at": "2018-10-23T02:34:15Z",
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"followers_url": "https://api.github.com/users/j... | 7 | 2014-06-29T15:09:37Z | 2019-04-30T12:59:53Z | 2019-04-30T12:59:53Z | CONTRIBUTOR | null | As pointed out here (https://stackoverflow.com/questions/24468333/set-xlim-for-pandas-matplotlib-where-index-is-string/24477138#24477138) if the index of a data frame is objects (that is not, the index obviously convertible to scalars and the data is being plotted against `range(len(df))`), the ticklabels are set to be... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7613/events | https://github.com/pandas-dev/pandas/pull/7613 | 36,781,055 | MDExOlB1bGxSZXF1ZXN0MTc3NjI4Nzc= | 7,613 | Fix typo in documentation | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7614/events | https://github.com/pandas-dev/pandas/issues/7614 | 36,790,649 | MDU6SXNzdWUzNjc5MDY0OQ== | 7,614 | BUG: Unexpected behavior from DataFrame.index.levels | {
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``` python
import pandas as pd
import numpy as np
np.random.seed(0)
idx = pd.MultiIndex.from_product([['John', 'Josh', 'Alex'], list('abcde')],
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https://api.github.com/repos/pandas-dev/pandas/issues/7615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7615/events | https://github.com/pandas-dev/pandas/issues/7615 | 36,794,262 | MDU6SXNzdWUzNjc5NDI2Mg== | 7,615 | ENH: DataFrame.to_csv support for "compression='gzip'" | {
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```
import numpy as np,pandas as pd
data=np.arange(10).reshape(5,2)
df=pd.DataFrame(data,columns=['a','b'])
df.to_csv('test.csv.gz',compression='gzip')
```
However, the file it creates is not compressed at all:
francesco@i3 ~/Desktop $ cat t... | {
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closes #6423
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https://api.github.com/repos/pandas-dev/pandas/issues/7618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7618/events | https://github.com/pandas-dev/pandas/issues/7618 | 36,806,229 | MDU6SXNzdWUzNjgwNjIyOQ== | 7,618 | BUG: rolling_window yields unexpected results with win_type='triang' | {
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"description": "Arithmetic, Comparison, ... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-06-30T15:08:40Z | 2014-09-23T13:04:18Z | 2014-09-23T13:04:18Z | CONTRIBUTOR | null | Here's the example in the documentation, modified to have non-zero mean:
```
n = 100
ser = pandas.Series(randn(n)+10, index=pandas.date_range('1/1/2000', periods=n))
pandas.rolling_window(ser, 5, 'triang').plot()
pandas.rolling_window(ser, 5, 'boxcar').plot()
```
The rolling boxcar window is centered around 10, as ex... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7619/events | https://github.com/pandas-dev/pandas/pull/7619 | 36,825,681 | MDExOlB1bGxSZXF1ZXN0MTc3OTAyODA= | 7,619 | ENH: dataframe memory usage | {
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"followers_url": "https://api.github.com/users/jr... | 61 | 2014-06-30T18:44:51Z | 2014-10-05T02:35:24Z | 2014-10-05T02:31:10Z | CONTRIBUTOR | null | Closes [#6852](https://github.com/pydata/pandas/issues/6852)
For a sample DataFrame:
``` python
df = pd.DataFrame({ 'float' : np.random.randn(10000000), 'int' : np.random.randint(0,5,size=10000000), 'date' : Timestamp('20130101'), 'string' : 'foo', 'smallint' : np.random.randint(0,5,size=10000000).astype('int16') })
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https://api.github.com/repos/pandas-dev/pandas/issues/7620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7620/events | https://github.com/pandas-dev/pandas/issues/7620 | 36,835,931 | MDU6SXNzdWUzNjgzNTkzMQ== | 7,620 | Timezones are not comparable between different pytz versions | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2014-06-30T20:33:28Z | 2014-07-06T14:45:14Z | 2014-07-06T14:45:14Z | CONTRIBUTOR | null | I have HDF5 files created in 0.12 which were localized to a timezone ('US/Eastern'). When the dataframe is updated, it is done by calling combine_first which ultimately calls tseries.index.join. Now when I try to update in 0.14, the HDF5 representation changes. This is because of the different versions of pytz and t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7621/events | https://github.com/pandas-dev/pandas/issues/7621 | 36,839,824 | MDU6SXNzdWUzNjgzOTgyNA== | 7,621 | ENH: Categorical serialized | {
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2014-06-30T21:19:09Z | 2018-07-06T22:31:40Z | 2018-07-06T22:31:36Z | CONTRIBUTOR | null | Currently df which include a categorical can be written to a hdf store and other on disc storage formats. For formats which include factors/categoricals, also ensure that such data is read into a categorical.
- [ ] CSV #7217, #7444 (just testing, not a completely fungible format)
- [x] HDF5 #7217, #7444 / #8793
- [x] ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7622/events | https://github.com/pandas-dev/pandas/issues/7622 | 36,842,139 | MDU6SXNzdWUzNjg0MjEzOQ== | 7,622 | DataFrame groupby partially drops timezone info (to_csv, in notebook) | {
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... | closed | false | null | [] | null | 4 | 2014-06-30T21:48:08Z | 2014-07-18T16:42:38Z | 2014-06-30T22:16:09Z | NONE | null | Hi all,
I've encountered a problem with DataFrames, groupby and timezones.
import pandas as pd
import numpy as np
dt_rng = pd.date_range(start='2014-01-01 00:00', periods = 1000, freq='1s', tz='Europe/Berlin')
df = pd.DataFrame({'a':np.random.randn(1000), 'b': np.random.randn(1000)},index = dt_rng)
df['b'] = df['b']... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7623/events | https://github.com/pandas-dev/pandas/issues/7623 | 36,843,792 | MDU6SXNzdWUzNjg0Mzc5Mg== | 7,623 | TST: failing windows parser test | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-06-30T22:10:22Z | 2014-07-02T14:19:28Z | 2014-07-02T14:19:28Z | CONTRIBUTOR | null | This test is failing on all versions of windows (but not linux).
related to #7582, #7591
cc @amras1
cc @mcwitt
not sure what is failing this
```
FAIL: test_concat_invalid_first_argument (pandas.tools.tests.test_merge.TestConcatenate)
----------------------------------------------------------------------
Traceback (... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7624/events | https://github.com/pandas-dev/pandas/pull/7624 | 36,856,382 | MDExOlB1bGxSZXF1ZXN0MTc4MDg4MDU= | 7,624 | CLN: Fix typo | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7625/events | https://github.com/pandas-dev/pandas/pull/7625 | 36,857,320 | MDExOlB1bGxSZXF1ZXN0MTc4MDkyOTQ= | 7,625 | CLN/DEPR: Fix instances of 'U'/'rU' in open(...) | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-07-01T02:28:22Z | 2014-07-02T11:46:11Z | 2014-07-01T17:52:25Z | CONTRIBUTOR | null | closes #7131
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https://api.github.com/repos/pandas-dev/pandas/issues/7626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7626/events | https://github.com/pandas-dev/pandas/issues/7626 | 36,858,406 | MDU6SXNzdWUzNjg1ODQwNg== | 7,626 | nrows limit fails reading well formed csv files from Australian electricity market data | {
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These market data files are 4 csv files combined into a single csv file and so the file has multiple headers and variable field size across the rows.
The first set of data is from ro... | {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2014-07-01T05:45:02Z | 2014-07-01T17:42:19Z | 2014-07-01T11:36:24Z | CONTRIBUTOR | null | This PR speeds up MultiIndex.from_product employing the fact that operating on categorical codes is faster than on the values themselves.
This yields about 2x improvement in the benchmark
``` python
In [1]: import pandas.util.testing as tm
In [2]: data = [tm.makeStringIndex(10000), tm.makeFloatIndex(20)]
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https://api.github.com/repos/pandas-dev/pandas/issues/7628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7628/events | https://github.com/pandas-dev/pandas/issues/7628 | 36,867,599 | MDU6SXNzdWUzNjg2NzU5OQ== | 7,628 | BUG: nth on series groupby with dropna=True gives TypeError | {
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```
In [1]: df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
In [2]: g = df.groupby('A')
In [3]: g.B.nth(0, dropna=True)
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https://api.github.com/repos/pandas-dev/pandas/issues/7629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7629/events | https://github.com/pandas-dev/pandas/issues/7629 | 36,879,985 | MDU6SXNzdWUzNjg3OTk4NQ== | 7,629 | Implement CategoricalIndex | {
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... | 9 | 2014-07-01T10:28:34Z | 2015-04-20T11:19:57Z | 2015-04-20T11:19:57Z | CONTRIBUTOR | null | When #7217 becomes available, it would be nice to also have a 'CategoricalIndex' so that the sorting behaviour based on levels will be preserved when a categorical becomes a index:
``` python
cats = pd.Categorical([1,2,3,4], levels=[4,2,3,1])
strings = ["a","b","c","d"]
values = [4,2,3,1]
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https://api.github.com/repos/pandas-dev/pandas/issues/7630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7630/events | https://github.com/pandas-dev/pandas/issues/7630 | 36,884,036 | MDU6SXNzdWUzNjg4NDAzNg== | 7,630 | BUG: tz lost on combine first/fillna | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-07-01T11:33:16Z | 2016-08-12T15:44:54Z | 2016-08-12T15:44:54Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24502838/pandas-tzinfo-lost-by-combine-first
```
In [10]: df1 = pd.DataFrame(columns=['UTCdatetime','abc'],
....: data=pd.to_datetime('20100101 01:01').tz_localize('UTC'),
....: index=pd.date_range('20140627', periods=1))
In [11]: print(df1)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7632/events | https://github.com/pandas-dev/pandas/issues/7632 | 36,887,783 | MDU6SXNzdWUzNjg4Nzc4Mw== | 7,632 | Bug? Dubious resulting index from merge outer join with left_index=True | {
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Is t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7633/events | https://github.com/pandas-dev/pandas/issues/7633 | 36,888,709 | MDU6SXNzdWUzNjg4ODcwOQ== | 7,633 | PERF: perf regressions vs 0.14.0 | {
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but need to look at these (resamples could be spurious)
```
dataframe_resample_mean_string | 4.0483 | 2.4170 | 1.6749 |
dataframe_resample_max_numpy | 3.3394 | 1.7230 | 1.9381 |
dataframe_resample_max_string | 3.3154 |... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7634/events | https://github.com/pandas-dev/pandas/pull/7634 | 36,889,772 | MDExOlB1bGxSZXF1ZXN0MTc4Mjg1MTE= | 7,634 | FIX: don't create 32 bit int type for int64 column (GH7433) | {
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Replace the sqlalchemy `Integer` type with `BigInteger` as the default integer type in pandas is int64.
Probably we could be more specific in the type conversion (check for the exact integer type).
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https://api.github.com/repos/pandas-dev/pandas/issues/7635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7635/events | https://github.com/pandas-dev/pandas/pull/7635 | 36,890,364 | MDExOlB1bGxSZXF1ZXN0MTc4Mjg4NTk= | 7,635 | DOC: small doc build fixes | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7636/events | https://github.com/pandas-dev/pandas/issues/7636 | 36,892,751 | MDU6SXNzdWUzNjg5Mjc1MQ== | 7,636 | Color bars by category | {
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"followers_url": "https://api.github.com/users/jorisva... | 8 | 2014-07-01T13:36:21Z | 2018-07-06T22:26:45Z | 2018-07-06T22:26:26Z | NONE | null | Would it make sense to add functionality for coloring bar charts according to a categorical value in a column? It could then by be passed as an argument such as:
``` python
df.plot(kind='bar', colors=['r', 'g', 'b'], color_by=['B'])
```
which would lead to ['r', 'g', 'b'] being cycled throughout the values in column ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7637/events | https://github.com/pandas-dev/pandas/issues/7637 | 36,898,928 | MDU6SXNzdWUzNjg5ODkyOA== | 7,637 | Accessing Series with duplicate index returns Series, but accessing a unique index returns scalar | {
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"descript... | closed | false | null | [] | null | 2 | 2014-07-01T14:40:49Z | 2014-08-15T12:49:14Z | 2014-07-07T15:40:28Z | CONTRIBUTOR | null | Suppose I have a Series with a duplicate indices:
``` python
In [109]: s = pd.Series([1, 2, 3, 4, 5], index=['a', 'a', 'a', 'a', 'b'])
In [110]: s
Out[110]:
a 1
a 2
a 3
a 4
b 5
dtype: int64
```
If I access the duplicated index, I get a Series:
``` python
In [111]: s.xs('a')
Out[111]:
a 1
a 2
a ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7640/events | https://github.com/pandas-dev/pandas/issues/7640 | 36,932,609 | MDU6SXNzdWUzNjkzMjYwOQ== | 7,640 | Proposal: New Index type for binned data (IntervalIndex) | {
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The idea is to have a natural representation of the grids that ubiquitously appear in simulations and measurements of physical systems. Instead of referencing a single value, a grid cell references a _range_ of values, based on the chosen discretization. Typically, cells boundaries would be specified by flo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7641/events | https://github.com/pandas-dev/pandas/issues/7641 | 36,940,305 | MDU6SXNzdWUzNjk0MDMwNQ== | 7,641 | kernel crash | {
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"name": "IO JSON",
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] | closed | false | null | [] | null | 5 | 2014-07-01T23:32:05Z | 2015-10-20T22:28:32Z | 2015-10-20T22:28:32Z | NONE | null | So I have a 1.07 GB json file that I am trying to open. However, the kernel crashes every time I try to run these three lines:
import pandas as pd
f = open("large json file", "r")
pd.read_json(f)
Anyone have any ideas on this? Is it a defect?
Python 2.7.3
[GCC 4.7.2] on linux2
**\* glibc detected **\* python: d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7642/events | https://github.com/pandas-dev/pandas/issues/7642 | 36,943,112 | MDU6SXNzdWUzNjk0MzExMg== | 7,642 | openpyxl compatibility warning doesn't catch no openpyxl installed | {
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> Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.
I did not have any version of openpyxl installed though. Perhaps the change made to `pandas/io/excel.py` in #7214 should have been more like:
``` diff
-register_writer(_Openpyxl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7643/events | https://github.com/pandas-dev/pandas/issues/7643 | 36,945,839 | MDU6SXNzdWUzNjk0NTgzOQ== | 7,643 | Plot method ignores upper and lower error bar arguments | {
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"gists... | [] | closed | false | null | [] | null | 1 | 2014-07-02T01:39:32Z | 2015-10-20T22:30:01Z | 2015-10-20T22:30:01Z | CONTRIBUTOR | null | This seems to be specific to Pandas as opposed to matplotlib. Worked as desired as of a few months ago when I last updated from master.
This is the desired behavior in matplotlib:
``` python
barh([0,1], [.3, .5], xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
![image](https://cloud.... | {
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"gists... | [] | closed | false | null | [] | null | 5 | 2014-07-02T04:02:24Z | 2014-10-02T12:29:19Z | 2014-10-02T12:29:19Z | NONE | null | While running OS X 10.9.3 and Python 2.7.6 I get a seg fault when trying to add a new column to an existing DataFrame where the new column is initialized to an empty string (""). When initializing to np.NaN, the fault does not occur. An example is below.
mydataframe['new_column'] = "" <- this results in 'Segmentatio... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7645 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7645/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7645/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7645/events | https://github.com/pandas-dev/pandas/issues/7645 | 36,961,957 | MDU6SXNzdWUzNjk2MTk1Nw== | 7,645 | Using .compound in groupby | {
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2014-07-02T08:38:16Z | 2018-07-06T22:15:43Z | 2018-07-06T22:15:43Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame({'grp': [0, 0, 1, 1], 'returns': [0, 0.1, -.1, .1]})
df.groupby('grp').returns.compound()
df.groupby('grp').returns.apply('compound')
```
Doesn't seem to work:
```
---------------------------------------------------------------------------
AttributeError Traceba... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7646 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7646/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7646/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7646/events | https://github.com/pandas-dev/pandas/issues/7646 | 36,967,231 | MDU6SXNzdWUzNjk2NzIzMQ== | 7,646 | Can't append hdf5 file with df.to_hdf | {
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```
df.to_hdf('store.h5', 'data', format='table', append=True)
```
gives
```
/home/jussitii/py3env/lib/python3.2/site-packages/pandas/core/generic.py in to_hdf(self, path_or_buf, key, **kwargs)
894
895 from pandas.io import p... | {
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"default": false,
"description": "Related to indexing on s... | closed | false | null | [] | {
"closed_at": "2015-03-23T10:50:37Z",
"closed_issues": 400,
"created_at": "2014-02-14T03:31:22Z",
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import pandas as pd
import numpy as np
ser = pd.Series(['a', 'b', np.nan])
print ser
ser.loc[ser.isnull()] = None
print ser
ser[ser.isnull()] = None
print ser
```
Output
```
0 a
1 b
2 NaN
dtype: object
0 a
1 b
2 None
dtype: object
0 a
1 b
2 NaN
dtype: object
```
I am... | {
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prob just need to protect these a bit
here as well: https://travis-ci.org/jreback/pandas/jobs/28958102
```
======================================================================
ERROR: test_get_all_data (pandas.io.tests.test_data.TestYahooOptions)
-----------------------------------------------------... | {
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Suppose I have a DataFrame:
``` python
In [61]: df
Out[61]:
ref localtime size
0 45361866 2014-06-25 14:11:11.753597 100
1 45361866 2014-06-25 14:11:11.753769 100
2 45361866 2014-06-25 14:11:11.754350 200
3 45361866 2014-06-25 14:11:11.756... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7650/events | https://github.com/pandas-dev/pandas/issues/7650 | 37,003,994 | MDU6SXNzdWUzNzAwMzk5NA== | 7,650 | TST: random read_html failure? | {
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@cpcloud
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2014-07-02T17:25:47Z | 2018-07-06T23:08:57Z | 2018-07-06T22:14:27Z | CONTRIBUTOR | null | As the next step of separation-of-concerns plan (#6744) I'd like to
propose adding a method (or several, actually) to `Index` class that
would encapsulate the details of `foo.loc[l1,l2,...]` lookup.
### Implementation Idea
Roughly, the idea is to make `loc`'s getitem as simple as
``` python
def __getitem__(self, inde... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-07-02T18:02:53Z | 2014-07-02T18:38:23Z | 2014-07-02T18:38:23Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
datetimeindex_normalize | 3.3297 | 83.0923 | 0.040... | {
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"default"... | closed | false | null | [] | null | 9 | 2014-07-02T20:47:36Z | 2014-07-16T23:51:25Z | 2014-07-03T19:40:30Z | NONE | null | I got the following error trying to do
```
store.append(output_name, data_frame)
```
for a DataFrame with 2 rows and 3202 columns:
```
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 914, in append
**kwargs)
File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 1273, in _write_to_... | {
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``` python
In [4]: pd.Series([datetime.now()]).dtype
Out[4]: dtype('<M8[ns]')
In [5]: pd.Series([pytz.UTC.localize(datetime.now())]).dtype
Out[5]: dtype('O')
```
pandas 0.13.1 and 0.14.0
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This should align on the columns axis. Not clear how to specify the alignment level though (e.g. broadcasting would be tricky here).
```
import numpy as np
import pandas as pd
arrays = [np.hstack([ ['one']*3,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7656 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7656/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7656/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7656/events | https://github.com/pandas-dev/pandas/issues/7656 | 37,086,687 | MDU6SXNzdWUzNzA4NjY4Nw== | 7,656 | DataFrame.where does not work as expected with inplace=True | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-07-03T14:25:22Z | 2014-07-03T16:26:12Z | 2014-07-03T16:26:12Z | CONTRIBUTOR | null | related #4667, #7647
According to the documentation, `inplace` should replace values appropriately with `other`, but this does not seem to work as expected. This worked in 0.12.
```
In [6]: df = pandas.DataFrame([{'A': 1, 'B': np.nan, 'C': 'Test'}, {'A': np.nan, 'B': 'Test', 'C': np.nan}])
In [7]: df
Out[7]:
A... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7657 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7657/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7657/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7657/events | https://github.com/pandas-dev/pandas/pull/7657 | 37,093,932 | MDExOlB1bGxSZXF1ZXN0MTc5NTE2MDk= | 7,657 | API: disallow inplace setting with where and a non-np.nan value (GH7656) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7658 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7658/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7658/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7658/events | https://github.com/pandas-dev/pandas/issues/7658 | 37,107,532 | MDU6SXNzdWUzNzEwNzUzMg== | 7,658 | SettingWithCopyWarning in Core\Indexing.py | {
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df.loc[:, 'A'] = df['A'] \* (df['B'] / df['C'])
and I received a warning from within Pandas
...\pandas\core\indexing.py:385: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value inst... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7659 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7659/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7659/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7659/events | https://github.com/pandas-dev/pandas/issues/7659 | 37,107,978 | MDU6SXNzdWUzNzEwNzk3OA== | 7,659 | Documentation on pandas.DataFrame.to_excel | {
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I noticed there is a typo in the documentation of [pandas.DataFrame.to_excel](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html):
The parameter startow should be startrow.
**startow**:
upper left cell row to dump data frame
should be:
**startrow**
upper left cell row to d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7660 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7660/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7660/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7660/events | https://github.com/pandas-dev/pandas/issues/7660 | 37,116,660 | MDU6SXNzdWUzNzExNjY2MA== | 7,660 | BUG: stacking multiple levels | {
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cc @bluefir
```
df = DataFrame(np.random.randn(1000, 27),
columns=MultiIndex.from_tuples(list(
itertools.product(xrange(3), repeat=3))))
df.stack(level=[1, 2])
```
should be equiv to:
`df.stack(level=1).stack(level=1)`
but is raising
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https://api.github.com/repos/pandas-dev/pandas/issues/7661 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7661/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7661/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7661/events | https://github.com/pandas-dev/pandas/pull/7661 | 37,126,259 | MDExOlB1bGxSZXF1ZXN0MTc5Njk4NDI= | 7,661 | FIX: Scalar timedelta NaT results raise | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2014-07-03T21:36:46Z | 2014-09-14T14:20:10Z | 2014-09-14T14:20:10Z | CONTRIBUTOR | null | Currently, coercion of scalar results from `float` to `timedelta64[ns]` passes through `int`, which raises when attempting to coerce `NaN` to `NaT`.
To reproduce:
``` python
import pandas as pd
import numpy as np
pd.Series([np.timedelta64('NaT')]).sum()
# TypeError: reduction operation 'sum' not allowed for this dty... | {
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"followers_url": "https://api.github.com/users/jorisva... | 19 | 2014-07-03T22:06:24Z | 2020-09-09T11:20:23Z | 2016-10-24T22:08:22Z | CONTRIBUTOR | null | related #7615
Bit of a UI problem here (although it's behaving as the docstring says it does, so it doesn't quite qualify as a bug):
```
>>> df = pd.DataFrame({"A": [1,2,3], "B": [4,5,6]})
>>> df.to_csv("tmp.csv", sep=";")
>>> !cat tmp.csv
;A;B
0;1;4
1;2;5
2;3;6
>>> df.to_csv("tmp.csv", delimiter=";")
>>> !cat tmp.cs... | {
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``` python
import pandas as pd
import numpy as np
df = pd.Series(np.random.randn(9))
df.i... | {
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```
======================================================================
ERROR: test_ts_plot_format_coord (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0... | {
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Also protected with RemoteDataError checking for the expiry month links.
Fixes #7648
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``` python
df = pd.DataFrame({'foo': [1, 2, 2], 'bar': [True, False, False]})
```
I want group this by foo and count the number of True values in the bar column. Counting the True values can be achieved with the sum command.
``` python
In [7]: bar = [True, Fa... | {
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#6608 seems to be solved by following 4 fixes.
- [ ] `PeriodIndex` should support the same freqs as `DatetimeIndex` (Related to #7222, maybe solved by #5148)
- [ ] Better logic to find common divisor frequency (try to use `tsplot` as much)
- [x] When plotting with `x_compat` to the `ax` w... | {
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Modified:
- Avoid every time module `import` in `Index.max/min`
- Avoid duplicated `max` call from `resample/_get_time_bins` and `_get_range_edges`.
- Optimize `lib/generate_bins_dt64` and `tslib/pe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7674 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7674/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7674/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7674/events | https://github.com/pandas-dev/pandas/pull/7674 | 37,220,316 | MDExOlB1bGxSZXF1ZXN0MTgwMTg2NzQ= | 7,674 | DOC: remove extra spaces from option descriptions | {
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This caused the descriptions to be longer than 79 characters, and so line wrapping in the terminal. Closes #6838.
Plus, moved the default and current values to the last line of the description as proposed by @jseabol... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7675/events | https://github.com/pandas-dev/pandas/pull/7675 | 37,223,708 | MDExOlB1bGxSZXF1ZXN0MTgwMjAxMjI= | 7,675 | TST: skip buggy tests on debian (GH6270, GH7664) | {
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closes #7664
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https://api.github.com/repos/pandas-dev/pandas/issues/7677 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7677/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7677/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7677/events | https://github.com/pandas-dev/pandas/issues/7677 | 37,250,973 | MDU6SXNzdWUzNzI1MDk3Mw== | 7,677 | BUG: sqrt not implemented in df.eval | {
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"closed_at": "2015-10-09T18:34:35Z",
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"followers_url": "https://api.github.com/users/jr... | 16 | 2014-07-07T09:08:09Z | 2015-10-19T20:52:48Z | 2015-10-19T20:52:40Z | CONTRIBUTOR | null | In the following scenario:
```
import pandas as pd
import numpy as np
a = np.random.rand(1000)
df = pd.DataFrame({'a' : a })
```
This call doesn't work (while it is supposed to, I guess)
```
df.eval('sqrt(a)')
NotImplementedError: 'Call' nodes are not implemented
```
while this one does
```
df.eval('(a)**(.... | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-07-07T09:47:10Z | 2014-07-07T20:07:52Z | 2014-07-07T17:46:41Z | CONTRIBUTOR | null | In the original code, `com.is_bool(arr_or_dtype)` checks whether `arr_or_dtype` is a boolean value instead of a boolean dtype.
A new function `is_bool_dtype` is added to `pandas.core.common` to fix this bug.
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``` python
from pandas import read_csv
from StringIO import StringIO
data = '''2013-09-28,4018.02
2013-10-05,5806.78
2013-10-26,6334.4
2013-11-02,6199.6
2013-11-09,6213.07
2013-11-16,7... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7680/events | https://github.com/pandas-dev/pandas/issues/7680 | 37,266,017 | MDU6SXNzdWUzNzI2NjAxNw== | 7,680 | index bug in 0.14? | {
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pandas.Index([('a',), ('a', 'b')]).values
```
pandas < 0.14 returns:
```
array([('a',), ('a', 'b')], dtype=object)
```
pandas 0.14 returns:
```
array([('a', nan), ('a', 'b')], dtype=object)
```
possibly related to https://github.com/pydata/pandas/issues/7548 ?
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``` python
[profile Data-Robot]
aws_access_key_id = a key
aws_secret_access_key = a secret
[Credentials]
aws_access_key_id = default key
aws_secret_access_key = default secret
```
you can pass
``````
In [70]: con = boto.connect_s3(p... | {
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The name of the function implies that it is an iterator and should not take much to run. ~~However~~, ~~in the method it uses builtin method 'zip'~~, ~~which can sometimes generate huge temporary list of tuples ... | {
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close to what it was in 0.14.0
key was to not keep recomputing whether an index `hasnans` every time we need it (it is now cached).
further `min/max` are optimized if the index is monotonic
```
-------------------------------------------------------------------------------
Test name ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7685/events | https://github.com/pandas-dev/pandas/issues/7685 | 37,301,449 | MDU6SXNzdWUzNzMwMTQ0OQ== | 7,685 | TST: test failure on get_near_stock_price | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-07-07T19:24:15Z | 2014-07-08T23:33:47Z | 2014-07-08T23:33:47Z | CONTRIBUTOR | null | have seen this a couple of times: https://travis-ci.org/pydata/pandas/jobs/29340410
slightly different error: https://travis-ci.org/pydata/pandas/jobs/29348881
cc @dstephens99 as you seem to be the expert :)
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-07-07T19:52:11Z | 2014-07-08T08:21:52Z | 2014-07-07T23:58:09Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/29348880
started with this merge: https://github.com/pydata/pandas/pull/7678
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When doing a re-sample, loffset has no effect if a numpy.timedelta64 is passed in:
``` python
In [1]: rng = pd.date_range('1/1/2012', periods=4, freq='S')
In [2]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [3]: ts.... | {
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Fixes #7685
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I'm trying to build a RESTful api with Flask-RESTful which will return Pandas DataFrame as JSON data.
```
from flask import Flask
from flask.ext import restful
from flask.ext.restful import Resource, Api
import pandas as pd
import click
import logging
app = Flask(__name__)
api = restful.Api(app)
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This should act like an inner join but it adds NULL rows to the table being operated on. At a very least it should raise an error when the index is not found.
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In the terminal:
If maxcols == 0 then auto detect columns
if maxrows == 0 auto detect rows
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https://api.github.com/repos/pandas-dev/pandas/issues/7692 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7692/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7692/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7692/events | https://github.com/pandas-dev/pandas/issues/7692 | 37,366,954 | MDU6SXNzdWUzNzM2Njk1NA== | 7,692 | ERR/API: raise NotImplemented for Panel broadcastables with non-scalar | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-07-08T12:58:19Z | 2015-09-21T15:32:29Z | 2015-09-21T15:32:29Z | CONTRIBUTOR | null | This works fine
http://stackoverflow.com/questions/24631991/division-of-a-panel-by-a-series/24632381#24632381
```
[8]: p = Panel(np.arange(3*4*5).reshape(3,4,5),items=['ItemA','ItemB','ItemC'],major_axis=date_range('20130101',periods=4),minor_axis=list('ABCDE'))
In [9]: p
Out[9]:
<class 'pandas.core.panel.Panel'>
Di... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7693 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7693/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7693/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7693/events | https://github.com/pandas-dev/pandas/issues/7693 | 37,370,240 | MDU6SXNzdWUzNzM3MDI0MA== | 7,693 | 32bit 0.14.0+git393-g959e3e4: test_select_dtypes_exclude_include | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-07-08T13:36:43Z | 2014-07-08T14:15:35Z | 2014-07-08T14:15:35Z | CONTRIBUTOR | null | goes like this
```
======================================================================
FAIL: test_select_dtypes_exclude_include (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0+git393-g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7694 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7694/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7694/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7694/events | https://github.com/pandas-dev/pandas/issues/7694 | 37,370,562 | MDU6SXNzdWUzNzM3MDU2Mg== | 7,694 | test_select_dtypes_not_an_attr_but_still_valid_dtype ValueError: 'timedelta64[ns]' is too specific of a frequency, try passing 'timedelta64' | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2014-07-08T13:40:29Z | 2014-07-08T15:40:09Z | 2014-07-08T15:40:09Z | CONTRIBUTOR | null | 0.14.0+git393-g959e3e4 building on not so recent ones (32bit builds are failing on that another one while testing for python2, so never see this one on python3)
```
======================================================================
ERROR: test_select_dtypes_not_an_attr_but_still_valid_dtype (pandas.tests.test_fra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7695 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7695/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7695/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7695/events | https://github.com/pandas-dev/pandas/issues/7695 | 37,373,830 | MDU6SXNzdWUzNzM3MzgzMA== | 7,695 | DataFrame.plot(kind="area") doesn't work with DatetimeIndex | {
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] | closed | false | null | [] | null | 2 | 2014-07-08T14:14:50Z | 2014-07-08T14:46:25Z | 2014-07-08T14:46:25Z | CONTRIBUTOR | null | I'm on pandas 0.14.0 / matplotlib 1.3.1.
Given the DataFrame
```
ipdb> print(df)
Coal Diesel Gas Hydro Nuclear
2003-01-31 0 0.893509 0 0.106491 0
2005-03-31 0 1.218053 0 0.000000 0
2006-03-31 0 1.218053 0 0.106491 0
2007-01-31 0 1.218... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7696/events | https://github.com/pandas-dev/pandas/pull/7696 | 37,373,851 | MDExOlB1bGxSZXF1ZXN0MTgxMDQzMTA= | 7,696 | TST/CLN: centralize numpy < 1.7 skips | {
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closes #7694
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https://api.github.com/repos/pandas-dev/pandas/issues/7697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7697/events | https://github.com/pandas-dev/pandas/pull/7697 | 37,384,875 | MDExOlB1bGxSZXF1ZXN0MTgxMTA4NzU= | 7,697 | BUG/PERF: offsets.apply doesnt preserve nanosecond | {
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- Support dateutil timezone
- Little performance improvement. Even though v0.14.1 should take longer than v0.14.0 because perf test in v0.14 doesn't perform timestamp conversion which was fixed in #7502.
NOTE: This cac... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7699/events | https://github.com/pandas-dev/pandas/issues/7699 | 37,430,669 | MDU6SXNzdWUzNzQzMDY2OQ== | 7,699 | Bug in quantile? | {
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I'm using Pandas 0.14.0 with Python3 on a 64bit Linux Mint. It looks like the Pandas `quantile` function is not working properly with the `axis=1` keyword. I'm getting the same output as `axis=0`, as you can see below.
```
In [23]: import numpy as np
In [24]: import numpy as np
In [25]: df = pd.DataFram... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/7700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7700/events | https://github.com/pandas-dev/pandas/issues/7700 | 37,438,726 | MDU6SXNzdWUzNzQzODcyNg== | 7,700 | Bug in resample with multi-index columns? | {
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"description": "r... | closed | false | null | [] | null | 3 | 2014-07-09T06:59:15Z | 2014-07-09T21:03:55Z | 2014-07-09T21:03:23Z | NONE | null | Hi folks,
It appears that in Pandas 0.14.0, resampling on data frames with multi-index columns erases the multi-index names. You can see an example of this at http://stackoverflow.com/questions/24442832/how-to-resample-a-pandas-multi-index-data-frame-via-methods-depending-on-the-col/24443544?noredirect=1#comment38000... | {
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