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https://api.github.com/repos/pandas-dev/pandas/issues/6701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6701/events | https://github.com/pandas-dev/pandas/issues/6701 | 30,117,584 | MDU6SXNzdWUzMDExNzU4NA== | 6,701 | ENH: SQL Enhancement for the Future | {
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... | 3 | 2014-03-25T12:12:44Z | 2019-10-22T04:02:07Z | 2019-10-22T04:02:06Z | CONTRIBUTOR | null | 0.14 Enhancements : #6292
- [x] handle no rows returns from a sql query, #3745
- [ ] option for default_charset to sql.write_frame #5008
- [x] sql to provided NaN/NaT conversions #2754 -> PR #8208
- [x] specify chunksize for sql.read_frame #2908 -> PR #8330
- [ ] SQL through SQLAlchemy - check performance #6416
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```
In [23]: df = DataFrame({'a':[1,2,3],'b':[1,2,3],'c':[1,2,3]})
In [24]: x = df.set_index(['a','b'])
In [25]: x
Out[25]:
c
a b
1 1 1
2 2 2
3 3 3
[3 rows x 1 columns]
In [26]: x... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6705/events | https://github.com/pandas-dev/pandas/pull/6705 | 30,154,809 | MDExOlB1bGxSZXF1ZXN0MTM5NzExMTU= | 6,705 | ENH: Implement core/strings/wrap method | {
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the behavior of R's stringr library. When a string is 'wrap'ped, the return value will be a
paragraph with lines of max(word length, width) length.
Example:
``` python
>>> str_wrap(Series([u'line to be wrapped', u'another li... | {
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"f... | 3 | 2014-03-25T23:46:02Z | 2019-12-11T22:39:05Z | null | NONE | null | As I understand it, read_csv and read_table both use the C parser, but read_fwf uses a pure Python parser. As such, the latter is significantly slower and there doesn't seem to be any workaround. Is there any plan to add an optimized fwf reader?
See: https://groups.google.com/forum/#!topic/pystatsmodels/0DVBmCsDrVI
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Closes #6708
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https://api.github.com/repos/pandas-dev/pandas/issues/6710 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6710/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6710/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6710/events | https://github.com/pandas-dev/pandas/issues/6710 | 30,229,592 | MDU6SXNzdWUzMDIyOTU5Mg== | 6,710 | BUG: read_csv raises exception if first row is incomplete | {
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xref #8985
This is a bit like issue #4749, but the conditions are different.
I have been able to reproduce it by specifying `usecols`:
``` python
In [98]: mydata='1,2,3\n1,2\n1,2\n'
In [99]: pd.read_csv(StringIO(mydata), names=['a', 'b', 'c'], usecols=['a', 'c'])
Out[99]:
a c
0 1 3
1 1 NaN
2 1 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6711 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6711/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6711/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6711/events | https://github.com/pandas-dev/pandas/issues/6711 | 30,236,029 | MDU6SXNzdWUzMDIzNjAyOQ== | 6,711 | version 0.13.1 appears as 0.12.0 in pip | {
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But when checking in pip freeze I get:
```
pip freeze | grep pandas
pandas==0.12.0
```
It looks like the package is in the right version, ie:
```
In [11]: import pandas as pd; pd.__version__
Out[11]: '0.13.1'
```
and only pip thinks that t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6712/events | https://github.com/pandas-dev/pandas/issues/6712 | 30,239,325 | MDU6SXNzdWUzMDIzOTMyNQ== | 6,712 | ENH: define searchsorted on a Series | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-03-26T18:42:19Z | 2016-10-12T23:04:44Z | 2014-10-05T01:02:29Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22669208/attributeerror-series-object-has-no-attribute-searchsorted-pandas
essentially a pass thru to `np.searchsorted`
- should return the index (rather than indices)
- can better handle `datetime64[ns]` (e.g. coerce input to/fro)
- could check if sorted internally (e.g. `is_monoton... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6713/events | https://github.com/pandas-dev/pandas/issues/6713 | 30,247,159 | MDU6SXNzdWUzMDI0NzE1OQ== | 6,713 | ENH: implement shift(method='head|tail') | {
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... | 12 | 2014-03-26T20:17:07Z | 2021-04-11T04:23:22Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/22670904/python-pandas-turn-absolute-periods-into-relative-periods/22671904?noredirect=1#comment34537319_22671904
this works, but I think could be more efficient on a whole frame basis
```
In [37]: def f(x):
....: y = x.dropna()
....: return Series(y.values,x.index[len... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6714/events | https://github.com/pandas-dev/pandas/issues/6714 | 30,261,979 | MDU6SXNzdWUzMDI2MTk3OQ== | 6,714 | Use of ISO time for date_range | {
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] | closed | false | null | [] | null | 1 | 2014-03-26T23:31:40Z | 2014-03-27T11:50:12Z | 2014-03-27T11:49:56Z | NONE | null | This works:
pd.Timestamp('2014-01-01 00:00 Z', tz='US/Eastern')
=> Timestamp('2013-12-31 19:00:00-0500', tz='US/Eastern')
and this works:
pd.date_range('2014-01-01 00:00', periods=1, tz='US/Eastern')
=> <class 'pandas.tseries.index.DatetimeIndex'>
[2014-01-01 00:00:00]
Length: 1, Freq: D, Timezone: US/Eastern
but t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6715/events | https://github.com/pandas-dev/pandas/issues/6715 | 30,284,400 | MDU6SXNzdWUzMDI4NDQwMA== | 6,715 | Inconsistent result with applying function between dataframe/groupby apply and 0.12/0.13/master | {
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"labels_url": "htt... | 5 | 2014-03-27T09:28:09Z | 2014-03-28T12:34:13Z | 2014-03-28T12:34:13Z | MEMBER | null | I stumbled upon an inconsistent behaviour in (groupby/dataframe) apply when updating some code from 0.12 to 0.13.
Say you have following custom function:
```
def P1(a):
try:
return np.percentile(a.dropna(), q=1)
except:
return np.nan
def P1_withouttry(a):
return np.percentile(a.dropna(), ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6716 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6716/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6716/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6716/events | https://github.com/pandas-dev/pandas/issues/6716 | 30,291,615 | MDU6SXNzdWUzMDI5MTYxNQ== | 6,716 | BUG: partial string indexing with PeriodIndex | {
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"labels_url": "htt... | 0 | 2014-03-27T11:23:10Z | 2014-05-13T14:29:35Z | 2014-05-13T14:29:35Z | CONTRIBUTOR | null | partial indexing works with time series, but not period index
```
In [27]: df2 = DataFrame(np.arange(20).reshape(10,2),index=pd.date_range('20130101 09:00',freq='T',periods=10))
In [28]: df = DataFrame(np.arange(20).reshape(10,2),index=pd.period_range('20130101 09:00',freq='T',periods=10))
In [29]: df2['20130101']
O... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6717 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6717/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6717/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6717/events | https://github.com/pandas-dev/pandas/issues/6717 | 30,294,255 | MDU6SXNzdWUzMDI5NDI1NQ== | 6,717 | ENH: add rank option to DataFrame for consistency with Series | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6718/events | https://github.com/pandas-dev/pandas/pull/6718 | 30,298,922 | MDExOlB1bGxSZXF1ZXN0MTQwNTcwNTQ= | 6,718 | BUG: Bug in consistency of groupby aggregation when passing a custom function (GH6715) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6719/events | https://github.com/pandas-dev/pandas/pull/6719 | 30,307,309 | MDExOlB1bGxSZXF1ZXN0MTQwNjE4NTc= | 6,719 | Initial implementation of holiday and holiday calendar. | {
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Currently the default dates are hard-coded in the calendar class but this should move somewhere e... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6720/events | https://github.com/pandas-dev/pandas/issues/6720 | 30,310,513 | MDU6SXNzdWUzMDMxMDUxMw== | 6,720 | OMP Error on Series experssions | {
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] | closed | false | null | [] | null | 12 | 2014-03-27T15:20:34Z | 2015-01-22T23:36:28Z | 2015-01-22T23:36:28Z | CONTRIBUTOR | null | from SO: http://stackoverflow.com/questions/23537265/inconsistent-system-error-with-pandas-0-13-series-operation/23542455#23542455
When performing some expressions involving Series returned from DataFrame column indexing (Pandas 0.13.1 with Numpy 1.8.0 in Windows 7), a new pythonw.exe console window appears (empty) bu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6721/events | https://github.com/pandas-dev/pandas/issues/6721 | 30,322,178 | MDU6SXNzdWUzMDMyMjE3OA== | 6,721 | Regression in DataFrameGroupBy.head() | {
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```
In [1]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})
```
In 0.12.0:
```
In [2]: df.groupby('... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6723/events | https://github.com/pandas-dev/pandas/issues/6723 | 30,340,370 | MDU6SXNzdWUzMDM0MDM3MA== | 6,723 | Baby names - series object has no object search sorted | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6724/events | https://github.com/pandas-dev/pandas/issues/6724 | 30,356,292 | MDU6SXNzdWUzMDM1NjI5Mg== | 6,724 | shift with named axis errors | {
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df.shift(1, axis='columns')
# TypeError: tuple indices must be integers, not str
```
np.roll is being passed the original axis
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https://api.github.com/repos/pandas-dev/pandas/issues/6725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6725/events | https://github.com/pandas-dev/pandas/pull/6725 | 30,356,406 | MDExOlB1bGxSZXF1ZXN0MTQwOTE0MzI= | 6,725 | Shift named axis | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6726/events | https://github.com/pandas-dev/pandas/issues/6726 | 30,357,677 | MDU6SXNzdWUzMDM1NzY3Nw== | 6,726 | is DataFrame.apply using axis the wrong way around? | {
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df = pd.DataFrame.from_dict({'a':'C C A'.split(),
'b':'C C A'.split(),
'c':'C A A'.split(),
'd':'C A A'.split()})
```
``` python
df.apply(len)
a 3
b 3
c 3
d 3
dtype: int64
```
``` python
df.apply(len, axis=1)
0 4
1 4
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https://api.github.com/repos/pandas-dev/pandas/issues/6727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6727/events | https://github.com/pandas-dev/pandas/issues/6727 | 30,370,628 | MDU6SXNzdWUzMDM3MDYyOA== | 6,727 | ENH:Implement .plot() for OLS Regression | {
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Let say I have some points and I do
`regression = ols(y=df['Y_VALUES'], x=df[['X_VALUES']])`
So if I type
`print (regression)`
it gives me a summary of the data.
What I am suggesting is that if I type something like:
```
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"labels_url": "htt... | 3 | 2014-03-28T14:42:29Z | 2014-07-16T08:59:31Z | 2014-03-28T15:17:31Z | CONTRIBUTOR | null | closes #6717
@jreback asked for DataFrame rank to have a percentage option to match consistency with series
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https://api.github.com/repos/pandas-dev/pandas/issues/6729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6729/events | https://github.com/pandas-dev/pandas/issues/6729 | 30,415,871 | MDU6SXNzdWUzMDQxNTg3MQ== | 6,729 | [API/ERR]: Better error message on unstack with non-unique index names | {
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"labels_url": "htt... | 0 | 2014-03-28T19:28:36Z | 2014-04-09T21:07:29Z | 2014-04-09T21:07:29Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame([1,1], index=pd.MultiIndex.from_tuples([('a', 'a'), ('a', 'b')], names=['i1', 'i1']))
In [5]: df.unstack('i1')
...
KeyError: 'Level i1 not found'
```
I can do a PR later.
Should also probably be a `ValueError` instead of a `KeyError`.
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https://api.github.com/repos/pandas-dev/pandas/issues/6730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6730/events | https://github.com/pandas-dev/pandas/pull/6730 | 30,419,815 | MDExOlB1bGxSZXF1ZXN0MTQxMjk0NDU= | 6,730 | BUG: bug in BlockManager._get_numeric_data, with invalid combine | {
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"labels_url": "htt... | 12 | 2014-03-29T03:50:09Z | 2020-10-04T00:39:33Z | 2014-05-12T12:41:17Z | CONTRIBUTOR | null | I do this
```
>>> df = pd.DataFrame([[1,np.nan,0],[1,1,1],[2,2,2],[2,3,3]], columns=list('abc'))
>>> print(df)
a b c
0 1 NaN 0
1 1 1 1
2 2 2 2
3 2 3 3
[4 rows x 3 columns]
>>> print(df.groupby('a').first())
b c
a
1 1 0
2 2 2
[2 rows x 2 columns]
```
but I expected this
```
>>> pri... | {
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```
import pandas as pd
import numpy as np
print 'numpy', np.__version__
print 'pandas', pd.__version__
df = pd.DataFrame({'x': [],'range': np.arange(0)})
t = df.sort('x').groupby('x').first()
print 'types', df['range'].dtype, t['range'].dtype
```
it is shown that after the groupby, an empty "int... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6735/events | https://github.com/pandas-dev/pandas/pull/6735 | 30,442,156 | MDExOlB1bGxSZXF1ZXN0MTQxNDE5MDQ= | 6,735 | ENH: SQL multiindex support | {
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This adds:
- fixes the `columns` argument (wasn't passed) + added test for that
- adds multi-index support in `to_sql`, by just using `reset_index` on the frame (+ tests)
- adds multi-index support in read_table (+ tests)
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Currently `drop` raises `ValueError` when non-existing label is passed. I think it is useful if `drop` has an option to suppress error and drop existing labels only.
For example, I sometimes process lots of files which has slightly different columns, and want to `drop` if data has unnecessary columns. P... | {
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We should raise a ValueError when (un)stacking and referring to a non-unique index _name_. You can still stack and unstack by position like normal (no ambiguity there, unless your names are integers and that integer is duplicated, in which case we'll raise a ValueErr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6739/events | https://github.com/pandas-dev/pandas/pull/6739 | 30,448,042 | MDExOlB1bGxSZXF1ZXN0MTQxNDQ0MTE= | 6,739 | DOC: small doc fixes (build warning in sym_diff + formatting in release notes) | {
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Converting from `datetime64[ns]` to `datetime64[m]` is disallowed, as mentioned in issue #3416, apparently because of a numpy bug. It looks to me like numpy does this correctly, however. It would be nice if this could work without resorting to raw numpy arrays:
``` python
In [89]: s = pd.Series(['2014... | {
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I'm not sure this also be a solution for #5388, but seems to be a workaround.
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closes #4069
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... | 4 | 2014-03-30T17:52:30Z | 2020-04-15T04:49:19Z | 2020-04-15T04:49:18Z | CONTRIBUTOR | null | This is an attempt to simplify/streamline internal API that has been brewing inside my head for quite a while. It does mean a significant overhaul and may take time, but it may prove worth the while. I'm putting it here for discussion ahead of time to make sure the effort isn't wasted for going in the wrong direction.
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Roadmap:
- [x] make ref_locs primary source of information (leaving items/ref_items in place to back it up and avoid breakage)
- [x] port groupby to loc-based implementation (there's quite a number of hacks a.t.m. that make this non-trivial)
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https://api.github.com/repos/pandas-dev/pandas/issues/6746 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6746/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6746/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6746/events | https://github.com/pandas-dev/pandas/pull/6746 | 30,477,119 | MDExOlB1bGxSZXF1ZXN0MTQxNTY1NjY= | 6,746 | Doc fixes | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6747 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6747/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6747/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6747/events | https://github.com/pandas-dev/pandas/pull/6747 | 30,482,104 | MDExOlB1bGxSZXF1ZXN0MTQxNTg5NDU= | 6,747 | Quick shift fixes | {
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https://gist.github.com/dalejung/9731798#file-vb_suite-log
```
-------------------------------------------------------------------------------
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https://api.github.com/repos/pandas-dev/pandas/issues/6748 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6748/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6748/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6748/events | https://github.com/pandas-dev/pandas/pull/6748 | 30,493,339 | MDExOlB1bGxSZXF1ZXN0MTQxNjQ4OTY= | 6,748 | BUG: fix NDFrame.as_blocks() for sparse containers | {
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"labels_url": "htt... | 0 | 2014-03-31T08:33:07Z | 2014-06-12T21:22:30Z | 2014-03-31T13:33:15Z | CONTRIBUTOR | null | SparseBlocks don't consolidate, so previous implementation silently dropped all but the last blocks for given dtype:
``` python
In [1]: pd.__version__
Out[1]: '0.13.1-527-g73506cb'
In [2]: pd.SparseDataFrame({'a': [1,2,3, np.nan, np.nan], 'b': [1,2,3, np.nan, np.nan]})
Out[2]:
a b
0 1 1
1 2 2
2 3 3... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6749 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6749/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6749/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6749/events | https://github.com/pandas-dev/pandas/issues/6749 | 30,513,070 | MDU6SXNzdWUzMDUxMzA3MA== | 6,749 | SparseSeries.value_counts doesn't include fill_value counts | {
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"closed_at": "2016-05-05T00:34:40Z",
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-03-31T13:50:22Z | 2016-04-29T17:17:59Z | 2016-04-29T17:17:59Z | NONE | null | Based on a suggestion from an user at stackoverflow.com, I am reporting a bug / enhancement request for sparse data frames. Please let me know if you need any more information.
Thanks in advance.
I am encountering a TypeError with a pandas sparse data frame when I use the value_counts method. I have listed the versio... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6750/events | https://github.com/pandas-dev/pandas/issues/6750 | 30,517,382 | MDU6SXNzdWUzMDUxNzM4Mg== | 6,750 | KeyError: 0L with pandas 0.13.1 | {
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"description": "Du... | closed | false | null | [] | null | 2 | 2014-03-31T14:39:15Z | 2014-03-31T14:45:28Z | 2014-03-31T14:45:13Z | NONE | null | I get the following error
``` python
Traceback (most recent call last):
File "asys.py", line 65, in <module>
compSub()
File "asys.py", line 46, in compSub
plt.hist(data['Total'],linewidth=0, alpha=.7)
File "/home/storm/Documents/lol/local/lib/python2.7/site-packages/matplotlib/pyplot.py", line 2827, in ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6751 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6751/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6751/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6751/events | https://github.com/pandas-dev/pandas/issues/6751 | 30,527,005 | MDU6SXNzdWUzMDUyNzAwNQ== | 6,751 | data frame and custom series class | {
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For example
```
>>> import pandas
>>> class XSeries(pandas.Series): pass
>>> df = pandas.DataFrame({'a': XSeries([1, 2, 3]), 'b': pandas.Series([3, 4, 5])})
>>> type(df.a)
pandas.core.series.Series
```
I would expect type(df.a) to be XSeries in the ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6752 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6752/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6752/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6752/events | https://github.com/pandas-dev/pandas/issues/6752 | 30,570,204 | MDU6SXNzdWUzMDU3MDIwNA== | 6,752 | Pandas 0.13.1 slower than 0.12.0 for pandas.datetime and strange error; expected behavior? | {
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The operation that takes a bit of time is the following:
```
self.SEEDFILE['tmp_refdate'] = self.SEEDFILE[['year','month','day','seconds']].apply(lambda x: pd.d... | {
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"labels_url": "htt... | 4 | 2014-04-01T09:34:17Z | 2019-08-15T06:44:43Z | 2014-04-01T13:18:29Z | CONTRIBUTOR | null | When calling the apply function of a DataFrame its called twice for the first row. The following code
``` python
df = DataFrame({"a":["x", "y"], "b":[1,2]})
def identity(row):
print tuple(row)
return row
df2=df.apply(identity, axis=1)
```
prints
``` python
('x', 1)
('x', 1)
('y', 2)
```
The result df2 is not a... | {
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-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timedelta_convert | 0.2253 | 71.0374 | 0.003... | {
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... | 0 | 2014-04-01T12:45:27Z | 2015-06-22T08:34:19Z | 2015-06-22T08:34:19Z | CONTRIBUTOR | null | see #6754
related #3009, #8190
from [SO](http://stackoverflow.com/questions/30894267/defining-a-function-pandas)
convering string like timedeltas, e.g.
'1 days', '3 days, 00:00:03', '3 days 2 hours'
is slow because it requires a regex match for each element. Can put cythonized (like format parsing for datetimes), ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6756/events | https://github.com/pandas-dev/pandas/pull/6756 | 30,595,559 | MDExOlB1bGxSZXF1ZXN0MTQyMjQ4Mzk= | 6,756 | DOC: documented that .apply(func) executes func twice on the first time | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6757/events | https://github.com/pandas-dev/pandas/issues/6757 | 30,595,814 | MDU6SXNzdWUzMDU5NTgxNA== | 6,757 | Possible spurious SettingWithCopyWarning | {
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Simplified dummy example:
```
In [1]: df = pd.DataFrame(np.arange(20).reshape(5,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6758 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6758/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6758/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6758/events | https://github.com/pandas-dev/pandas/issues/6758 | 30,597,587 | MDU6SXNzdWUzMDU5NzU4Nw== | 6,758 | Lists as elements of DataFrame | {
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``` python
df = DataFrame({"a":[[1,2,3], [4,5,6]], "b":[[1,2],[3,4]]})
df.iloc[0,1] = [1,20] #works
df.iloc[0,0] = [1,2,30] #throws exception
ValueError: Must have equal len keys and valuewhen setting with an i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6759/events | https://github.com/pandas-dev/pandas/pull/6759 | 30,612,024 | MDExOlB1bGxSZXF1ZXN0MTQyMzQ4Mjc= | 6,759 | PRF: Optimize 2d take operations. | {
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"labels_url": "htt... | 14 | 2014-04-01T15:56:46Z | 2014-04-28T23:35:24Z | 2014-04-28T23:35:24Z | CONTRIBUTOR | null | Wanted to get eyes on this. The original axis=1 2d methods were optimized for Fortran. I changed it to be friendly to C order and transposed the array when F ordered.
These timings are for master. `cdf` is just the consolidated dataframe to switch from C to F.
```
In [3]:
%timeit a = df.take(indexes, axis=0)
10 loop... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6760/events | https://github.com/pandas-dev/pandas/issues/6760 | 30,620,912 | MDU6SXNzdWUzMDYyMDkxMg== | 6,760 | BUG: Series.iteritems() isn't lazy | {
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"gi... | [] | closed | false | null | [] | null | 2 | 2014-04-01T17:44:41Z | 2014-04-01T20:36:23Z | 2014-04-01T20:36:23Z | CONTRIBUTOR | null | The docstring indicates that it is supposed to be lazy, but we return `lzip(iter(self.index), iter(self))`. That should be `zip(...)` right? Or is the docstring wrong?
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https://api.github.com/repos/pandas-dev/pandas/issues/6761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6761/events | https://github.com/pandas-dev/pandas/pull/6761 | 30,627,884 | MDExOlB1bGxSZXF1ZXN0MTQyNDQ2MDU= | 6,761 | BUG: Series.iteritems should be lazy | {
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"gi... | [] | closed | false | null | [] | null | 3 | 2014-04-01T19:07:10Z | 2014-06-30T14:03:50Z | 2014-04-01T20:36:23Z | CONTRIBUTOR | null | Closes #6760
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https://api.github.com/repos/pandas-dev/pandas/issues/6762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6762/events | https://github.com/pandas-dev/pandas/issues/6762 | 30,636,411 | MDU6SXNzdWUzMDYzNjQxMQ== | 6,762 | numexpr 2.3.1 error with pandas 0.13.1 | {
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"labels_url": "htt... | 24 | 2014-04-01T20:48:21Z | 2014-05-22T19:53:06Z | 2014-04-05T20:18:58Z | NONE | null | I just installed numexpr 2.3.1 with pandas 0.13.1 and got the following error:
File "C:\Python27\lib\site-packages\pandas\core\ops.py", line 496, in wrapper
arr = na_op(lvalues, rvalues)
File "C:\Python27\lib\site-packages\pandas\core\ops.py", line 443, in na_op
raise_on_error=True, *_eval_kwargs)
File "... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6763 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6763/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6763/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6763/events | https://github.com/pandas-dev/pandas/issues/6763 | 30,680,374 | MDU6SXNzdWUzMDY4MDM3NA== | 6,763 | DOC: DataFrame MERGE Example | {
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"labels_url": "htt... | 4 | 2014-04-02T11:41:40Z | 2014-04-03T12:42:23Z | 2014-04-03T12:42:23Z | CONTRIBUTOR | null | In the Docstring for the **Merge** method, the example provided:
```
merge(A, B, left_on='lkey', right_on='rkey', how='outer')
```
returns a _sorted_ DataFrame. Thus, we need to either add a **sort=True** _kwarg_ to the above code or replace the purported output with the actual (unsorted) output:
```
lkey value... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6764/events | https://github.com/pandas-dev/pandas/issues/6764 | 30,683,146 | MDU6SXNzdWUzMDY4MzE0Ng== | 6,764 | BUG: multiple grouping with a TimeGrouper requires sort | {
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"labels_url": "htt... | 2 | 2014-04-02T12:29:16Z | 2014-04-19T12:47:08Z | 2014-04-19T12:47:08Z | CONTRIBUTOR | null | resampling has been fixed, so this is only with 2 or more groupers (#6516)
```
In [9]:
df = DataFrame({
'date' : pd.to_datetime([
'20121002','20121007','20130130','20130202','20130305','20121002',
'20121207','20130130','20130202','20130305','20130202','20130305']),
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"gi... | [] | closed | false | null | [] | null | 1 | 2014-04-02T13:28:31Z | 2017-05-15T21:15:56Z | 2014-04-04T12:34:11Z | CONTRIBUTOR | null | As pointed out at https://github.com/pydata/pandas/pull/6761#issuecomment-39291213, `__getslice__` was deprecated in Py3, so the test was faulty. I switched to checking for `reverse`.
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"labels_url": "htt... | 13 | 2014-04-02T14:41:17Z | 2014-04-30T01:05:17Z | 2014-04-30T01:05:17Z | NONE | null | After a concat of two DataFrames with the same columns. I want to consolidate some data and remove NaNs in some columns by values in other columns. I ended up with a DataFrame that magically had additional columns.
This is the minimum example that I can give to reproduce the faulty behaviour using current master (70de... | {
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"labels_url": "htt... | 4 | 2014-04-02T14:58:15Z | 2015-11-14T05:27:03Z | 2014-04-05T15:58:05Z | MEMBER | null | Same background as #6736.
Currently,
- When a function is passed, any error caused by the function results in `rename` error.
- When a dict is passed, label which is not included in the axis will be silently skipped. Even though `drop` raises `ValueError` in such a case.
```
>>> df = pd.DataFrame({1: [1, 2], 'B': pd.... | {
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] | closed | false | null | [] | null | 11 | 2014-04-02T15:37:14Z | 2014-04-24T13:33:43Z | 2014-04-03T17:37:38Z | NONE | null | After "upgrading" to Xcode 5.1, I can no longer build Pandas on OS X 10.9.2. I'm wondering if anyone has successfully done so. I get the following when running build_ext:
```
python setup.py build_ext --inplace
running build_ext
skipping 'pandas/index.c' Cython extension (up-to-date)
building 'pandas.index' extension
... | {
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```
lsl_dr[lsl_dr.test_type==t].groupby('type').boxplot(column='score', grid=False)
```
generates this plot:
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-04-02T16:30:29Z | 2014-09-10T13:15:28Z | 2014-09-10T13:14:50Z | CONTRIBUTOR | null | When I tried 'DataFrame.read_csv' for the file named with local-language in Windows (the filesystem encoding is 'cp932'), it has failed with the error 'File does not exist'.
I find the line 538 in 'pandas / parser.pyx' that set filename encoding into utf-8 regardless its environment,
```
if isinstance(source, bas... | {
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... | 4 | 2014-04-02T18:34:46Z | 2021-04-11T04:24:38Z | null | CONTRIBUTOR | null | xref #8466
Can't it just return the `freq` for you instead of raising an error? Helps with duck typing.
```
[~/]
[1]: pd.version.version
[1]: '0.13.1-434-gdcbbc59'
[2]: pd.tseries.frequencies.infer_freq(pd.PeriodIndex(['2000-01-01'], freq='D'))
```
Error message
```
File "/home/skipper/src/pandas... | {
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"f... | 6 | 2014-04-02T21:45:13Z | 2020-03-11T16:23:47Z | 2019-12-28T05:11:56Z | CONTRIBUTOR | null | https://groups.google.com/forum/#!msg/pydata/LLPnzMfDlSg/OpK-mHJzJwYJ
http://stackoverflow.com/questions/22820292/how-to-use-pandas-rolling-functions-on-a-forward-looking-basis
Applying to reverse Series and reversing could work on all (?) rolling functions, I think sometimes can just do on values array, a kwarg would... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6773 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6773/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6773/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6773/events | https://github.com/pandas-dev/pandas/issues/6773 | 30,740,939 | MDU6SXNzdWUzMDc0MDkzOQ== | 6,773 | fill_value should default to 0 in add/sub/... numeric methods | {
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Instead, what pandas does is, with lack of explicit instructions, convert... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6774 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6774/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6774/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6774/events | https://github.com/pandas-dev/pandas/issues/6774 | 30,747,226 | MDU6SXNzdWUzMDc0NzIyNg== | 6,774 | read_csv: chunksize clashes with nrows | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-04-03T05:14:21Z | 2017-09-29T15:45:08Z | 2014-06-24T13:16:52Z | CONTRIBUTOR | null | If nrows are determined, the use of chunksize option does not create a TextFileReader object.
``` python
reader = pd.read_table(fname, sep=',',chunksize=4, na_values=['null'],
nrows=20)
type(reader)
```
``` bash
pandas.core.frame.DataFrame
```
My suggestion:
- Either put into docs that they ar... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6775 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6775/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6775/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6775/events | https://github.com/pandas-dev/pandas/issues/6775 | 30,747,477 | MDU6SXNzdWUzMDc0NzQ3Nw== | 6,775 | reading a slice of a DataFrame seems to alter its dtypes, which further prevents repr() | {
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I noticed that after slicing, i couldn't have a clean repr() of my DataFrame due to an altered dtype. E.g.
``` python
import pandas as pd
print pd.__version__
pl = pd.DataFrame([range(10,15), range(11,16)])
_ = pl.loc[:,1]
print pl.dtypes
_ = pl.loc[:,1:3]
print pl.dtypes
print pl[3].dtype
```
The output being:
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https://api.github.com/repos/pandas-dev/pandas/issues/6776 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6776/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6776/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6776/events | https://github.com/pandas-dev/pandas/pull/6776 | 30,770,277 | MDExOlB1bGxSZXF1ZXN0MTQzMjgzODA= | 6,776 | DOC: Issue #6763 - Corrected MERGE example | {
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"labels_url": "htt... | 3 | 2014-04-03T12:23:54Z | 2014-07-16T09:00:13Z | 2014-04-03T12:42:23Z | CONTRIBUTOR | null | Corrected the example provided in the MERGE function docstring.
Closes #6763
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https://api.github.com/repos/pandas-dev/pandas/issues/6777 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6777/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6777/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6777/events | https://github.com/pandas-dev/pandas/issues/6777 | 30,772,540 | MDU6SXNzdWUzMDc3MjU0MA== | 6,777 | Ambiguity in DataFrame.replace regex handling | {
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I think that it is confusing how DataFrame.replace interprets the to_replace values as a pure string or a regexp.
When you pass a dictionary of from and to values, these are interpreted as pure string literals and work as expected. If you pass a nested dictionary with the same values, it is interpre... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6778/events | https://github.com/pandas-dev/pandas/pull/6778 | 30,782,212 | MDExOlB1bGxSZXF1ZXN0MTQzMzU2MzE= | 6,778 | BUG: fix metacharacter replacement bug in DataFrame.replace() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6779/events | https://github.com/pandas-dev/pandas/issues/6779 | 30,786,265 | MDU6SXNzdWUzMDc4NjI2NQ== | 6,779 | Adding Period and Offset not implemented | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-04-03T15:35:40Z | 2017-11-24T10:50:44Z | 2017-11-24T10:50:08Z | CONTRIBUTOR | null | This works
```
from pandas.tseries.frequencies import to_offset
pd.Timestamp('1990-9-30') + 2*to_offset('Q')
```
This doesn't. Shouldn't it? Workaround is to convert with `to_timestamp`.
```
pd.Period('1990q1') + 2*to_offset('Q')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6780/events | https://github.com/pandas-dev/pandas/issues/6780 | 30,786,795 | MDU6SXNzdWUzMDc4Njc5NQ== | 6,780 | Cannot create DatetimeIndex using Period | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-04-03T15:41:25Z | 2014-09-29T22:07:51Z | 2014-09-29T22:07:51Z | CONTRIBUTOR | null | This doesn't work. Looks like `start` and `end` should try a bit harder to convert to timestamp?
```
from datetime import datetime
pd.DatetimeIndex(start=pd.Period('1990q1'), end=datetime(1992, 12, 31), freq='Q')
```
This does.
```
pd.DatetimeIndex(start=pd.Period('1990q1').to_timestamp(), end=datetime(1992, 12, 31... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6781/events | https://github.com/pandas-dev/pandas/issues/6781 | 30,794,104 | MDU6SXNzdWUzMDc5NDEwNA== | 6,781 | DataFrame.convert_objects(copy=False) is ignored | {
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It should be:
1. either mentioned in the documentation (as #3436 suggest),
2. enforce it, or
3. drop the kw-arg.
``` python
>>> import pandas as pd
>>> df = pd.DataFrame([['1',2], [3,'4']])
>>> ... | {
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TypeError("NAN/INF not supported in write_number()")
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In [24]: x = Series(np.around(randn(10) * 100).astype('timedelta64[ns]'))
In [25]: x
Out[25]:
0 -00:00:00.000000
1 00:00:00.000000
2 -00:00:00.000000
3 -00:00:00.000000
4 00:00:00.000000
5 -00:00:00.000000
6 -00:00:00.000000
7 -00:00:00.000000
8 -00:00:00.000000
9 -00:00:00.000000
dtype: time... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6785 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6785/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6785/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6785/events | https://github.com/pandas-dev/pandas/issues/6785 | 30,803,675 | MDU6SXNzdWUzMDgwMzY3NQ== | 6,785 | Inconsistent behavior when setting DataFrame index to datetime Series | {
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```
df = pandas.DataFrame([{'ts':datetime(2014, 4, 1, tzinfo=pytz.utc), 'foo':1}])
```
This works fine:
```
df = df.set_index('ts')
print(df)
```
```
foo
ts
2014-04-01 00:00:00+00:00 1
```
This yields an AttributeError:
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want to run all built-in functions (e.g. `first,last,nth,sum,min`...etc with
large number of groups, say 10000 to verify perf.
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"labels_url": "htt... | 10 | 2014-04-03T20:55:05Z | 2014-04-03T22:18:18Z | 2014-04-03T22:11:49Z | NONE | null | The following code errors out. Seems like it should just return the original dataframe.
pandas: 0.13.1-552-g8120a59
``` python
import pandas as pd
import numpy as np
from itertools import product
attributes = ['Attribute' + str(i) for i in range(1)]
attribute_values = ['Value' + str(i) for i in range(1000)]
index = ... | {
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The following code snippets demonstrates the problem.
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.13.1'
In [3]: df = pd.DataFrame(["... | {
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... | 6 | 2014-04-04T00:59:52Z | 2021-04-11T04:25:28Z | null | CONTRIBUTOR | null | atm we're checking dict-like by doing isinstance(x, dict), I think we should be using collections.MutableMapping, slightly more general.
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```
In [4]: df = web.get_data_yahoo('GOOG')
In [5]: df
Out[5]:
Open High Low Close Volume Adj Close
Date
2014-03-27 568.00 568.00 552.92 558.46 13100 558.46
2014-03-28 561.20 ... | {
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```
import pandas as pd
import numpy as np
a=pd.DataFrame(np.random.random((3,1)))
excel_writer=pd.ExcelWriter('c:\excel.xlsx')
a.to_excel(excel_writer, 'a1')
excel_writer.save()
excel_writer=pd.ExcelWriter('c:\excel.xlsx')
a.to_excel(excel_writer, 'a2'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6795/events | https://github.com/pandas-dev/pandas/issues/6795 | 30,833,043 | MDU6SXNzdWUzMDgzMzA0Mw== | 6,795 | BUG: rolling_max with center=True and min_periods=1 returns incorrect results | {
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```
Python 3.3.5 (default, Mar 10 2014, 03:21:31)
[GCC 4.8.2 20140206 (prerelease)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df=pd.DataFrame([0,1,2,1,0], columns=['a']
### This is OK
>>> p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6796/events | https://github.com/pandas-dev/pandas/issues/6796 | 30,842,180 | MDU6SXNzdWUzMDg0MjE4MA== | 6,796 | BUG/API: converting invalid column names in to_sql | {
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"labels_url": "htt... | 11 | 2014-04-04T08:27:52Z | 2014-04-22T14:33:16Z | 2014-04-22T14:33:16Z | MEMBER | null | At the moment, with the new sql code, there is a bug in the conversion of invalid column names (trough the function `_safe_col_name`). Eg:
```
In [1]: import sqlalchemy
In [2]: engine = sqlalchemy.create_engine('sqlite:///:memory:')
In [4]: df = pd.DataFrame({'safe_col':[1,2], 'invalid col':[3,4]})
In [7]: pd.io.sql.t... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-04-04T08:31:43Z | 2015-11-08T19:46:21Z | 2015-11-08T19:45:26Z | NONE | null | Using pandas 0.13.1 ...
When df is a 2-level datetime indexed dataframe, to_csv prints 00:00 following the dates, even when you specify date_format = '%Y-%m-%d'.
df.to_csv(date_format = '%Y-%m-%d')
To get around this, I'm currently having to use reset_index; then to_csv works as expected.
```
In [4]: df = DataFram... | {
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"followers_url": "https://api.github.com/users/jorisva... | 17 | 2014-04-04T13:02:45Z | 2021-05-10T16:24:32Z | 2014-07-07T16:55:21Z | NONE | null | The `pandas.io.sql` module has a convenient `read_frame` function which has been of great use to me. The function has an argument `coerce_float`. I propose to make that more general akin to the `pandas.DataFrame` initialization method which has an argument `dtype`. This would allow passing a `dict` with column names as... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6799/events | https://github.com/pandas-dev/pandas/pull/6799 | 30,862,921 | MDExOlB1bGxSZXF1ZXN0MTQzODM5OTk= | 6,799 | BUG: duplicate (getitem) indexing with iloc (GH6766) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6800/events | https://github.com/pandas-dev/pandas/issues/6800 | 30,874,454 | MDU6SXNzdWUzMDg3NDQ1NA== | 6,800 | Removing duplicated rows of 2-level datetime index dataframe | {
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... | [] | closed | false | null | [] | null | 2 | 2014-04-04T16:22:25Z | 2014-07-07T15:49:51Z | 2014-07-07T15:49:51Z | NONE | null | Using pandas 0.13.1
I have a dataframe with three columns. The first two contain datetimes and the third floats (the entire data frame originates from df = pandas.io.sql.frame_query(...,...) ).
I need to remove rows for which dates in the first two columns are duplicated. So something like the following would seem t... | {
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