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} | 19 | 2014-08-02T01:10:16Z | 2014-08-05T17:14:00Z | 2014-08-05T17:14:00Z | CONTRIBUTOR | null | Example is below. I would expect that if dates have a timezone on them, date_range would then use that timezone to fill in the rest of the period. However, something goes awry (notice the 01:00 below). If I have dates with a timezone and pass a timezone (case 2), it still doesn't work. Only when I remove the timezone from the dates does it work (case 3). I would expect all these to work the same.
```
import pytz
tz = pytz.timezone('US/Eastern')
from datetime import datetime
sd = tz.localize(datetime(2014, 3, 6))
ed = tz.localize(datetime(2014, 3, 12))
list(pd.date_range(sd, ed, freq='D'))
Out[41]:
[Timestamp('2014-03-06 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-07 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-08 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-09 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-10 01:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-11 01:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-12 01:00:00-0400', tz='US/Eastern', offset='D')]
list(pd.date_range(sd, ed, freq='D', tz='US/Eastern'))
Out[42]:
[Timestamp('2014-03-06 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-07 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-08 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-09 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-10 01:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-11 01:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-12 01:00:00-0400', tz='US/Eastern', offset='D')]
list(pd.date_range(sd.replace(tzinfo=None), ed.replace(tzinfo=None), freq='D', tz='US/Eastern'))
Out[43]:
[Timestamp('2014-03-06 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-07 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-08 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-09 00:00:00-0500', tz='US/Eastern', offset='D'),
Timestamp('2014-03-10 00:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-11 00:00:00-0400', tz='US/Eastern', offset='D'),
Timestamp('2014-03-12 00:00:00-0400', tz='US/Eastern', offset='D')]
```
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} | 3 | 2014-08-02T07:21:11Z | 2014-08-02T13:06:56Z | 2014-08-02T12:53:36Z | MEMBER | null | closes #7857... this time it checks also for a Series (and it passes all tests).
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} | 5 | 2014-08-02T09:27:50Z | 2014-08-02T22:13:59Z | 2014-08-02T12:59:20Z | CONTRIBUTOR | null | html test_encode fails on OSX 10.9.4 due to missing dash in `utf32` encoding
string.
The test encoding string is derived from a split of the test file name.
Only utf32 is affected but this commit changes the utf8 and utf16 containing filenames as well for the sake of consistency.
``` ======================================================================
ERROR: test_encode (pandas.io.tests.test_html.TestReadHtmlEncodingLxml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jmorris/Code/pandas/pandas/io/tests/test_html.py", line 624, in test_encode
from_string = self.read_string(f, encoding).pop()
File "/Users/jmorris/Code/pandas/pandas/io/tests/test_html.py", line 619, in read_string
return self.read_html(fobj.read(), encoding=encoding, index_col=0)
File "/Users/jmorris/Code/pandas/pandas/io/tests/test_html.py", line 607, in read_html
return read_html(*args, **kwargs)
File "/Users/jmorris/Code/pandas/pandas/io/html.py", line 851, in read_html
parse_dates, tupleize_cols, thousands, attrs, encoding)
File "/Users/jmorris/Code/pandas/pandas/io/html.py", line 714, in _parse
raise_with_traceback(retained)
File "/Users/jmorris/Code/pandas/pandas/io/html.py", line 708, in _parse
tables = p.parse_tables()
File "/Users/jmorris/Code/pandas/pandas/io/html.py", line 178, in parse_tables
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
File "/Users/jmorris/Code/pandas/pandas/io/html.py", line 527, in _build_doc
parser = HTMLParser(recover=False, encoding=self.encoding)
File "/Users/jmorris/anaconda/envs/py27/lib/python2.7/site-packages/lxml/html/__init__.py", line 1664, in __init__
super(HTMLParser, self).__init__(**kwargs)
File "parser.pxi", line 1598, in lxml.etree.HTMLParser.__init__ (src/lxml/lxml.etree.c:100669)
File "parser.pxi", line 792, in lxml.etree._BaseParser.__init__ (src/lxml/lxml.etree.c:93393)
LookupError: unknown encoding: 'utf32'
```
Test failure was not resolved by building the most recent lxml with static dependencies and most recent versions of libxml2 and libxslt.
I contacted the lxml mailing list.
http://mailman-mail5.webfaction.com/pipermail/lxml/2014-July/007239.html
It was suggested that problem may be in OSX libiconv though `iconv -l`doesn't show the bug on my system.
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} | 1 | 2014-08-02T13:36:37Z | 2014-08-11T15:30:02Z | 2014-08-07T14:27:34Z | CONTRIBUTOR | null | followup from #7891
- [x] tests for `argmin/argmax`, #7951
- [x] doc-strings for properties / functions that are ndarray compat, #7891, #7951
- [x] `any/all` doc-strings (series/index) (unbox already does this)
- [ ] `searchsorted`, #7447
- [x] `Index.__nonzero__` should raise ? #7897, fixed in #7951
- [x] see if can figure out `sortorder` (e.g. in MultiIndex.repeat), leaving for now
- [x] defined `nbytes` (for index its ok), for NDFrame this should actually return the memory of data + index memory (#6852), done in #7891
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} | 10 | 2014-08-02T14:56:55Z | 2015-05-06T17:38:09Z | 2015-05-05T22:27:56Z | MEMBER | null | Closes #2469. Create `BusinessHour` offset to specify business hours on `BusinessDay`. Appreciated if any feedbacks regarding bahaviours, keywords, etc.
### Basic
By default, use 9:00 - 17:00 as business hours. Adding `BusinessHour` will increment timestamp by hourly on the days belong to `BusinessDay` offset.
```
bh = pd.offsets.BusinessHour()
repr(bh)
# <BusinessHour: BH=09:00-17:00>
pd.Timestamp('2014-08-01 10:00') + bh
#2014-08-01 11:00:00
pd.Timestamp('2014-08-01 08:00') + bh
#2014-08-01 10:00:00
# move to next Business Day
pd.Timestamp('2014-08-01 19:00') + bh
#2014-08-04 10:00:00
# if exceeds the closing time, remaining are added to next day
pd.Timestamp('2014-08-01 16:45') + bh
#2014-08-04 09:45:00
# if n != 1
pd.Timestamp('2014-08-01 10:00') + pd.offsets.BusinessHour(2)
#2014-08-01 12:00:00
pd.Timestamp('2014-08-01 10:00') + pd.offsets.BusinessHour(-3)
#2014-07-31 15:00:00
# Create Index
pd.date_range('2014-07-08 10:00', freq='BH', periods=40)
# [2014-07-08 10:00:00, ..., 2014-07-14 17:00:00]
# Length: 40, Freq: BH, Timezone: None
```
### Specify Opening/Closing Hour
Allow to specify opening/closing time using `start` and `end` keyword by `hour:minute` string or `datetime.time`.
```
bh = pd.offsets.BusinessHour(start='11:00', end=datetime.time(20, 0))
repr(bh)
# <BusinessHour: BH=11:00-20:00>
pd.Timestamp('2014-08-01 10:00') + bh
#2014-08-01 12:00:00
pd.Timestamp('2014-08-01 13:00') + bh
#2014-08-01 14:00:00
pd.Timestamp('2014-08-01 19:00') + bh
#2014-08-04 11:00:00
# if end < start, it will be midnight business hour
bh = pd.offsets.BusinessHour(start='17:00', end='9:00')
repr(bh)
# <BusinessHour: BH=17:00-09:00>
pd.Timestamp('2014-07-31 10:00') + bh
#2014-07-31 18:00:00
pd.Timestamp('2014-07-31 23:00') + bh
#2014-08-01 00:00:00
pd.Timestamp('2014-07-31 01:00') + bh
#2014-07-31 02:00:00
pd.Timestamp('2014-08-01 13:00') + bh
#2014-08-01 18:00:00
```
### Edge cases
`onOffset` should include both edges.
```
pd.offsets.BusinessHour().onOffset(pd.Timestamp('2014-08-01 09:00'))
# True
pd.offsets.BusinessHour().onOffset(pd.Timestamp('2014-08-01 17:00'))
# True
```
If result is on the end-edge of business hour, move to next
```
# not 2014-08-01 17:00
pd.Timestamp('2014-08-01 16:00') + pd.offsets.BusinessHour()
#2014-08-04 09:00
# not 2014-08-01 09:00
pd.Timestamp('2014-08-01 10:00') - pd.offsets.BusinessHour()
#2014-07:31 17:00
```
In case of midnight business hour, distinguish the date by its opening hour
```
#2014-08-02 is Saturday, but handled as valid because its business hour starts on 08-01
pd.Timestamp('2014-08-02 01:00') + pd.offsets.BusinessHour(start='17:00', end='9:00')
#2014-08:02 02:00
```
### Remainings:
- [x] doc
- impl & tests
- [x] Index creation
- [x] Confirm better conditions whether inferred_freq results in `BH` or `H`
- [x] `normalize`
- ~~tsplot~~ (This can't be done without #5148, skipped)
- [x] Correct edge handling for `rollback` and `rollforward`
- [x] handling `nanosecond` (minor issue, but current `apply_wraps` may add preserved `nanosecond` after the closing time)
- tests
- [x] `n` larger than business shours
- [x] Specifying `start` and `end`
- [x] midnight business hours
- [x] Error cases for initialization
- [x] Better fix for `test_apply_out_of_range` timezone check, which fails because of DST difference.
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https://api.github.com/repos/pandas-dev/pandas/issues/7906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7906/events | https://github.com/pandas-dev/pandas/pull/7906 | 39,357,279 | MDExOlB1bGxSZXF1ZXN0MTkyNDk3NjU= | 7,906 | DOC: grammar fixes in docs | {
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| null | 1 | 2014-08-02T21:06:10Z | 2015-04-29T15:34:09Z | 2014-08-02T22:47:36Z | CONTRIBUTOR | null | {
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} | 7 | 2014-08-03T12:45:12Z | 2014-08-12T22:11:41Z | 2014-08-11T12:17:58Z | MEMBER | null | Fixes 2 problems related to `Timestamp` string parsing:
- `Timestamp` parsing results incorrect if input contains offset string (Closes #7833).
NOTE: Also modified `Timestamp.__repr__` to display fixed timezone info, because this can be either `pytz` or `dateutil`.
```
# Result after the fix
# If string contains offset, it will be parsed using fixed timezone offset. Following results in 2013-11-01 05:00:00 in UTC (There is some existing tests checking this behaviour).
repr(pd.Timestamp('2013-11-01 00:00:00-0500'))
# Timestamp('2013-11-01 00:00:00-0500', tz='pytz.FixedOffset(-300)')
# If tz is specified simultaneously, it should convert the timezone.
repr(pd.Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago'))
# Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago')
repr(pd.Timestamp('2013-11-01 00:00:00-0500', tz='Asia/Tokyo'))
# Timestamp('2013-11-01 14:00:00+0900', tz='Asia/Tokyo')
```
- `Timestamp` loses `nanosecond` info when parsing from `str`. (Closes #7878)
```
# Result after the fix
repr(pd.Timestamp('2001-01-01 00:00:00.000000005'))
# Timestamp('2001-01-01 00:00:00.000000005')
repr(pd.Timestamp('2001-01-01 00:00:00.000000005', tz='US/Eastern'))
# Timestamp('2001-01-01 00:00:00.000000005-0500', tz='US/Eastern')
repr(pd.Timestamp('2001-01-01 00:00:00.000001234+09:00'))
# Timestamp('2001-01-01 00:00:00.000001234+0900', tz='pytz.FixedOffset(540)')
repr(pd.Timestamp('2001-01-01 00:00:00.000001234+09:00', tz='Asia/Tokyo'))
# Timestamp('2001-01-01 00:00:00.000001234+0900', tz='Asia/Tokyo')
# Because non ISO 8601 format is parsed by dateutil, nanosecond will lost (no change)
repr(pd.Timestamp('01-01-01 00:00:00.000000001'))
# Timestamp('2001-01-01 00:00:00')
repr(pd.Timestamp('01-01-01 00:00:00.000000001+9:00'))
# Timestamp('2001-01-01 00:00:00+0900', tz='tzoffset(None, 32400)')
```
CC @cyber42 @ischwabacher @rockg @adamgreenhall
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| null | 1 | 2014-08-03T18:06:21Z | 2014-08-03T19:25:45Z | 2014-08-03T19:25:45Z | NONE | null | Looks like the package builder needs to ignore backup files.
tar tf /sw/src/pandas-0.14.1.tar.gz | grep '~' | head
pandas-0.14.1/doc/sphinxext/numpydoc.py~
pandas-0.14.1/doc/sphinxext/**init**.py~
pandas-0.14.1/doc/sphinxext/docscrape_sphinx.py~
pandas-0.14.1/doc/source/gotchas.rst~
pandas-0.14.1/doc/source/ecosystem.rst~
pandas-0.14.1/doc/source/enhancingperf.rst~
pandas-0.14.1/doc/source/release.rst~
pandas-0.14.1/doc/source/v0.4.x.txt~
pandas-0.14.1/doc/source/v0.9.1.txt~
pandas-0.14.1/doc/source/tutorials.rst~
tar tf /sw/src/pandas-0.14.1.tar.gz | grep '~' | wc -l
229
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} | 6 | 2014-08-03T19:40:39Z | 2014-08-05T17:14:05Z | 2014-08-05T17:14:01Z | CONTRIBUTOR | null | Below fixes date_range when input dates are localized. In that case inferred_freq is not None and so the dates do not have their tzinfo removed. This causes a fixed offset to be applied when the range is created. If tzinfo is removed, they will be correctly localized.
Fixes #7901
Fixes #7835.
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} | 6 | 2014-08-03T21:20:30Z | 2015-04-29T15:33:46Z | 2014-09-04T00:42:46Z | CONTRIBUTOR | null | I'm not 100% sure this is the best way to implement this, but I think it'd be nice for GroupBy.nth() to support selecting multiple values. For instance if we have
```
df = DataFrame(1, index=pd.date_range(start='1/1/2013', end='6/30/2014', freq='B'), columns=['a', 'b'])
```
and currently we can easily get the nth entry for each month by:
```
df.groupby((df.index.year, df.index.month)).nth(0)
```
however we can't easily get, for instance, the first 5 entries for each group without having a more complex loop in the user code. If nth() accepts a list of integers we would be able to simply do
```
df.groupby((df.index.year, df.index.month)).nth(range(5))
```
This PR makes the above work when there's no dropna. I can't quite figure out the section where dropna is handled, and therefore I'm currently requiring that no dropna be passed when n is a list of values. I'm happy to improve this and also enhance the tests. Please let me know your feedback. Thanks.
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} | 3 | 2014-08-04T00:48:56Z | 2014-09-10T00:02:26Z | 2014-09-10T00:02:26Z | CONTRIBUTOR | null | Currently `ewma()` has an `adjust` parameter (affecting how the weights are calculated) whereas all the other `ewm*()` functions do not. When they call `ewma()`, `adjust` is implicitly set to its default value, `True`. This is probably fine in most cases, but if `ewma()` is going to expose the `adjust` parameter, the other `ewm*()` should as well.
For clarity, here's the description of the `adjust` parameter in `_ewm_notes`:
```
When adjust is True (default), weighted averages are calculated using weights
(1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1.
When adjust is False, weighted averages are calculated recursively as:
weighted_average[0] = arg[0];
weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].
```
On a related note, I'm not sure what the policy/practice is for reordering parameters, but if it's not too strict I'd like to reorder the `ewm*()` parameters just a little so that (i) `freq` and `how` are next to each other, and (ii) `adjust` and `ignore_na` are next to each other.
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} | 4 | 2014-08-04T01:27:58Z | 2014-09-10T00:02:26Z | 2014-09-10T00:02:26Z | CONTRIBUTOR | null | `ewmvar()` and `ewmcov()` contain the following code:
```
if not bias:
result *= (1.0 + 2.0 * com) / (2.0 * com)
```
Where does this scaling formula come from? Does the number of observations not enter in to the bias of the (sample) variance estimation? I would imagine that perhaps the formula above is for the case that have an infinite sample, but for a finite sample I would have thought that the number of observations would enter into things.
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} | 14 | 2014-08-04T03:09:11Z | 2014-08-19T19:33:03Z | 2014-08-11T12:57:15Z | CONTRIBUTOR | null | I am working on a financial calendar and wanted to add the following functions to pandas.tseries.holiday in order to properly handle half days.
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} | 3 | 2014-08-04T04:51:34Z | 2014-08-04T18:59:52Z | 2014-08-04T15:17:33Z | NONE | null | I submit that this should be a valid pandas DataFrame, odd though it may appear
```
df = DataFrame([[np.mean, np.median],['mean','median']],
columns=MultiIndex.from_tuples([('functs','mean'),
('functs','median')]),
index=['function', 'name'])
```
It looks like this:
```
In [45]: print df
functs
mean median
function <function mean at 0x33fb8c0> <function median at 0x34fac80>
name mean median
```
However, it can't always be indexed using the `.ix` or `.loc` indexers.
This works as expected:
```
print df[('functs','mean')]['function']
```
As does this:
```
print df.loc['name'][('functs','mean')]
```
But this raises an AttributeError, because `np.mean` does not have an `ndim` attribute, but the indexing code assumes that it does:
```
print df.loc['function'][('functs','mean')]
```
The traceback is as follows:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-43-95d403db0b5a> in <module>()
----> 1 df.loc['function',('functs','mean')]
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1140 def __getitem__(self, key):
1141 if type(key) is tuple:
-> 1142 return self._getitem_tuple(key)
1143 else:
1144 return self._getitem_axis(key, axis=0)
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
653 def _getitem_tuple(self, tup):
654 try:
--> 655 return self._getitem_lowerdim(tup)
656 except IndexingError:
657 pass
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
761 # we may have a nested tuples indexer here
762 if self._is_nested_tuple_indexer(tup):
--> 763 return self._getitem_nested_tuple(tup)
764
765 # we maybe be using a tuple to represent multiple dimensions here
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _getitem_nested_tuple(self, tup)
842 # has the dim of the obj changed?
843 # GH 7199
--> 844 if obj.ndim < current_ndim:
845
846 # GH 7516
AttributeError: 'function' object has no attribute 'ndim'
```
This worked for me until I upgraded to 0.14.1. Either it is a bug or I am wrong that DataFrames are type-agnostic containers. (In the latter case, it seems that some kind of error-handling should be in place to check for disallowed datatypes.)
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} | 6 | 2014-08-04T05:34:58Z | 2014-08-07T20:47:42Z | 2014-08-07T20:47:32Z | CONTRIBUTOR | null | Closes #7846
New `level` argument for `DataFrame.tz_localize()` and `DataFrame.tz_convert()`, needed for a DataFrame with MultiIndex:
```
tz_convert(self, tz, axis=0, level=None, copy=True)
tz_localize(self, tz, axis=0, level=None, copy=True, infer_dst=False)
```
@jreback Not sure if `test_generic.py` is the right place to add the tests.
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} | 9 | 2014-08-04T05:47:44Z | 2014-09-16T17:31:21Z | 2014-09-15T06:24:01Z | CONTRIBUTOR | null | Added logic to `rolling_var` to detect windows where all non-NaN
values are identical.
Need to assess both correctness and performance impact.
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| null | 0 | 2014-08-04T07:57:10Z | 2014-08-04T14:11:01Z | 2014-08-04T14:11:01Z | MEMBER | null | This fixes some doc build errors introduced by the categorical docs.
There were also some other failures, but I will open an issue for those (don't directly see the solution for them).
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} | 5 | 2014-08-04T08:04:39Z | 2014-08-04T20:54:19Z | 2014-08-04T20:29:34Z | MEMBER | null | After #7917 still some errors in the categorical docs:
- Docs say "Writing data to a HDF store works", but `df.to_hdf(hdf_file, "frame")` gives a `TypeError: objects of type ``Categorical`` are not supported in this context, sorry` (http://pandas-docs.github.io/pandas-docs-travis/categorical.html#getting-data-in-out). Also `pd.read_hdf(hdf_file, "frame")` gives an error.
- In the section about indexing, `df.ix["h":"j",0:1]` gives a `AttributeError: 'DataFrame' object has no attribute '_is_view'` (http://pandas-docs.github.io/pandas-docs-travis/categorical.html#getting)
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| null | 2 | 2014-08-04T11:15:15Z | 2015-10-20T22:30:08Z | 2015-10-20T22:30:08Z | NONE | null | I know that Series.plot() of dataframe.plot() has sharey and sharex arguments, but would'nt it be nicer if it wasnt only boolean, but also could be used to set axe which should be common ?
Example:
```
fig, axes = plt.subplots(3,2)
seriesA.plot(ax=axes[1][0])
seriesB.plot(ax= axes[2][1], sharey=axes[1][0])
```
even though matplotlib's plot has that kind of argument, i cannot acces it via kwds because sharey and sharex are already used as arguments in pandas
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| null | 0 | 2014-08-04T13:16:01Z | 2014-08-07T22:13:28Z | 2014-08-04T14:10:46Z | MEMBER | null | #7798 was added to incorrect section.
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| null | 7 | 2014-08-04T13:41:31Z | 2014-12-04T09:52:18Z | 2014-08-06T21:17:17Z | CONTRIBUTOR | null | Reports are starting to trickle in of failing statsmodels tests on pandas 0.14.0. I haven't had a chance to see what the problem is yet.
https://github.com/statsmodels/statsmodels/issues/1822
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https://api.github.com/repos/pandas-dev/pandas/issues/7923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7923/events | https://github.com/pandas-dev/pandas/pull/7923 | 39,428,661 | MDExOlB1bGxSZXF1ZXN0MTkyODcxMDY= | 7,923 | BUG: Fix Grouper with multi-level index and frequency (GH7885) | {
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} | 13 | 2014-08-04T15:00:30Z | 2014-09-08T04:26:23Z | 2014-09-07T14:30:53Z | CONTRIBUTOR | null | close #7885
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} | 3 | 2014-08-04T15:01:57Z | 2014-08-04T20:29:34Z | 2014-08-04T20:29:34Z | CONTRIBUTOR | null | Categoricals now raise NotImplementedError when writing to HDFStore with a Fixed type store
Slicing bug with a single-dtyped category and a possible view
closes #7918
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} | 17 | 2014-08-04T16:29:44Z | 2014-09-15T04:45:33Z | 2014-08-19T14:47:20Z | NONE | null | Hello,
I have found some incorrect behavior in the pd.rolling_\* functions regarding the combination of 'center' and 'min_periods' arguments. I believe this is an issue for all rolling_\* functions. Here is an example using rolling_mean:
When using only the 'center' argument, the behavior works as expected:
``` python
In [5]: df = pd.DataFrame(range(10))
In [6]: df
Out[6]:
0
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
In [7]: pd.rolling_mean(df, window=3, center=True)
Out[7]:
0
0 NaN
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 NaN
```
When using the 'min_periods' argument, the behavior also works as expected:
``` python
In [8]: pd.rolling_mean(df, window=3, min_periods=1)
Out[8]:
0
0 0.0
1 0.5
2 1.0
3 2.0
4 3.0
5 4.0
6 5.0
7 6.0
8 7.0
9 8.0
```
When combining both the 'center' and 'min_periods' arguments, however, the result is incorrect. The last entry should be 8.5 I believe.
``` python
In [9]: pd.rolling_mean(df, window=3, center=True, min_periods=1)
Out[9]:
0
0 0.5
1 1.0
2 2.0
3 3.0
4 4.0
5 5.0
6 6.0
7 7.0
8 8.0
9 NaN
```
``` python
In [10]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.0
statsmodels: 0.6.0.dev-bed3499
IPython: 2.0.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.3.1
numexpr: 2.0.1
matplotlib: 1.3.1
openpyxl: 1.5.8
xlrd: 0.6.1
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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} | 42 | 2014-08-04T16:34:07Z | 2014-09-10T00:11:00Z | 2014-09-10T00:02:26Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7911.
Closes https://github.com/pydata/pandas/issues/7912.
Closes https://github.com/pydata/pandas/issues/7977.
Closes https://github.com/pydata/pandas/issues/7900.
These are all fixed simultaneously in order to enable consistency testing across the various functions.
Enhancement -- add `adjust` to `ewm*()` (https://github.com/pydata/pandas/issues/7911):
This code adds `adjust` arguments to `ewmvar`, `ewmstd`, `ewmvol`, `ewmcov`, and `ewmcorr` (`ewma` already had it). If ok, I'd like to reorder the parameters to be more logical and consistent, but for now I just added `adjust=True` to the end of the arguments list.
Bug fix -- `ewmvar/std/cov` debiasing factors (https://github.com/pydata/pandas/issues/7912):
This code corrects the bias correction factor for exponentially weighted (co)variance calculations. The prior factor was the asymptotic value for an infinite number of observations.
As a result of this change, `ewmvar/cov` will now return `NaN` for the (co)variance of a single value when `bias=False`, and `0` when `bias=True`. Previously they always returned `0` for a single value. See also https://github.com/pydata/pandas/issues/7900.
API -- meaning of `min_periods` (https://github.com/pydata/pandas/issues/7977):
The meaning of the `min_periods` argument to the `ewm*()` functions is now consistent with that of the `expanding_*()` functions, i.e. an entry will be `NaN` if the count of non-`NaN` input values through that point is `<min_periods`. Note that in view of this change, https://github.com/pydata/pandas/issues/7884 / https://github.com/pydata/pandas/pull/7898 is no longer relevant.
Bug fix -- `rolling/expanding_var/std` for a single value (https://github.com/pydata/pandas/issues/7900)
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} | 27 | 2014-08-04T17:44:53Z | 2016-10-12T23:05:04Z | 2014-08-14T19:41:22Z | CONTRIBUTOR | null | @cpcloud not sure if this is something I did (or didn't do)
I was testing the index sub-class on 3.4 (may have appeared on travis too)
`/mnt/home/jreback/venv/py3.4/index/pandas/io/tests/data/html_encoding/chinese_utf-16.html'`
```
======================================================================
ERROR: test_encode (pandas.io.tests.test_html.TestReadHtmlEncoding)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/home/jreback/venv/py3.4/index/pandas/io/tests/test_html.py", line 627, in test_encode
from_string = self.read_string(f, encoding).pop()
File "/mnt/home/jreback/venv/py3.4/index/pandas/io/tests/test_html.py", line 622, in read_string
return self.read_html(fobj.read(), encoding=encoding, index_col=0)
File "/mnt/home/jreback/venv/py3.4/index/pandas/io/tests/test_html.py", line 610, in read_html
return read_html(*args, **kwargs)
File "/mnt/home/jreback/venv/py3.4/index/pandas/io/html.py", line 843, in read_html
parse_dates, tupleize_cols, thousands, attrs, encoding)
File "/mnt/home/jreback/venv/py3.4/index/pandas/io/html.py", line 709, in _parse
raise_with_traceback(retained)
File "/mnt/home/jreback/venv/py3.4/index/pandas/compat/__init__.py", line 705, in raise_with_traceback
raise exc.with_traceback(traceback)
TypeError: Cannot read object of type 'bytes'
----------------------------------------------------------------------
Ran 64 tests in 61.014s
FAILED (errors=1)
(py3.4)jreback@sheep:~/venv/py3.4/index$ nosetests pandas//io/tests/test_html.py --pdb --pdb-failure^C
(py3.4)jreback@sheep:~/venv/py3.4/index$ python ci/print_versions.py
INSTALLED VERSIONS
------------------
commit: d1c4fbb0d170cfaf920a27907c014e8cc45752d1
python: 3.4.0.beta.3
python-bits: 64
OS: Linux
OS-release: 2.6.32-5-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: None
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.0
scipy: 0.13.3
statsmodels: None
IPython: None
sphinx: None
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 2.0.4
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.6
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.6
pymysql: 0.6.1.None
psycopg2: 2.5.2 (dt dec pq3 ext)
```
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} | 5 | 2014-08-04T18:02:16Z | 2016-07-26T05:10:56Z | 2015-03-02T11:53:04Z | MEMBER | null | closes #7889
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} | 11 | 2014-08-04T20:13:40Z | 2014-08-12T19:24:44Z | 2014-08-10T03:47:08Z | CONTRIBUTOR | null | I was about to answer an SO question giving the standard "use `lambda` or `functools.partial`" to create a new function with bound arguments when to my surprise my example didn't work with the OP's code. After some experimentation, it turns out to be because we don't always get the same return type. For example:
```
df = pd.DataFrame(np.arange(5, dtype=np.int64),
index=pd.DatetimeIndex(start='2014/01/01', periods=5, freq='d'))
def f(x,a=1):
print(type(x))
return int(isinstance(x, pd.DataFrame)) + 1000*a
```
After which:
```
>>> df.resample("M", how=f)
<class 'pandas.core.series.Series'>
0
2014-01-31 1000
>>> df.resample("M", how=lambda x: f(x,5))
<class 'pandas.core.series.Series'>
0
2014-01-31 5000
>>> df.resample("M", how=partial(f, a=9))
<class 'pandas.core.frame.DataFrame'>
0
2014-01-31 9001
```
`how` shouldn't care about how the function was constructed, only whether it's callable, and we should be getting the same result (whether Series or DataFrame) fed into `how` in every case.
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} | 5 | 2014-08-04T21:21:37Z | 2014-11-07T17:40:14Z | 2014-08-04T22:42:19Z | NONE | null | Hi,
see this example:
```
# imagine a dataframe with a from/to
x_df = DataFrame([[20120101, 20121231], [20130101, 20131231], [20140101, 20141231], [20150101, 99991231]])
x_df.columns = ['date_from', 'date_to']
date_def = '%Y%m%d'
# so everything is ok & peachy for the from dates
x_df['date_from_2'] = pd.to_datetime(x_df['date_from'], format=date_def, coerce=True)
x_df['date_from_2'].dtype
list(x_df['date_from_2'])
# but with out of bound dates it goes horribly wrong
x_df['date_to_2'] = pd.to_datetime(x_df['date_to'], format=date_def, coerce=True)
x_df['date_to_2'].dtype
list(x_df['date_to_2']) # note the lack of NATs and conversion to datetime.datetime instead of np.datetime64
# now we can do
x_df['date_to_3'] = [np.datetime64(date_val, unit='s') for date_val in x_df['date_to_2']] # works great but unfortunately pandas chose to aim for nanoseconds as a standard for date detail...
x_df['date_to_3'] = [pd.Timestamp(date_val) for date_val in x_df['date_to_2']] # which breaks on the 99991231 example
```
This is a bug, but also related to the discussion in #7307. Probably has to do with:
"Note Specifying a format argument will potentially speed up the conversion considerably and on versions later then 0.13.0 explicitly specifying a format string of ‘%Y%m%d’ takes a faster path still."
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} | 0 | 2014-08-04T21:50:55Z | 2014-08-04T22:42:19Z | 2014-08-04T22:42:19Z | CONTRIBUTOR | null | closes #7930
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} | 0 | 2014-08-04T23:18:26Z | 2014-09-10T18:09:27Z | 2014-09-10T18:09:27Z | CONTRIBUTOR | null | ```
In [47]: span
Out[47]:
<class 'pandas.tseries.period.PeriodIndex'>
[2012-12-31, ..., 9999-12-31]
Length: 3, Freq: D
In [48]: Series(span)
Out[48]:
0 15705
1 16404
2 2932896
dtype: int64
In [49]: Series(span.asobject)
Out[49]:
0 2012-12-31
1 2014-11-30
2 9999-12-31
dtype: object
```
I believe this works correctly if its a setitem in a frame, e.g.
`df['span'] = span` but not in the `Series` constructor, needs this: https://github.com/pydata/pandas/blob/master/pandas/core/series.py#L2436 called in the `Series.__init__`
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https://api.github.com/repos/pandas-dev/pandas/issues/7933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7933/events | https://github.com/pandas-dev/pandas/pull/7933 | 39,471,180 | MDExOlB1bGxSZXF1ZXN0MTkzMTQyMjM= | 7,933 | DOC: update docs to show construction of periodindex when needing out-of bounds spans | {
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} | 2 | 2014-08-04T23:23:49Z | 2014-08-04T23:48:44Z | 2014-08-04T23:48:44Z | CONTRIBUTOR | null | {
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} | 7 | 2014-08-04T23:31:33Z | 2014-09-14T19:17:18Z | 2014-08-19T14:47:20Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7925.
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} | 6 | 2014-08-04T23:54:07Z | 2014-10-30T11:19:43Z | 2014-10-20T11:03:40Z | NONE | null | It would be great if a `columns` option could be added to the Stata Reader. Some `dta` files can be large and I would like to import a few specified columns. Current I am importing the entire file and returning a selection such as:
``` python
data = pd.read_stata(fn)[['year', 'exports']]
```
I would propose `pd.read_stata(fn, columns=['year', 'exports'])`
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} | 11 | 2014-08-05T08:40:54Z | 2021-04-09T18:28:01Z | 2014-09-19T09:56:28Z | NONE | null | Hello,
This code
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import sqlalchemy
def main():
N = 5
df = pd.DataFrame({'Date':pd.date_range('2000-12-29', freq='D', periods=N), 'Open': range(N)})
scheme = 'mysql+mysqlconnector'
config = {
'host' : 'localhost',
'database': 'test',
'user' : 'root',
'password': '123456',
'port': 3306
}
db_uri = "{scheme}://{user}:{password}@{host}:{port}/{database}"
db_uri = db_uri.format(
scheme = scheme,
user = config['user'],
password = config['password'],
port = config['port'],
host = config['host'],
database = config['database']
)
engine = sqlalchemy.create_engine(db_uri)
df["Date"] = df["Date"] + np.timedelta64(1, 'us') # to test resolution issue
df = df.set_index("Date")
print(df)
print(df.dtypes)
print(type(df.index), df.index.dtype)
print(type(df.index[0]))
df.to_sql("orcl", engine, flavor="mysql", if_exists="replace")
if __name__ == '__main__':
main()
```
raises following error:
```
sqlalchemy.exc.ProgrammingError: (ProgrammingError) Failed processing pyformat-parameters; 'MySQLConverter' object has no attribute '_timestamp_to_mysql' u'INSERT INTO orcl (`Date`, `Open`) VALUES (%(Date)s, %(Open)s)' ({'Date': Timestamp('2000-12-29 00:00:00.000001'), 'Open': 0}, {'Date': Timestamp('2000-12-30 00:00:00.000001'), 'Open': 1}, {'Date': Timestamp('2000-12-31 00:00:00.000001'), 'Open': 2}, {'Date': Timestamp('2001-01-01 00:00:00.000001'), 'Open': 3}, {'Date': Timestamp('2001-01-02 00:00:00.000001'), 'Open': 4})
```
I try to pass my own converter see
- http://stackoverflow.com/questions/25093950/avoid-mysqlconverter-object-has-no-attribute-timestamp-to-mysql-error-with/25094000#25094000
- http://chat.stackoverflow.com/rooms/58566/discussion-between-femto-trader-and-unutbu
But I didn't have success
I think it should be nice if `datetime64[ns]` where automatically stored as `BIGINT` in database.
I'm aware that I can convert df column (or index) with `datetime64[ns]` (or Pandas Timestamp) data to integer data before calling `to_sql` but I don't consider this as a clean way of doing.
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.14.1'
In [3]: import sqlalchemy
In [4]: sqlalchemy.__version__
Out[4]: '0.8.4'
In [5]: import numpy as np
In [6]: np.__version__
Out[6]: '1.8.1'
In [7]: import mysql.connector
In [8]: mysql.connector.__version__
Out[8]: '1.1.6'
```
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| null | 3 | 2014-08-05T09:25:06Z | 2014-08-05T22:05:03Z | 2014-08-05T17:03:01Z | MEMBER | null | It took me a while to discover what was causing this TypeError... I guess this can be useful to other people.
(Yes, I know there is a note somewhere warning users that some operations depend on the index being sorted, but maybe this is a sligthly different case, since the operations do work after all, the problem is they do something more than what is documented)
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| null | 11 | 2014-08-05T12:08:17Z | 2021-04-11T05:04:14Z | 2021-04-11T05:04:14Z | NONE | null | Hello,
Timestamp sub second resolution doesn't seem to be supported with mysqldb.
See:
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import sqlalchemy
def main():
N = 5
df = pd.DataFrame({'Date':pd.date_range('2000-12-29', freq='D', periods=N), 'Open': range(N)})
scheme = 'mysql+mysqldb'
config = {
'host' : 'localhost',
'database': 'test',
'user' : 'root',
'password': '123456',
'port': 3306
}
db_uri = "{scheme}://{user}:{password}@{host}:{port}/{database}"
db_uri = db_uri.format(
scheme = scheme,
user = config['user'],
password = config['password'],
port = config['port'],
host = config['host'],
database = config['database']
)
engine = sqlalchemy.create_engine(db_uri)
df["Date"] = df["Date"] + np.timedelta64(1, 'us') # to test resolution issue
df = df.set_index("Date")
print(df)
print(df.dtypes)
print(type(df.index), df.index.dtype)
print(type(df.index[0]))
df.to_sql("orcl", engine, flavor="mysql", if_exists="replace")
if __name__ == '__main__':
main()
```
doesn't raise any error but when I queried database I noticed that I have only second resolution timestamp (no microsecond, in fact no fractional seconds).
```
$ mysql -u root mysql -p
Enter password:
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A
Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 40
Server version: 5.5.38-0ubuntu0.14.04.1 (Ubuntu)
Copyright (c) 2000, 2014, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.
mysql> use test;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A
Database changed
mysql> select * from orcl;
+---------------------+------+
| Date | Open |
+---------------------+------+
| 2000-12-29 00:00:00 | 0 |
| 2000-12-30 00:00:00 | 1 |
| 2000-12-31 00:00:00 | 2 |
| 2001-01-01 00:00:00 | 3 |
| 2001-01-02 00:00:00 | 4 |
+---------------------+------+
5 rows in set (0.00 sec)
mysql> show columns from orcl;
+-------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------+------------+------+-----+---------+-------+
| Date | datetime | YES | MUL | NULL | |
| Open | bigint(20) | YES | | NULL | |
+-------+------------+------+-----+---------+-------+
2 rows in set (0.00 sec)
```
Maybe storing `datetime64` into integer value could be the most portable solution over database, driver...
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} | 20 | 2014-08-05T12:24:10Z | 2014-08-19T16:42:50Z | 2014-08-19T16:42:50Z | CONTRIBUTOR | null | Calling `.where()` treats `None` differently depending on the value of `inplace`.
```
In [1]: import pandas
In [2]: s1 = pandas.Series(['a', 'b', 'c'])
In [3]: s1.where(s1 != 'a', None)
Out[3]:
0 None
1 b
2 c
dtype: object
In [4]: s2 = pandas.Series(['a', 'b', 'c'])
In [5]: s2.where(s1 != 'a', None, inplace=True)
In [6]: s2
Out[6]:
0 NaN # would have expected None here!
1 b
2 c
dtype: object
```
This causes surprising behaviour when setting values in a series to None.
```
In [1]: import pandas
In [2]: s3 = pandas.Series(['a', 'b', 'c'])
In [3]: s3[0] = None
In [4]: s3[s3 == 'b'] = None
In [5]: s3
Out[5]:
0 None
1 NaN # Expected None here too!
2 c
dtype: object
```
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} | 3 | 2014-08-05T12:51:27Z | 2014-08-06T15:44:30Z | 2014-08-05T13:18:00Z | CONTRIBUTOR | null | closes #7939
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} | 17 | 2014-08-05T12:59:05Z | 2014-09-04T00:23:14Z | 2014-08-19T16:42:50Z | CONTRIBUTOR | null | This pull request fixes #7939 by ensuring that None coercion always obeys the following rules:
1. If the container is numeric, None should be coerced to NaN
2. If the container is for time data, None should be coerced to NaT
3. If the container contains heterogenous data (i.e. objects), None should be preserved.
## Old behaviour examples
### Assigning None in integer containers
None is sometimes preserved, sometimes coerced to NaN, and sometimes causes an error.
``` python
>>> s = Series([1, 2, 3])
>>> s[0] = None # error
>>> s = Series([1, 2, 3])
>>> s.loc[0] = None
>>> s
0 None
1 2
2 3
dtype: object
>>> s = Series([1, 2, 3])
>>> s[s == 1] = None
>>> s
0 NaN
1 2
2 3
dtype: float64
>>> s = Series([1, 2, 3])
>>> s.loc[s == 1] = None
0 None
1 2
2 3
dtype: object
```
Datetimes are similar.
### Assigning None in object containers
None is sometimes preserved, and sometimes coerced.
``` python
>>> s = Series(["a", "b", "c"])
>>> s[0] = None
0 None
1 b
2 c
dtype: object
>>> s = Series(["a", "b", "c"])
>>> s.loc[0] = None
0 None
1 b
2 c
dtype: object
>>> s = Series(["a", "b", "c"])
>>> s[s == 'a'] = None
0 NaN
1 b
2 c
dtype: object
>>> s = Series(["a", "b", "c"])
>>> s.loc[s == 'a'] = None
0 None
1 b
2 c
dtype: object
```
## New behaviour examples
### None is always coerced to NaN in integer containers
``` python
>>> s = Series([1, 2, 3])
>>> s[0] = None
0 NaN
1 2
2 3
dtype: float64
>>> s = Series([1, 2, 3])
>>> s.loc[0] = None # same
>>> s = Series([1, 2, 3])
>>> s[s == 1] = None # same
>>> s = Series([1, 2, 3])
>>> s.loc[s == 1] = None # same
```
### None is always preserved in object containers
``` python
>>> s = Series(["a", "b", "c"])
>>> s[0] = None
0 None
1 b
2 c
dtype: object
>>> s = Series(["a", "b", "c"])
>>> s.loc[0] = None # same
>>> s = Series(["a", "b", "c"])
>>> s[s == 'a'] = None
>>> s = Series(["a", "b", "c"])
>>> s.loc[s == 'a'] = None # same
```
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| null | 14 | 2014-08-05T15:53:40Z | 2014-08-29T21:53:05Z | 2014-08-29T20:11:28Z | CONTRIBUTOR | null | It looks like `concat()` changes the bdate_range index of a DataFrame:
```
In [1]: from pandas.util.testing import assert_series_equal, assert_frame_equal
In [2]: from pandas import Series, DataFrame, concat, bdate_range
In [3]: import numpy as np
In [4]: from datetime import datetime
In [5]: df = DataFrame(np.nan, index=bdate_range(datetime(2009,1,1), periods=3), columns=['A','B'])
In [7]: df2 = concat([df, DataFrame(np.nan, index=range(2), columns=df.columns)]).slice_shift(-2)
In [8]: df
Out[8]:
A B
2009-01-01 NaN NaN
2009-01-02 NaN NaN
2009-01-05 NaN NaN
In [9]: df2
Out[9]:
A B
2009-01-01 00:00:00 NaN NaN
2009-01-02 00:00:00 NaN NaN
2009-01-05 00:00:00 NaN NaN
In [10]: assert_frame_equal(df, df2)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-10-ea37dae851f7> in <module>()
----> 1 assert_frame_equal(df, df2)
C:\Python34\lib\site-packages\pandas\util\testing.py in assert_frame_equal(left, right, check_dtype, check_index_type, check_column_
type, check_frame_type, check_less_precise, check_names, by_blocks, check_exact)
625 if not by_blocks:
626 assert_index_equal(left.columns, right.columns)
--> 627 assert_index_equal(left.index, right.index)
628
629 # compare by blocks
C:\Python34\lib\site-packages\pandas\util\testing.py in assert_index_equal(left, right)
562 left,
563 right,
--> 564 right.dtype))
565
566
AssertionError: [index] left [datetime64[ns] <class 'pandas.tseries.index.DatetimeIndex'>
[2009-01-01, ..., 2009-01-05]
Length: 3, Freq: B, Timezone: None], right [Index([2009-01-01 00:00:00, 2009-01-02 00:00:00, 2009-01-05 00:00:00], dtype='object') o
bject]
In [16]: concat([df, DataFrame(np.nan, index=range(2), columns=df.columns)])
Out[16]:
A B
2009-01-01 00:00:00 NaN NaN
2009-01-02 00:00:00 NaN NaN
2009-01-05 00:00:00 NaN NaN
0 NaN NaN
1 NaN NaN
```
Curiously, `concat()` also seems to change the bdate_range index of a Series, though somehow under `slice_shift()` it gets restored to its original form (which is not the case for a DataFrame):
```
In [11]: s = Series(np.nan, index=bdate_range(datetime(2009,1,1), periods=3))
In [12]: s2 = concat([s, Series(np.nan, index=range(2))]).slice_shift(-2)
In [13]: s
Out[13]:
2009-01-01 NaN
2009-01-02 NaN
2009-01-05 NaN
Freq: B, dtype: float64
In [14]: s2
Out[14]:
2009-01-01 NaN
2009-01-02 NaN
2009-01-05 NaN
dtype: float64
In [15]: assert_series_equal(s, s2)
In [17]: concat([s, Series(np.nan, index=range(2))])
Out[17]:
2009-01-01 00:00:00 NaN
2009-01-02 00:00:00 NaN
2009-01-05 00:00:00 NaN
0 NaN
1 NaN
dtype: float64
```
This is using v0.14.1 on 64-bit Window Python 3.4.1.
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} | 14 | 2014-08-05T16:23:25Z | 2014-09-13T19:27:34Z | 2014-09-13T19:27:34Z | CONTRIBUTOR | null | When storing datetimes with timezone information in mysql I split out the is_dst flag into a separate column. Then when reconstructing the Timestamps I am either forced to iterate through each row and call pytz.timezone.localize on every Timestamp which is very slow or do some magic with localizing what I can and then manually dealing with the fall transition time (note that infer_dst won't work because there could be many rows that have transitions in them). I would much rather create the DatetimeIndex from the column of dates and then call tz_localize with the is_dst column. This would then appropriately set the offset.
```
di = DatetimeIndex(frame['DateColumn'])
di = di.tz_localize(TimeZone, is_dst_flat=frame['IsDstColumn'])
```
Thoughts?
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} | 4 | 2014-08-05T21:26:15Z | 2015-07-28T21:52:04Z | 2015-07-28T21:52:04Z | NONE | null | Not too much to say; this is a documentation PR.
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} | 1 | 2014-08-06T07:38:50Z | 2014-08-06T14:07:00Z | 2014-08-06T14:07:00Z | MEMBER | null | Small adaptation, I think it is a bit clearer this way.
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} | 0 | 2014-08-06T13:40:03Z | 2014-08-06T14:26:12Z | 2014-08-06T14:26:12Z | MEMBER | null | There are already some references in the API docs to numpy functions, but the links did not yet work up to now.
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} | 13 | 2014-08-06T13:50:06Z | 2015-03-08T16:59:46Z | 2015-03-08T16:59:46Z | NONE | null | can pandas have twisted protocol support for accessing db?
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} | 5 | 2014-08-06T14:22:47Z | 2019-10-21T16:06:43Z | 2019-10-21T16:06:42Z | CONTRIBUTOR | null | EAFP is an acronym that stands for "easier to ask forgiveness than permission" and describes a principle behind the pythonic way of doing some operations that may fail:
``` python
try:
do_smth()
except MyError:
do_smth_otherwise()
```
as opposed to
``` python
if i_can_do_smth():
do_smth()
else:
do_smth_otherwise()
```
pandas sources use this a lot but often the exception is caught with `except:` which among other things intercept `SystemExit` and `KeyboardInterrupt`. Because of that it's possible that when you're trying to interrupt an operation it goes on simply choosing another code path.
There are valid use cases for carpet-catching excepts like that, most obvious ones being performing some cleanup and re-raising the error afterwards and catching all errors coming from evaluation of user-supplied code. In the rest of the cases `except Exception:` should be used instead.
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} | 6 | 2014-08-06T17:10:27Z | 2014-09-04T22:03:08Z | 2014-09-04T22:02:54Z | CONTRIBUTOR | null | BUG: Previously, a negative sign was being prepended for positive infinity, not for negative infinity. (GH6812)
xref #6812
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} | 0 | 2014-08-06T21:45:32Z | 2014-08-11T13:06:48Z | 2014-08-11T13:06:48Z | CONTRIBUTOR | null | This will detect even more situations where chained assignment is being used.
FYI technically there are 2 situations where the same error is raised: 1) setting on a copy, 2) chained assignment on a view. Both are really similar and result in usually the same issue. An assignment that doesn't appear to assign anything.
This detected a number of tests that were incorrectly using chaining in the pandas test suite as well (fixed below).
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} | 3 | 2014-08-07T13:15:24Z | 2014-08-07T14:27:08Z | 2014-08-07T14:27:08Z | CONTRIBUTOR | null | TST: argmin/max impl, tests, and doc-strings
DOC: release notes corrections
API: raise on `__nonzero__` for Indexes, closes #7897
partial close of #7904
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} | 21 | 2014-08-07T14:12:28Z | 2016-10-14T04:55:27Z | 2014-08-31T22:29:28Z | MEMBER | null | Related #7441. Work in progress for now
Add support to read/write data from/to a specific database schema through a `schema` keyword. If this is `None`, use the default schema as before.
- [x] support via a `schema` arg in the `read_sql_table` and `to_sql` functions
- [x] not for `read_sql_query` -> is this needed? (this is a direct `execute`, and I don't directly see how this can be aware of schema's) -> indeed not
- [x] tested for postgresql
- [ ] test for other databases (mysql and sqlite don't support schema's -> so can't test on travis)
- [x] docs
- [x] Cause of the failure at the moment: `schema` as arg for `MetaData` is only introduced in sqlalchemy 0.7.4. We test on 0.7.1 => raise required version
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} | 23 | 2014-08-07T16:59:07Z | 2014-08-10T22:25:41Z | 2014-08-10T22:25:36Z | CONTRIBUTOR | null | closes #7207
```
In [1]: s = Series(date_range('20130101',periods=3))
In [2]: s.dt.
s.dt.date s.dt.dayofweek s.dt.hour s.dt.is_month_start s.dt.is_quarter_start s.dt.is_year_start s.dt.minute s.dt.nanosecond s.dt.second s.dt.week s.dt.year
s.dt.day s.dt.dayofyear s.dt.is_month_end s.dt.is_quarter_end s.dt.is_year_end s.dt.microsecond s.dt.month s.dt.quarter s.dt.time s.dt.weekofyear
In [3]: s.dt.year
Out[3]: array([2013, 2013, 2013])
In [4]: s.dt.hour
Out[4]: array([0, 0, 0])
In [5]: Series(np.arange(5)).dt
TypeError: Can only use .dt accessor with datetimelike values
```
Tab completion is specific to the type of wrapped delegate (DatetimeIndex or PeriodIndex)
```
In [5]: p = Series(period_range('20130101',periods=3,freq='D').asobject)
In [6]: p.dt.
p.dt.day p.dt.dayofweek p.dt.dayofyear p.dt.hour p.dt.minute p.dt.month p.dt.quarter p.dt.qyear p.dt.second p.dt.week p.dt.weekofyear p.dt.year
```
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} | 6 | 2014-08-07T18:03:20Z | 2014-08-07T19:06:59Z | 2014-08-07T19:00:31Z | MEMBER | null | because i live dangerously and run master pretty much all the time i found this in some code of mine that saves indices to matlab files, the solution is to call `.values`, but it would be nice if we could not break this.
Here's a simple test:
``` python
import pandas as pd
import numpy as np
from scipy.io import savemat
idx = pd.Index(np.arange(10))
savemat('test.mat', {'idx': idx}, oned_as='column')
```
i'll look into fixing this
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| null | 4 | 2014-08-07T18:52:48Z | 2014-08-07T18:59:36Z | 2014-08-07T18:58:17Z | CONTRIBUTOR | null | ``` python
rdr2.to_csv('jdate_test_out.csv', index=False)
csvtest = pd.read_csv('jdate_test_out.csv')
print csvtest.jdate.ptp()
rdr2.to_csv('jdate_test_out.csv', index=False, engine='fast')
csvtest = pd.read_csv('jdate_test_out.csv')
print csvtest.jdate.ptp()
```
0.0416644699872
0.0
Disclaimer: the 'fast' engine came from my own PR, so I messed up somewhere.
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} | 11 | 2014-08-07T21:52:44Z | 2020-05-16T03:33:27Z | null | NONE | null | In `_sqlalchemy_type` function, pandas import Text type from sqlalchemy as the default type when generating text field in SQL table, which is transformed into "text" type in microsoft sql. This type could not be used by "group by" in SQL query.
SQL query with a group by clause with the field generate an error msg like:
Msg 306, Level 16, State 2, Line 3
The text, ntext, and image data types cannot be compared or sorted, except when using IS NULL or LIKE operator.
I tried default text field to be the `String`, which works for MS sql.
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} | 18 | 2014-08-08T00:50:03Z | 2021-01-24T18:00:40Z | 2021-01-24T18:00:39Z | NONE | null | I'm building a simulation tool that essentially requires iterating through some fairly large DataFrames with iterrows (ie. the tool cannot be vectorized). I've looked into converting it to Cython but I'm just not that skilled with Cython to make it work. Also, I don't quite get why it's such a no-no to iterate on rows (other than it's slow) but for my purpose I have to since each row is a record in time.
However, I _did_ build my own iterator for the DataFrame using np.nditer that is a whole lot faster than iterrows and can give dict-like objects unlike itertuples. Is there a desire for something like this?
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} | 16 | 2014-08-08T09:19:08Z | 2021-04-11T05:06:33Z | null | MEMBER | null | Follow-up of #6300 to discuss the object-oriented API for the sql functions.
So there are two ways to interact with SQL databases:
- more simple, high-level functions: `read_sql_query`, `read_sql_table`, `to_sql`
- "OO API" for more advanced functionality
The high-level functions are already quite polished (but there are still some issues with NaN, datetime, schema, ..), but the OO API should still be discussed and is not yet really public.
**Current design**:
- `PandasSQLAlchemy` object (with `execute`, `read_sql`, `read_table`, `to_sql`, `has_table`, `drop_table` methods)
- `PandasSQLTable` object to do the actual table reading/writing
**Some questions remain**:
- What is this 'more advanced' usage that we want to enable in the API?
- possibility to provide custom `MetaData`
- the fact you don't recreate the object and metadata for each query (if you do multiple queries)
- other sqlalchemy features people would want? Possibility to provide your own `Table` description instead of generating automatically?
- more like `HDF5Store`? (getitem/setitem support? eg `pandas_sql['table_name']` to read a table?)
- What do we regard as public API?
- Only `PandasSQLAlchemy`? Or also `PandasSQLTable`?
- which methods of `PandasSQLAlchemy` should be public?
- Naming:
- naming of `PandasSQLAlchemy` -> do we need a better name? ('SQLDatabase', 'SQLdb', 'SQLConnection', ...)
- naming of the methods (`read_sql` -> `read_query`?)
- How do we see `PandasSQLAlchemy`?
- wrapper around an `Engine`? or wrapper around `Engine` + `MetaData`
- do we want to support reading/writing to multiple schema's? Or do you need to create seperate objects for each schema?
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} | 0 | 2014-08-08T09:57:11Z | 2014-08-22T08:50:47Z | 2014-08-22T08:50:47Z | MEMBER | null | Closes #7826.
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| null | 1 | 2014-08-08T11:15:40Z | 2014-08-08T12:31:03Z | 2014-08-08T12:30:56Z | CONTRIBUTOR | null | See the following code:
``` python
from pandas import DataFrame
from numpy import NaN
df = DataFrame({'A': [1,2,3,5]})
# interpolation with 'limit' argument gives error when there are
# no nan values in the series:
df.interpolate(limit=1)
# after adding a NaN value, no error
df.loc[1] = NaN
df.interpolate(limit=1)
```
the bug appears to be in pandas/core/common.py. The datatype for
``` python
1438 violate = np.array([x.all() & (x.size > limit) for x in violate],)
```
is implicit, and should be made explicit with dtype=bool.
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https://api.github.com/repos/pandas-dev/pandas/issues/7963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7963/events | https://github.com/pandas-dev/pandas/pull/7963 | 39,815,874 | MDExOlB1bGxSZXF1ZXN0MTk1MjMyNTI= | 7,963 | Support is_dst indicators in tz_localize | {
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} | 47 | 2014-08-08T12:01:42Z | 2014-09-13T22:07:44Z | 2014-09-13T19:27:34Z | CONTRIBUTOR | null | The indicators are useful for assigning the correct offset when there are ambiguous transition times
closes #7943.
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} | 4 | 2014-08-08T12:21:08Z | 2018-07-06T14:20:18Z | 2018-07-06T14:20:18Z | CONTRIBUTOR | null | Instead of having a represenation of an array of `Period` objects (object dtype) in a Series/DataFrame. Allow an internal type of `PeriodBlock` to represent, similary to `DatetimeBlock`.
Here is a usecase. The following would be very simple if `to_period(...)` returned a `PeriodIndex`
```
In [59]: df = DataFrame({'date' : pd.date_range('20130101 00:01:00',periods=5,freq='61s')})
In [60]: df
Out[60]:
date
0 2013-01-01 00:01:00
1 2013-01-01 00:02:01
2 2013-01-01 00:03:02
3 2013-01-01 00:04:03
4 2013-01-01 00:05:04
In [61]: df.date.dt.to_period('T')
Out[61]:
0 2013-01-01 00:01
1 2013-01-01 00:02
2 2013-01-01 00:03
3 2013-01-01 00:04
4 2013-01-01 00:05
dtype: object
In [62]: pd.Series(pd.PeriodIndex(df.date.dt.to_period('T')+1).to_timestamp())
Out[62]:
0 2013-01-01 00:02:00
1 2013-01-01 00:03:00
2 2013-01-01 00:04:00
3 2013-01-01 00:05:00
4 2013-01-01 00:06:00
dtype: datetime64[ns]
```
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} | 13 | 2014-08-08T13:33:25Z | 2014-09-17T19:28:01Z | 2014-09-17T19:28:01Z | CONTRIBUTOR | null | I fear that I somehow created a DataFrame with a numeric MultiIndex that triggers (un?)intended behaviour in the `.loc` function. I failed to create a reproducible example with anything but a pickled dump of a DataFrame.
**Question** Is this a bug, or a user error? I'm confused...
### Steps to reproduce
1. Get `http://ojdo.de/tmp/df.pickle` (17 kB)
2. Execute
``` python
import pandas as pd
df = pd.read_pickle('df.pickle')
df.loc[(1,199), 'Elec']
```
### Resulting output
``` python
Vertex1 Vertex2
1 199 7.602552
Name: Elec, dtype: float64
```
### Expected output
The value on its own:
`7.602552`
### It get's weirder
There must be something between row 80 and 90 in this DataFrame, because the following snippet yields a single value, while returing a Series if executed with `.head(90)`.
``` python
In [20]: df2 = df.head(80)
In [21]: df2.loc[(1,199), 'Elec']
Out[21]: 7.6025524199999994
```
# Installed versions
commit: None
python: 2.7.0.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.3
Cython: None
numpy: 1.8.1
scipy: 0.12.0
statsmodels: None
IPython: 0.13.2
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5-mpl
pytz: 2012d
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.2.1
openpyxl: 2.0.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.2
html5lib: 1.0b3
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 1 | 2014-08-08T16:58:44Z | 2014-08-10T15:20:26Z | 2014-08-10T14:53:57Z | MEMBER | null | Must be revisited after #7954 to remove `_skip_if_not_numpy17_friendly`
Closes #7740.
Allow `Period` and `PeriodIndex` `add` and `sub` to support `timedelta`-like. If period freq is `offsets.Tick`, offsets can be added if the result can have same freq. Otherwise, `ValueError` will be raised.
```
idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
idx + pd.offsets.Hour(2)
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-07-01 11:00, ..., 2014-07-01 15:00]
# Length: 5, Freq: H
idx + datetime.timedelta(minutes=120)
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-07-01 11:00, ..., 2014-07-01 15:00]
# Length: 5, Freq: H
idx + np.timedelta64(7200, 's')
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-07-01 11:00, ..., 2014-07-01 15:00]
# Length: 5, Freq: H
idx + pd.offsets.Minute(5)
# ValueError: Input has different freq from PeriodIndex(freq=H)
```
If period freq isn't `Tick`, only the same offset can be added. Otherwise, `ValueError` will be raised.
```
idx = pd.period_range('2014-07', periods=5, freq='M')
idx + pd.offsets.MonthEnd(3)
# <class 'pandas.tseries.period.PeriodIndex'>
# [2014-10, ..., 2015-02]
# Length: 5, Freq: M
idx + pd.offsets.MonthBegin(3)
# ValueError: Input has different freq from PeriodIndex(freq=M)
```
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} | 0 | 2014-08-08T19:50:02Z | 2014-10-06T17:09:18Z | 2014-10-06T17:09:18Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25210819/speeding-up-data-import-function-pandas-and-appending-to-dataframe
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| null | 4 | 2014-08-09T03:33:42Z | 2014-08-09T14:10:04Z | 2014-08-09T09:08:27Z | NONE | null | if i use the chinese as column name,the column names and values could not be aligned,like this:

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} | 2 | 2014-08-09T04:31:14Z | 2014-08-09T13:35:54Z | 2014-08-09T13:35:54Z | CONTRIBUTOR | null | This may well be a case of can't or won't be supported, but I thought I'd at least clarify.
If I have a column name with a space in it (e.g. "foo bar") then there doesn't appear to be a way to access that column via df.eval.
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} | 21 | 2014-08-09T12:35:21Z | 2015-08-15T23:57:54Z | 2015-08-15T23:57:54Z | NONE | null | Also there was unimported module warnings with calls to it. In pandas/core/panel.py lines 718, 748 for example.
closes #7879
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} | 70 | 2014-08-09T18:35:35Z | 2014-10-02T17:03:41Z | 2014-10-02T17:02:28Z | MEMBER | null | closes #6870
- [x] use conda.binstar.org/cpcloud channel for versions/packages not in conda defaults
- [x] possibly bring back the ccache, not totally sure what this is doing
this doesn't remove maintenance, but it sure does simplify things, since conda recipes are very easy to create
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} | 5 | 2014-08-09T23:23:22Z | 2014-08-10T00:28:04Z | 2014-08-10T00:27:49Z | CONTRIBUTOR | null | After grouping on an integer column, we seem to forget that we have groups:
```
>>> pd.__version__
'0.14.1-172-gab64d58'
>>> x = np.arange(6, dtype=np.int64)
>>> df = pd.DataFrame({"a": x//2, "b": 2.0*x, "c": 3.0*x})
>>> df
a b c
0 0 0 0
1 0 2 3
2 1 4 6
3 1 6 9
4 2 8 12
5 2 10 15
>>> df.groupby("a").transform("mean")
b c
0 1 1.5
1 5 7.5
2 9 13.5
3 NaN NaN
4 NaN NaN
5 NaN NaN
>>> df["a"] = df["a"]*1.0
>>> df.groupby("a").transform("mean")
b c
0 1 1.5
1 1 1.5
2 5 7.5
3 5 7.5
4 9 13.5
5 9 13.5
```
To make it even more obvious:
```
>>> df.index = range(20, 26)
>>> df.groupby("a").transform("mean")
b c
0 1 1.5
1 5 7.5
2 9 13.5
20 NaN NaN
21 NaN NaN
22 NaN NaN
23 NaN NaN
24 NaN NaN
25 NaN NaN
```
Switching to a float index seems to avoid the issues as well.
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} | 1 | 2014-08-10T00:03:55Z | 2014-08-10T00:27:51Z | 2014-08-10T00:27:49Z | MEMBER | null | closes #7972
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} | 3 | 2014-08-10T01:13:29Z | 2014-08-10T03:47:49Z | 2014-08-10T03:47:08Z | CONTRIBUTOR | null | Allow `functools.partial` objects (and similar callables without `__name__` attributes) to be used as `groupby` functions, which also adds support along the tree (e.g. `resample`, which is specifically tested.)
Closes #7929.
This does not address the potential enhancements allowing automatic naming of duplicate-named functions (relatively minor) and the use of a Series to specify names (more useful).
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} | 0 | 2014-08-10T01:24:50Z | 2014-08-10T01:40:24Z | 2014-08-10T01:40:24Z | CONTRIBUTOR | null | xref #6496
Additional perf improvements for Series.transform (when specifying cythonizable functions)
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_transform_series2 | 98.7293 | 3621.1437 | 0.0273 |
groupby_transform2 | 19.5080 | 168.8046 | 0.1156 |
```
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| null | 13 | 2014-08-10T02:18:26Z | 2014-08-10T19:36:37Z | 2014-08-10T13:41:54Z | NONE | null | See this very strange behavior when taking the mean of a dataframe that has a series with uint16 datatype. Why does the mean of column y change after including column x?
```
Python 2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun 2 2014, 12:48:16)
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: import random
In [4]:
In [4]: np.__version__
Out[4]: '1.8.1'
In [5]: pd.__version__
Out[5]: '0.14.0'
In [6]:
In [6]: y = np.array([random.randint(1900,2000) for x in range(0,2000)])
In [7]: y.mean()
Out[7]: 1950.0115000000001
In [8]: y.astype(np.uint16).mean()
Out[8]: 1950.0115000000001
In [9]:
In [9]: d1 = pd.DataFrame()
In [10]: d1['y'] = y.astype(np.uint16)
In [11]: d1.mean()
Out[11]:
y 16.6995
dtype: float64
In [12]:
In [12]: d1['x'] = y.astype(np.int16)
In [13]: d1.mean()
Out[13]:
y 1950.0115
x 1950.0115
dtype: float64
```
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} | 9 | 2014-08-10T07:10:58Z | 2014-09-10T00:02:26Z | 2014-09-10T00:02:26Z | CONTRIBUTOR | null | The interpretation of `min_periods` in the `ewm*()` functions seems rather odd to me. For example (in 0.14.1):
```
In [19]: x
Out[19]:
0 0
1 NaN
2 NaN
3 NaN
4 4
5 NaN
6 6
dtype: float64
In [20]: ewma(x, com=3., min_periods=2)
Out[20]:
0 NaN
1 NaN
2 0.000000
3 0.000000
4 2.285714
5 2.285714
6 3.891892
dtype: float64
```
The way it works, is it finds the first non-`NaN` value (`0` in the example above) and then makes sure that the `min_periods` entries (`min_periods-1` in 0.15.0, per https://github.com/pydata/pandas/pull/7898) in the result starting at that entry are `NaN`. Does it make any sense that the result has entry `0` set to `NaN`, but entries `2` and `3` (and `1` in 0.15.0) set to `0.0`?
I would have thought that the values to be explicitly `NaN`ed would be those determined by `x.notnull().cumsum() < min_periods`. This would be consistent with the meaning of `min_periods` in the `rolling_*()` and `expanding_*()` functions.
CC'ing @snth and @jaimefrio, in case they have opinions.
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| null | 3 | 2014-08-10T11:17:26Z | 2014-08-10T14:02:45Z | 2014-08-10T14:02:40Z | NONE | null | I just tried to pass `axis=1` on a shift call to a dataframe and it threw an error, I couldn't find an issue here and this is the first time I tried this function so not sure if this was something that used to work and then got broken.
I'm using python 64-bit 3.3.4, pandas 0.14.1 and numpy 1.8.1
```
In [57]:
df = pd.DataFrame({'a':randn(10), 'b':randn(10)})
df.shift(axis=1)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-57-160107eee7e4> in <module>()
1 df = pd.DataFrame({'a':randn(10), 'b':randn(10)})
----> 2 df.shift(axis=1)
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py in shift(self, periods, freq, axis, **kwds)
3173 block_axis = self._get_block_manager_axis(axis)
3174 indexer = com._shift_indexer(len(self), periods)
-> 3175 new_data = self._data.shift(indexer, periods, axis=block_axis)
3176 else:
3177 return self.tshift(periods, freq, **kwds)
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in shift(self, *args, **kwargs)
2405
2406 def shift(self, *args, **kwargs):
-> 2407 return self.apply('shift', *args, **kwargs)
2408
2409 def fillna(self, *args, **kwargs):
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in apply(self, f, *args, **kwargs)
2373
2374 else:
-> 2375 applied = getattr(blk, f)(*args, **kwargs)
2376
2377 if isinstance(applied, list):
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in shift(self, indexer, periods, axis)
916 """ shift the block by periods, possibly upcast """
917
--> 918 new_values = self.values.take(indexer, axis=axis)
919 # convert integer to float if necessary. need to do a lot more than
920 # that, handle boolean etc also
IndexError: index 2 is out of bounds for size 2
In [57]:
df = pd.DataFrame({'a':randn(10), 'b':randn(10)})
df.shift(axis=1)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-57-160107eee7e4> in <module>()
1 df = pd.DataFrame({'a':randn(10), 'b':randn(10)})
----> 2 df.shift(axis=1)
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py in shift(self, periods, freq, axis, **kwds)
3173 block_axis = self._get_block_manager_axis(axis)
3174 indexer = com._shift_indexer(len(self), periods)
-> 3175 new_data = self._data.shift(indexer, periods, axis=block_axis)
3176 else:
3177 return self.tshift(periods, freq, **kwds)
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in shift(self, *args, **kwargs)
2405
2406 def shift(self, *args, **kwargs):
-> 2407 return self.apply('shift', *args, **kwargs)
2408
2409 def fillna(self, *args, **kwargs):
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in apply(self, f, *args, **kwargs)
2373
2374 else:
-> 2375 applied = getattr(blk, f)(*args, **kwargs)
2376
2377 if isinstance(applied, list):
C:\WinPython-64bit-3.3.5.0\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py in shift(self, indexer, periods, axis)
916 """ shift the block by periods, possibly upcast """
917
--> 918 new_values = self.values.take(indexer, axis=axis)
919 # convert integer to float if necessary. need to do a lot more than
920 # that, handle boolean etc also
IndexError: index 2 is out of bounds for size 2
```
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} | 8 | 2014-08-10T15:25:48Z | 2014-08-15T13:01:47Z | 2014-08-15T12:54:51Z | MEMBER | null | Closes #4060.
```
idx = pd.Index([1, 2, 3, 4, 1, 2])
idx.duplicated()
# Index([False, False, False, False, True, True], dtype='bool')
idx.drop_duplicates()
# Int64Index([1, 2, 3, 4], dtype='int64')
idx.duplicated(take_last=True)
# Index([True, True, False, False, False, False], dtype='bool')
idx.drop_duplicates(take_last=True)
# Int64Index([3, 4, 1, 2], dtype='int64')
```
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| null | 4 | 2014-08-10T15:31:00Z | 2014-08-11T13:05:20Z | 2014-08-11T13:05:20Z | NONE | null | data1.loc['2012-1,'yb']=list(y2)
the 'yb' column is a new column,the pandas's warning is "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 instead"
how to fix it?
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| null | 10 | 2014-08-10T15:35:26Z | 2014-08-10T16:41:20Z | 2014-08-10T16:41:20Z | NONE | null | I wonder to know if the following is an excepted behaviour:
``` python
arrays = [[3, 3, 5, 5, 7, 8, 9, 9, 9],
[1, 2, 2, 8, 8, 1, 2, 4, 5]]
tuples = list(zip(*arrays))
df = pd.DataFrame(np.random.randn(9, 4), index=arrays)
print(df)
```
```
0 1 2 3
3 1 -0.406271 -0.365945 1.014546 0.193454
2 -1.433601 -2.398783 0.421473 1.183917
5 2 0.358215 0.183478 1.327390 1.846286
8 -0.054229 1.476181 -2.193220 -0.945004
7 8 -0.598960 0.191141 1.245808 -0.614519
8 1 1.051352 -0.537377 -0.534096 0.319221
9 2 0.784284 1.561996 0.258979 1.344247
4 -0.348615 0.410410 0.152473 -1.153719
5 1.524407 -0.184949 -0.732762 0.186657
```
Now I want to retrieve some data with multi indexing.
``` python
# It works
indexes = [tuple([3, 1]), tuple([5, 8])]
df.loc[indexes]
```
``` python
# It fails
indexes = np.array([[3, 1], [5, 8]])
df.loc[indexes]
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-d91db2bf1d2a> in <module>()
1 indexes = np.array([[3, 1], [5, 8]])
----> 2 df.loc[indexes]
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1127 return self._getitem_tuple(key)
1128 else:
-> 1129 return self._getitem_axis(key, axis=0)
1130
1131 def _getitem_axis(self, key, axis=0, validate_iterable=False):
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis, validate_iterable)
1248
1249 if hasattr(key, 'ndim') and key.ndim > 1:
-> 1250 raise ValueError('Cannot index with multidimensional key')
1251
1252 if validate_iterable:
ValueError: Cannot index with multidimensional key
```
``` python
# It fails
indexes = [[3, 1], [5, 8]]
df.loc[indexes]
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-be34675485dc> in <module>()
1 indexes = [[3, 1], [5, 8]]
----> 2 df.loc[indexes]
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1127 return self._getitem_tuple(key)
1128 else:
-> 1129 return self._getitem_axis(key, axis=0)
1130
1131 def _getitem_axis(self, key, axis=0, validate_iterable=False):
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis, validate_iterable)
1252 if validate_iterable:
1253 self._has_valid_type(key, axis)
-> 1254 return self._getitem_iterable(key, axis=axis)
1255 elif _is_nested_tuple(key, labels):
1256 locs = labels.get_locs(key)
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
901 level = None
902
--> 903 keyarr_is_unique = Index(keyarr).is_unique
904
905 # existing labels are unique and indexer is unique
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/lib.cpython-34m.so in pandas.lib.cache_readonly.__get__ (pandas/lib.c:36059)()
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/core/index.py in is_unique(self)
453 @cache_readonly(allow_setting=True)
454 def is_unique(self):
--> 455 return self._engine.is_unique
456
457 def is_integer(self):
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/index.cpython-34m.so in pandas.index.IndexEngine.is_unique.__get__ (pandas/index.c:4026)()
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/index.cpython-34m.so in pandas.index.IndexEngine._do_unique_check (pandas/index.c:4442)()
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/index.cpython-34m.so in pandas.index.IndexEngine._ensure_mapping_populated (pandas/index.c:4615)()
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/index.cpython-34m.so in pandas.index.IndexEngine.initialize (pandas/index.c:4703)()
/home/hadim/.virtualenvs/st/lib/python3.4/site-packages/pandas/hashtable.cpython-34m.so in pandas.hashtable.PyObjectHashTable.map_locations (pandas/hashtable.c:11720)()
TypeError: unhashable type: 'list'
```
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} | 5 | 2014-08-10T20:38:49Z | 2014-09-09T05:46:21Z | 2014-08-19T17:51:26Z | MEMBER | null | Currently, the positioning of \midrule is determined by the number of index
levels, not columns levels:
```
>>> print pd.DataFrame({('x', 'y'): ['a']}).to_latex()
\begin{tabular}{ll}
\toprule
{} & x \\
\midrule
{} & y \\
0 & a \\
\bottomrule
\end{tabular}
```
The fix is simple: use the number of column levels instead of the number
of index levels.
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| null | 2 | 2014-08-11T07:57:24Z | 2014-08-11T12:33:49Z | 2014-08-11T12:33:49Z | CONTRIBUTOR | null | Just a thought to investigate further?
Instead of specifiying chunksize, could give column or columns and it could split when these values change.
e.g.
Say I have a dataset, I know is sorted by date, and there are a lot of records for each date.
I could specify to read_csv to iterate on date, and it will read all rows where date's value are the same and adjacent it each "chunk".
Like I said, not sure how feasible it is, but could be really useful.
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} | 11 | 2014-08-11T16:58:51Z | 2020-08-18T15:18:44Z | null | NONE | null | This could very well be something that just isn't clear to me from reading the docs, but I would like to have my index be unique when I dump the dataframe to SQLite table so that when I append more data I don't get duplicate rows. Here's what my resulting scheme looks like after using the to_sql function
sqlite> .schema
CREATE TABLE "Sniffs" (
"ID" TEXT,
"Day" TEXT,
"ModelName" TEXT,
"PassRate" FLOAT,
"RtxName" TEXT,
"Shadow" TEXT,
"Time" TEXT,
"Unit" TEXT
);
CREATE INDEX "ix_Sniffs_ID" ON "Sniffs" ("ID");
Looking at these docs:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html#pandas.DataFrame.to_sql
My resulting call looks something like this:
sniff_df.to_sql('Sniffs', sqleng, if_exists='append', index_label='ID')
Am I reading the docs wrong or anything/is there currently a method to get unique indexes in the output sql to avoid overlapping data? Could we get a kwarg like: unique_index=True ?
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} | 3 | 2014-08-11T17:59:37Z | 2020-06-28T18:07:32Z | 2020-06-28T18:07:32Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects
is their
Isn't 'LWOM-SUN' supposed to work for the offset?
it raises `ValueError`
```
In [5]: date_range('20130101',periods=5,freq=pd.offsets.LastWeekOfMonth(weekday=pd.offsets.WeekDay.SUN))
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-27, ..., 2013-05-26]
Length: 5, Freq: LWOM-SUN, Timezone: None
In [6]: date_range('20130101',periods=5,freq=pd.offsets.LastWeekOfMonth(weekday=pd.offsets.WeekDay.SUN)).tolist()
Out[6]:
[Timestamp('2013-01-27 00:00:00', offset='LWOM-SUN'),
Timestamp('2013-02-24 00:00:00', offset='LWOM-SUN'),
Timestamp('2013-03-31 00:00:00', offset='LWOM-SUN'),
Timestamp('2013-04-28 00:00:00', offset='LWOM-SUN'),
Timestamp('2013-05-26 00:00:00', offset='LWOM-SUN')]
```
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} | 5 | 2014-08-11T18:12:49Z | 2021-08-10T13:10:48Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/25249033/week-of-a-month-pandas
should be easy extension
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} | 17 | 2014-08-11T18:33:13Z | 2014-08-19T17:07:30Z | 2014-08-19T17:07:30Z | CONTRIBUTOR | null | if observance is None and offset is None, Holiday breaks when Timestamp is added to None. This was a bug in the 0.14.1 before a recent PR. I will issue a pr for this shortly.
Example:
```
july_3rd = Holiday('July 4th Eve', month=7, day=3)
```
July 3rd is a nyse exchange early close that is observed only on July 3rd.
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} | 5 | 2014-08-11T18:43:05Z | 2014-08-19T19:33:11Z | 2014-08-19T17:07:30Z | CONTRIBUTOR | null | This should fix issue 7987 where there is no offset or observance.
closes https://github.com/pydata/pandas/issues/7987
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| null | 10 | 2014-08-11T19:18:44Z | 2014-08-12T12:19:01Z | 2014-08-12T11:21:50Z | CONTRIBUTOR | null | Assume that you have a csv file separated by the sequence `\x01` (namely `chr(1)`):
`0S15SOfKdx\x013580\x015738.0\x011407348006000\n0S15SOfKdx\x0140\x015738.0\x011407351601000\n`
How can I load this in Pandas? I tried everything:
- `pd.read_csv(my_file, sep=chr(1))`
- `pd.read_csv(my_file, sep='\x01')`
with no luck. Is this a bug, or I am typing the separator incorrectly?
I am on **Pandas 0.14.1** with **Python 3**.
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} | 7 | 2014-08-11T20:59:26Z | 2014-09-19T20:20:24Z | 2014-08-12T13:04:01Z | MEMBER | null | This makes CI much easier (probably faster) and is less of a maintenance burden.
- [x] `pep8` method/function calls where necessary in `io/pytables.py` (maybe tests too)
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} | 14 | 2014-08-11T21:17:48Z | 2014-09-09T20:09:08Z | 2014-09-09T19:46:29Z | CONTRIBUTOR | null | This will allow deprecated kwargs to be transformed before being passed to a function. For example, in #7963, one could write
``` python
@deprecate_kwarg(old_arg_name='infer_dst', new_arg_name='ambiguous',
mapping={True: 'infer', False: 'raise'})
def tz_localize(self, tz, ambiguous=None):
...
```
Open issues:
- should we test for a bad mapping at creation time? (in this PR: yes)
- how much error checking should we do? (in this PR: pass args unrecognized by a mapping through unchanged; assume a function will do its own error handling)
- should we support both mappings and callables? (in this PR: yes)
- if so, how should we recognize mappings? (in this PR: existence of a `get` method)
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} | 7 | 2014-08-11T21:44:40Z | 2014-08-12T07:08:25Z | 2014-08-11T23:30:54Z | CONTRIBUTOR | null | Since version 0.14 `sort_index` does some rearangment of the ordering of rows:
```
X = pd.DataFrame({'a': range(17), 'b': [1] * 17})
X.sort_index(by=['b'], inplace=True)
```
results in:
```
a b
0 0 1
14 14 1
13 13 1
12 12 1
11 11 1
10 10 1
9 9 1
15 15 1
8 8 1
6 6 1
5 5 1
4 4 1
3 3 1
2 2 1
1 1 1
7 7 1
16 16 1
```
This seems only to be the case if the length of the dataframe is longer then 16 rows (therefor the lenghty example). If you execute `X.sort_index(by=['b'], inplace=True)` again you will have the dataframe in the previous order. In version 0.13.1 the ordering of the rows was untouched, as I would expect. Is there a reason for changing the behaviour (couldn't find anything in the change logs) or is this a bug?
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} | 13 | 2014-08-11T22:29:51Z | 2014-08-29T20:37:06Z | 2014-08-29T20:37:06Z | CONTRIBUTOR | null | (ignore the pandas 0.12.0 install, this is on master, its in a separate build dir)
cc @sinhrks
related #7907
```
C:\Users\Jeff Reback\Documents\GitHub\pandas> more versions.27-32.log
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.12.0
nose: 1.3.0
Cython: 0.19.2
numpy: 1.9.0b1
scipy: None
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: 0.91.3
dateutil: 2.2
pytz: 2013.8
bottleneck: 0.7.0
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.0
lxml: 3.3.5
bs4: 4.3.2
html5lib: 1.0b3
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.3
pymysql: 0.6.1.None
psycopg2: 2.5.2 (dt dec pq3 ext)
C:\Users\Jeff Reback\Documents\GitHub\pandas>more test.27-32.log
..SS..SS..SS..SS..SS..SS..SSSSSSSSSS..SS..SS..SSSS...........SS..SS..SS..SS..SS..SS..SS................SSSSSSSSSSSSSSSS..SS...........................
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................FFF...F...................................................................................c:\Users\Jeff Reback\Documents\GitHub\pandas
\build\lib.win32-2.7\pandas\core\algorithms.py:140: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
sorter = uniques.argsort()
C:\python27-32\lib\site-packages\numpy\core\fromnumeric.py:791: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
................................................................................................................S.....................................
......................................................................................................................C:\python27-32\lib\site-packages
\numpy\core\_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
....................................................................................................................................S.SSSSS...........
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SSS.SSS.S.S.S.S.S.S.S...S.S.SSSSSSSSS..............S.....S..SS..S......S.......S..............SS.SSSSS.S....S................c:\Users\Jeff Reback\Docu
ments\GitHub\pandas\build\lib.win32-2.7\pandas\tools\plotting.py:3004: UserWarning: To output multiple subplots, the figure containing the passed axes
is being cleared
"is being cleared", UserWarning)
......................................................................................................................................................
................................................................................................................c:\Users\Jeff Reback\Documents\GitHub\
pandas\build\lib.win32-2.7\pandas\core\index.py:1200: RuntimeWarning: Cannot compare type 'Timestamp' with type 'str', sort order is undefined for inc
omparable objects
"incomparable objects" % e, RuntimeWarning)
......................................................c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\core\index.py:1200: Runt
imeWarning: Cannot compare type 'Timestamp' with type 'long', sort order is undefined for incomparable objects
"incomparable objects" % e, RuntimeWarning)
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======================================================================
FAIL: test_constructor_datetime64_tzformat (pandas.tseries.tests.test_timeseries.TestDatetimeIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_timeseries.py", line 2182, in test_constructor_date
time64_tzformat
tm.assert_index_equal(idx, expected)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\testing.py", line 579, in assert_index_equal
right.dtype))
AssertionError: [index] left [datetime64[ns] <class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2016-01-01 00:00:00-05:00]
Length: 4, Freq: AS-JAN, Timezone: tzoffset(None, -18000)], right [<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00, ..., 2016-01-01 00:00:00-05:00]
Length: 4, Freq: AS-JAN, Timezone: pytz.FixedOffset(-300) datetime64[ns]]
======================================================================
FAIL: test_constructor (pandas.tseries.tests.test_tslib.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_tslib.py", line 47, in test_constructor
self.assertEqual(result.value, expected)
AssertionError: 1404205200000000000L != 1404205200000000005L
======================================================================
FAIL: test_constructor_with_stringoffset (pandas.tseries.tests.test_tslib.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_tslib.py", line 100, in test_constructor_with_strin
goffset
self.assertEqual(result.value, expected)
AssertionError: 1404205200000000000L != 1404205200000000005L
======================================================================
FAIL: test_repr (pandas.tseries.tests.test_tslib.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_tslib.py", line 166, in test_repr
self.assertIn(tz_repr, repr(date_tz))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\testing.py", line 143, in assertIn
assert a in b, "%s: %r is not in %r" % (msg.format(a,b), a, b)
AssertionError: : '/US/Pacific' is not in "Timestamp('2014-03-07 00:00:00-0800', tz='dateutil/America/Los_Angeles')"
======================================================================
FAIL: test_nanosecond_string_parsing (pandas.tseries.tests.test_tslib.TestTimestampNsOperations)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_tslib.py", line 461, in test_nanosecond_string_pars
ing
self.assertEqual(ts.value, expected_value)
AssertionError: 1367392545123456000L != 1367392545123456789L
```
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} | 13 | 2014-08-11T22:54:44Z | 2014-08-20T08:07:14Z | 2014-08-12T13:04:01Z | CONTRIBUTOR | null | closes #7990
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} | 16 | 2014-08-12T11:40:31Z | 2017-12-28T12:31:28Z | 2017-12-28T12:31:28Z | CONTRIBUTOR | null | ```
import datetime
s = pd.Series(pd.date_range('20130101',periods=3))
s-pd.Timestamp('20130101')
s-datetime.datetime(2013,1')
```
This fails as the datetime64 is not converted properly (because numpy datetime ops suck)
```
s-np.datetime64('20130101')
```
e.g. `np.datetime64('20130101').astype('M8[ns]')` is a bug, no?
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} | 0 | 2014-08-12T11:43:22Z | 2014-09-30T19:39:45Z | 2014-09-30T19:39:45Z | CONTRIBUTOR | null | reported in #7965
```
df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]])
df.columns = pd.MultiIndex.from_tuples([(0,1),(1,1),(2,1)])
df.groupby(axis=1, level=[0,1]).first()
TypeError: can only sort by level with a hierarchical index
```
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} | 10 | 2014-08-12T13:37:51Z | 2014-08-30T01:19:34Z | 2014-08-25T04:10:14Z | MEMBER | null | Like #7809, added `DataFrame.plot(kind='box')` to plot boxplot under the common plotting method.
**NOTE:** `DataFrame.boxplot` remains untouched (few lines are modified to share some definitions).
**NOTE:** This is based on #7736.
```
import pandas as pd
import numpy as np
pd.options.display.mpl_style = 'default'
df = pd.DataFrame(np.random.randn(100, 5))
ax = df.plot(kind='box')
```

One enhancement is easier colorization by passing `color` keyword. You can pass `dict` with keys are `boxes`, `whiskers`, `medians` and `caps`. If some keys are missing in the `dict`, default colors are used for the corresponding artists. Also, boxplot has `sym` keyword to specify fliers style (This is mpl keyword and simply forward to mpl).
```
color = dict(boxes='DarkGreen', whiskers='DarkOrange',
medians='DarkBlue', caps='Gray')
ax = df.plot(kind='box', color=color)
```

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} | 9 | 2014-08-12T13:39:24Z | 2014-08-14T17:13:13Z | 2014-08-14T17:13:13Z | CONTRIBUTOR | null | ```
df = pandas.DataFrame([['a'], ['b']], index=[1,2], columns=['x'])
df.loc[[3]] # does not raise KeyError as documented
```
"ALL of the labels for which you ask, must be in the index or a KeyError will be raised!"
http://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-label
and
".loc is strictly label based, will raise KeyError when the items are not found":
http://pandas.pydata.org/pandas-docs/stable/indexing.html#different-choices-for-indexing-loc-iloc-and-ix
also, possibly separate bug:
```
df.loc[pandas.Index(['a', 'k', 'b']), : ]
```
DOES fail, so we have an inconsistency between .loc[values,:] and .loc[values]
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} | 17 | 2014-08-12T13:46:04Z | 2019-07-13T16:49:49Z | 2019-07-13T16:49:49Z | CONTRIBUTOR | null | See also the discussion at [StackOverflow](http://stackoverflow.com/questions/25255496/dataframe-interpolate-extrapolates-over-trailing-missing-data).
Linear interpolation on a series with missing data at the end of the array will overwrite trailing missing values with the last non-missing value. In effect, the function extrapolates rather than strictly interpolating.
Example:
```
import pandas as pd
import numpy as np
a = pd.Series([np.nan, 1, np.nan, 3, np.nan])
a.interpolate()
```
Yields (note the extrapolated 4):
```
0 NaN
1 1
2 2
3 3
4 4
5 4
dtype: float64
```
not
```
0 NaN
1 1
2 2
3 3
4 4
5 NaN
dtype: float64
```
I believe the fix is something along the lines of changing lines 1545:1546 in core/common.py from
```
result[firstIndex:][invalid] = np.interp(inds[invalid], inds[valid], yvalues[firstIndex:][valid])
```
to
```
result[firstIndex:][invalid] = np.interp(inds[invalid], inds[valid], yvalues[firstIndex:][valid], np.nan, np.nan)
```
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