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https://api.github.com/repos/pandas-dev/pandas/issues/7901
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39,338,980
MDU6SXNzdWUzOTMzODk4MA==
7,901
date_range does not capture right timezone from input dates
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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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7,902
BUG: do not assume 0 is in index of potential Series
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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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TST/BUG: Rename html encoding test files.
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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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INT: followup to Index not a sub-class of ndarray
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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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7,905
ENH: Add BusinessHour offset
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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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7,906
DOC: grammar fixes in docs
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2014-08-02T21:06:10Z
2015-04-29T15:34:09Z
2014-08-02T22:47:36Z
CONTRIBUTOR
null
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7,907
BUG: Timestamp cannot parse nanosecond from string
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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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7,908
pandas 0.14.1 dist has backup files included
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2014-08-03T18:06:21Z
2014-08-03T19:25:45Z
2014-08-03T19:25:45Z
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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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39,377,327
MDExOlB1bGxSZXF1ZXN0MTkyNTg1OTU=
7,909
Remove from start/end dates if tz is not None (#7901, #7835)
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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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MDExOlB1bGxSZXF1ZXN0MTkyNTk0Njk=
7,910
added support for selecting multiple nth values
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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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7,911
ENH: add "adjust" parameter to ewmvar(), ewmstd(), ewmvol(), ewmcov(), and ewmcorr()
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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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7,912
BUG: ewmvar/std/cov() de-biasing formula incorrect
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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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7,913
update to holiday to help with GH#7070
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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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DataFrame with MultiIndex columns implicitly assumes that stored data has an ndim attribute
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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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ENH: New `level` argument for DataFrame.tz_localize and DataFrame.tz_convert (GH7846)
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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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WIP: Experimental changes in `rolling_var` related to #7900
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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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7,917
DOC: small fixes categorical docs
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2014-08-04T07:57:10Z
2014-08-04T14:11:01Z
2014-08-04T14:11:01Z
MEMBER
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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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DOC/BUG: errors in the categorical docs
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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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7,919
Api: shared axis of plot
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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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7,920
DOC: Fix release note for GH7798
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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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7,921
REGR: Regression in multi-index indexing with a non-scalar type object (GH7914)
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0
2014-08-04T13:40:12Z
2014-08-04T15:17:33Z
2014-08-04T15:17:33Z
CONTRIBUTOR
null
closes #7914
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7,922
statsmodels frequency inference problems on 0.14.0
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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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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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39,428,821
MDExOlB1bGxSZXF1ZXN0MTkyODcyMDI=
7,924
BUG/DOC: Categorical fixes (GH7918)
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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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7,925
Bug in rolling_* functions when combining center and min_periods arguments
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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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7,926
API/BUG/ENH: ewmvar/cov debiasing factors; add 'adjust' to ewmvar/std/vol/cov/corr; ewm*() min_periods
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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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TST: test_encode in test_html
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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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39,444,962
MDExOlB1bGxSZXF1ZXN0MTkyOTcyMzM=
7,928
BUG: define empty product on Series and DataFrame to be 1
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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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39,456,357
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7,929
BUG: resample unexpectedly branching on type of `how` callable
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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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to_datetime %Y%m%d does not coerce correctly
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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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39,464,905
MDExOlB1bGxSZXF1ZXN0MTkzMTAxNzM=
7,931
BUG: Bug in to_datetime when format='%Y%m%d and coerce=True are specified (GH7930)
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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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39,470,901
MDU6SXNzdWUzOTQ3MDkwMQ==
7,932
BUG: periodindex converted to Series incorrectly getting coerce to int64
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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
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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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39,471,622
MDExOlB1bGxSZXF1ZXN0MTkzMTQ1MDk=
7,934
BUG: rolling/expanding_* treatment of center
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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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39,472,848
MDU6SXNzdWUzOTQ3Mjg0OA==
7,935
[pandas/io/stata.py] Importing option for importing specified columns from stata .dta files
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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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7,936
'MySQLConverter' object has no attribute '_timestamp_to_mysql' error with datetime64[ns], MySQL and MySQL-connector
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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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DOC: mention that stack/unstack implicicly sort
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3
2014-08-05T09:25:06Z
2014-08-05T22:05:03Z
2014-08-05T17:03:01Z
MEMBER
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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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7,938
Timestamp sub second resolution not supported with MySQL
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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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7,939
Inconsistent treatment of None with .where() and inplace
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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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BUG/API: Consistency in .where() when setting with None for both inplace in a Series (GH7939)
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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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API: Coerce None according to the dtype of the container
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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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BUG: DataFrame bdate_range index being changed under concat()?
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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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7,943
tz_localize should support is_dst input array
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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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39,567,213
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7,944
Add warning about HDFStore concurrent reads
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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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39,596,678
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7,945
Update deprecate_kwarg message
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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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7,946
DOC: add intersphinx mapping to numpy
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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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7,947
twisted support is possible?
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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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ERR: catchall exception should be explicity and use Exception EAFP
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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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7,949
BUG: Fix for to_excel +/- infinity
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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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7,950
COMPAT: raise SettingWithCopy in even more situations when a view is at hand
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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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7,951
DOC/TST: index followup
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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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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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39,745,918
MDExOlB1bGxSZXF1ZXN0MTk0Nzk4NzY=
7,953
API: add Series.dt delegator for datetimelike methods (GH7207)
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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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MDExOlB1bGxSZXF1ZXN0MTk0ODE2MjI=
7,954
CI: Drop numpy 1.6 support
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21
2014-08-07T17:30:47Z
2014-08-12T21:59:23Z
2014-08-12T21:59:20Z
MEMBER
null
closes #7711
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39,751,718
MDU6SXNzdWUzOTc1MTcxOA==
7,955
Index cannot be written with scipy.io.savemat
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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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7,956
csv writer engine 'fast' has a bug
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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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7,957
DataFrame.to_sql generated text field that could not be used by "group by". (MS sql)
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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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7,958
iterrows, itertuples, and using np.nditer for a new iterator?
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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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DOC: Removed unpaired right-paren. Rephrased references.
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CONTRIBUTOR
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39,805,784
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7,960
API: object oriented interface for SQL functionality
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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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7,961
BUG: fix checking of table name in read_sql (GH7826)
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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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MDU6SXNzdWUzOTgxMzI0MA==
7,962
BUG: Interpolation with 'limit' argument gives error when there are no nan values in the data series
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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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39,815,874
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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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7,964
API/ENH: allow Period to be a first-class type via a PeriodBlockinternal type
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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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BUG: at with non-unique multi-index buggy
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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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ENH/BUG: Period and PeriodIndex ops supports timedelta-like
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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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39,856,894
MDU6SXNzdWUzOTg1Njg5NA==
7,967
DOC: update DataFrame.append / concat section to have a warning about the copying
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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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39,877,331
MDU6SXNzdWUzOTg3NzMzMQ==
7,968
How to align title and value?
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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: ![image](https://cloud.githubusercontent.com/assets/7219739/3864939/939ef854-1f76-11e4-9ab4-c02434d91a32.jpg)
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39,878,094
MDU6SXNzdWUzOTg3ODA5NA==
7,969
Can't access columns with spaces in them via df.eval
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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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39,884,622
MDExOlB1bGxSZXF1ZXN0MTk1NjM5Njk=
7,970
ENH Warn about panel.to_frame() discarding NaN GH7879
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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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CI/WIP: Use conda for most deps
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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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MDU6SXNzdWUzOTg5Njk5MA==
7,972
BUG: odd transform behaviour with integers
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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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BUG: fix transform with integers
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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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7,974
BUG: Allow __name__less callables as groupby hows (GH7929)
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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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7,975
PERF: perf improvements for Series.transform (revised) (GH6496)
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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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39,899,171
MDU6SXNzdWUzOTg5OTE3MQ==
7,976
Taking mean on dataframe with uint16 gives wrong number
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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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MDU6SXNzdWUzOTkwMjc5MQ==
7,977
API: meaning of min_periods for ewm*() functions?
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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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39,905,970
MDU6SXNzdWUzOTkwNTk3MA==
7,978
BUG: df.shift(axis=1) complains about index out of range
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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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39,909,905
MDExOlB1bGxSZXF1ZXN0MTk1NzQzNTE=
7,979
ENH: Add duplicated/drop_duplicates to Index
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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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39,910,004
MDU6SXNzdWUzOTkxMDAwNA==
7,980
How to deal with this Pandas warning?
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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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MDU6SXNzdWUzOTkxMDA3Mw==
7,981
.loc indexing with list of list or array fail
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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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39,916,125
MDExOlB1bGxSZXF1ZXN0MTk1NzY5NDM=
7,982
Fix DataFrame.to_latex() midrule positioning with MultiIndex columns
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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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39,935,693
MDU6SXNzdWUzOTkzNTY5Mw==
7,983
read_csv could support chunking by contigous values??
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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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39,977,889
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7,984
API to_sql method doesn't provide the option to specify unique indexes
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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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7,985
BUG: LWOM-SUN (LastWeekOfMonth) should directly work / doc examples of use
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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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39,984,696
MDU6SXNzdWUzOTk4NDY5Ng==
7,986
ENH: weekofmonth accessor for DatetimeIndex/PeriodIndex
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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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39,986,582
MDU6SXNzdWUzOTk4NjU4Mg==
7,987
Annual Holiday with no offset or observance
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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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39,987,498
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7,988
bug fix for 7987 and add day of week functionality to Holiday
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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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Reading \x01 separated files
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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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7,990
Drop support for PyTables < 3.0.0
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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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7,991
Allow deprecate_kwarg to transform arguments
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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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sort_index changes order of rows
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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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TST: windows failures after #7907 (failing 27-32,26-32,26-64)
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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........................... .S...S.S....SSS..............................................S......................................................S................................. ....................S..................................................SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS........................................ ...................................................................................................................................................... ...................................................................................................................................................... .........S...........................................................................................................................................S ............S.....................................................................................................................SS......SSSSSSSSSSSS SSSSSSSSSSSSSSSSSSSSSSSSSS.....S........................................................................S.............................SSSSSSSSSSS..... ...................................................................................................................................................... .........................S..........S...................................S..................S..S.SSSSSSS.......................................S....... ...S......S.....SSSSSSSSSSSSSSSSSSSSSSSSSSS........................................................................................................... .........................................................S............................................................................................ ................................................S..S.................................................................................................. ............................................................................S...S..................................................................... ...................................................................................................................................................... ....................................................................................................................F................................. ......................................................................S............................................................................... ..................................................................................S................................................................... ................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........... ...................................................................................................................................................... ........S.........................................S.S................................................................................................. ...................................S...........................................................................................SSS...SSSS.S.S.S.S.S.S. 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) ...................................................................................................................................................... ..............................................S.....S................................................................S................................ ...................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... 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...................................................................................................................................................... ...................................................................................................................................................... ...................................................................................................................................................... ............................................................................S......................................................................... ...S.S....S........SS.......................................................................................................S......................... ....S...S.............SSS.........S...........SS.............SSS....SSSS.............................................................................. ..........................................SS..............................................................................S.......................S... ...........................SS............................................S............................................................................ ..........................................................................................S....... ====================================================================== 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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COMPAT: change pytables to use 3.0.0 API (GH7990)
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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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DOC: Release note format correction
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2014-08-12T01:59:18Z
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BUG: coercion of non-M8[ns] in datetime ops
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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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REGR: grouping with a multi-index with first, a regression from 0.14.1
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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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ENH/CLN: add BoxPlot class inheriting MPLPlot
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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') ``` ![figure_1](https://cloud.githubusercontent.com/assets/1696302/3890938/2cc60f44-2225-11e4-8d79-4633c5ac7b40.png) 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) ``` ![figure_2](https://cloud.githubusercontent.com/assets/1696302/3890940/310b00e6-2225-11e4-8120-4c7fd7ffa697.png)
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7,999
Documentation/behaviour error for selecting by label
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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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8,000
DataFrame.interpolate() extrapolates over trailing missing data
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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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