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https://api.github.com/repos/pandas-dev/pandas/issues/6501
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28,467,007
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6,501
get_loc() returns integer or slice or KeyError nondeterministic in multiindex data frame
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2014-02-27T23:56:42Z
2014-07-10T20:05:04Z
2014-02-28T01:01:32Z
NONE
null
See example, if `n` is big, `get_loc` returns `slice`, otherwise it returns an integer. The boundary of `n` being big changes from time to time (but frequently 25 or 50). http://stackoverflow.com/questions/22067205/when-does-pandas-xs-drop-dimensions-and-how-can-i-force-it-to-not-to ``` python n=23 df = pd.DataFrame({'a':np.append(np.random.randint(0,10,n), -1), 'b':np.append(np.random.randint(0,10,n), -1), 'c':np.append(np.random.randint(0,10,n), -1), 'value':np.random.randint(0,100,n+1)}) df.set_index(['a','b','c'], inplace=True) df.sortlevel(inplace = True) #display(df.xs((-1,-1,-1))) df.index.get_loc((-1,-1,-1)) ``` The directly consequence is, `xs` would now returns a Series or a Data Frame (even if there is only 1 match) nondeterministicly (up to whether an integer or a slice is returned from `get_loc` ) What more, if the key is not in the indices, `get_loc` would sometimes throw `KeyError` exception, sometimes returns`Slice(0,0,None)` Try `df.index.get_loc((-2,-1,-1))` more times and you will see. I suspect it depends on whether there are duplicate values in the multiindex.
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6,502
int_format in display options
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3
2014-02-28T07:12:45Z
2021-04-11T04:12:45Z
null
CONTRIBUTOR
null
http://stackoverflow.com/a/22088497/1240268 Maybe date would be useful too?
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28,498,452
MDU6SXNzdWUyODQ5ODQ1Mg==
6,503
Syntax for multiple column selection
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1
2014-02-28T12:54:34Z
2014-03-13T01:22:21Z
2014-03-13T01:22:21Z
MEMBER
null
Occasionally I find myself and others wanting to select the same or or and conditions in a query. http://stackoverflow.com/questions/22093006/python-pandas-filter-dataframe-by-applying-regular-expression for example. Was just wondering if it might be useful to have a more convenient way to apply the same query to multiple columns without having to write or/and a bunch of times. Something that would translate to df[df[subset].eval("bool expression")] The pytables selection mechanism does something similar with a list. This smells like it could be feature bloat since it would be adding syntax but i could be convinced if it's useful.
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6,504
KeyError when indexing both axes with .loc[] with duplicate indices
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1
2014-02-28T13:44:27Z
2014-02-28T16:06:28Z
2014-02-28T16:06:28Z
MEMBER
null
The last line of the following code snippet does not work, while it seems it should. Am I overlooking something? ``` df = pd.DataFrame(np.random.randn(9,2), index=[1,1,1,2,2,2,3,3,3], columns=['a', 'b']) df df.loc[[1, 2]] # works df.loc[:,['a', 'b']] # works df.loc[[1, 2], ['a', 'b']] # does not work ``` You don't have this with an index without duplicates. ``` In [1]: df = pd.DataFrame(np.random.randn(9,2), index=[1,1,1,2,2,2,3,3,3], colum ns=['a', 'b']) In [2]: df Out[2]: a b 1 0.714735 -1.125141 1 0.061066 -0.737312 1 0.269988 1.434862 2 0.336829 1.040681 2 0.929521 -1.142978 2 0.272813 -0.370184 3 -0.520348 1.126233 3 0.232261 -0.075287 3 -0.314570 -2.635400 [9 rows x 2 columns] In [3]: df.loc[[1, 2]] Out[3]: a b 1 0.714735 -1.125141 1 0.061066 -0.737312 1 0.269988 1.434862 2 0.336829 1.040681 2 0.929521 -1.142978 2 0.272813 -0.370184 [6 rows x 2 columns] In [4]: df.loc[:,['a', 'b']] Out[4]: a b 1 0.714735 -1.125141 1 0.061066 -0.737312 1 0.269988 1.434862 2 0.336829 1.040681 2 0.929521 -1.142978 2 0.272813 -0.370184 3 -0.520348 1.126233 3 0.232261 -0.075287 3 -0.314570 -2.635400 [9 rows x 2 columns] In [5]: df.loc[[1, 2], ['a', 'b']] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-5-617013ef905a> in <module>() ----> 1 df.loc[[1, 2], ['a', 'b']] c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in __getitem__(sel f, key) 1152 def __getitem__(self, key): 1153 if type(key) is tuple: -> 1154 return self._getitem_tuple(key) 1155 else: 1156 return self._getitem_axis(key, axis=0) c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in _getitem_tuple( self, tup) 680 # ugly hack for GH #836 681 if self._multi_take_opportunity(tup): --> 682 return self._multi_take(tup) 683 684 # no shortcut needed c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in _multi_take(sel f, tup) 726 return o.reindex(**d) 727 except: --> 728 raise self._exception 729 730 def _convert_for_reindex(self, key, axis=0): KeyError: ```
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6,505
BUG: HDFStore utf8 corrupted reads
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11
2014-02-28T15:06:06Z
2014-05-27T12:16:05Z
2014-04-06T17:43:30Z
CONTRIBUTOR
null
When reading back utf8 encoded data, it randomly is corrupted when read back. The output contains random values or fails with an `UnicodeDecodeError`. After commenting out the fast version (`astype`) in `_unconvert_string_array` the issue did not show up anymore. pyton 3.3, numpy 1.8.0, pytables 3.1.0 I'll try to get a reproducible test up in the next days.
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BUG: Bug in multi-axis indexing using .loc on non-unique indices (GH6504)
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0
2014-02-28T15:32:01Z
2014-07-01T14:57:33Z
2014-02-28T16:06:28Z
CONTRIBUTOR
null
closes #6504
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6,507
BUG: xs with drop_level=True with a fully specified tuples buggy
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4
2014-02-28T16:10:03Z
2020-09-22T01:48:06Z
null
CONTRIBUTOR
null
related #6501 ``` In [1]: n=23 In [2]: df = pd.DataFrame({'a':np.append(np.random.randint(0,10,n), -1), ...: 'b':np.append(np.random.randint(0,10,n), -1), ...: 'c':np.append(np.random.randint(0,10,n), -1), ...: 'value':np.random.randint(0,100,n+1)}) In [3]: df.set_index(['a','b','c'], inplace=True) In [4]: df.sortlevel(inplace = True) In [7]: df.xs((-1,-1,-1),drop_level=True) Out[7]: value 40 Name: (-1, -1, -1), dtype: int64 In [8]: df.xs((-1,-1),drop_level=True) Out[8]: value c -1 40 [1 rows x 1 columns] ```
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6,508
pandas.DataFrame.query to allow column name with space
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41
2014-02-28T17:09:23Z
2019-06-24T11:49:19Z
2019-03-20T12:24:18Z
NONE
null
Able to do something like this would be nice ``` df.query('[col with space] < col') ``` I came across many external data files which have spaces in the column names. It would be nice to be able to do quick analysis on the data without first renaming the columns.
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BUG: SQL writing can lead to data loss
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11
2014-02-28T17:13:47Z
2014-03-11T12:05:51Z
2014-03-11T12:05:51Z
CONTRIBUTOR
null
`to_sql` uses `iterrows` which leads to data conversion, and in the case of mixed data types, can lead to data loss. ``` python s1 = pd.Series([0],dtype=np.float32) s2 = pd.Series([2**27 + 1],dtype=np.int32) df = pd.DataFrame({'float':s1, 'int':s2}) for row in df.iterrows(): print row[1][1] - (2**27 + 1) ``` (The same issue applies when using `df.to_sql`) I found the same bug in `to_stata` and have submitted a PR on it. `to_sql` is the only other location in pandas the uses `iterrows`, which should probably be avoided in all non-user code. It should be an easy fix using something like `itertuple` - I don't use the SQL code so I'm not in a good position to fix this problem.
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6,510
remove semicolon from CREATE TABLE legacy template
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3
2014-02-28T18:23:18Z
2014-07-16T08:56:38Z
2014-03-11T11:53:19Z
CONTRIBUTOR
null
It's not necessary and causes some DB engines to choke (e.g., Impala)
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6,511
pandas.DataFrame.duplicated to allow take_all
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3
2014-02-28T19:46:45Z
2015-08-08T19:46:01Z
2015-08-08T19:46:01Z
NONE
null
When working with external data, I often see rows with primary key violations. Currently, I could not easily select all the violating rows. For example, if I have a massive file with some inconsistent data ``` datecol,valuecol ... 2014-01-01,12 2014-01-01,13 2014-01-02,10 ... ``` In this use case, it would be good if we can do `df[df.duplicated('datecol', take_all=True)]` to directly get the bad rows ``` 2014-01-01,12 2014-01-01,13 ```
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6,512
groupby filter can't access all columns
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5
2014-02-28T22:25:05Z
2014-03-11T00:54:30Z
2014-03-11T00:54:21Z
CONTRIBUTOR
null
Unexpectedly? don't have access to the grouped columns. Also seems to allow returning a boolean Series (and takes the first item as the condition). ``` In [11]: df = pd.DataFrame([[1, 2], [1, 3], [5, 6]], columns=['A', 'B']) In [12]: g = df.groupby('A') # same with as_index=False (which *correctly* has no effect) In [13]: g.filter(lambda x: x['A'].sum() == 2) KeyError: u'no item named A' In [14]: g.filter(lambda x: x['B'].sum() == 5) # works Out[14]: A B 0 1 2 1 1 3 named A' In [14]: g.filter(lambda x: x['B'].sum() == 5) # works Out[14]: A B 0 1 2 1 1 3 In [15]: g.filter(lambda x: x.sum() == 5) # weird that this works (excepted raise) Out[15]: A B 0 1 2 1 1 3 In [16]: g = df.groupby(df['A']) # hack/workaround In [16]: g.filter(lambda x: x.sum() == 5) # seems to look at first col Out[16]: A B 2 5 6 In [17]: g.filter(lambda x: x['A'].sum() == 5) # works Out[17]: A B 2 5 6 In [18]: g.filter(lambda x: x['B'].sum() == 5) # works Out[18]: A B 0 1 2 1 1 3 ``` cc @danielballan
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6,513
read_csv can't read right-aligned columns delimited by spaces
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2
2014-03-01T02:01:17Z
2014-03-01T20:20:26Z
2014-03-01T20:20:26Z
NONE
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![screenshot 2014-02-28 18 02 06](https://f.cloud.github.com/assets/1332094/2300216/8785597e-a0e5-11e3-9d5d-00c8fa3bb68a.png) I often work with tables that are right aligned and space delimited. This means that the spaces in front of the first column varies from 0 to N. In this screenshot, this transition happens from 999 to 1000. I would like pandas to figure out that the first non-space values in each row all belong to the same column. Using skipinitialspace=True does not work (since some rows have no space at all), and so some of the values get pushed into the wrong column!
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6,514
ENH: add method='dense' to rank
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12
2014-03-01T03:10:52Z
2014-06-13T10:20:39Z
2014-03-01T15:41:14Z
CONTRIBUTOR
null
Addresses #6333. (Let's see how it goes on Travis; last time there was some strange behaviour which I think turned out to be because of an uninitialized variable which was behaving differently on my system than on the buildbots.)
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6,515
ENH: allow dict to name columns for multiple-aggregations in groupby
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3
2014-03-01T15:53:11Z
2017-01-23T14:10:04Z
2017-01-23T14:10:03Z
CONTRIBUTOR
null
from SO: http://stackoverflow.com/questions/22115671/apply-different-resampling-method-to-the-same-column-pandas related #7700 (make sure to have name support) Might be nice to support a dict for how on resample/groupby.agg so that the resulting columns of an aggregation are named as the keys `df.resample('A',how={ 'mymax' : 'max', 'mymean' : 'mean'})` ``` In [23]: df = DataFrame(np.random.randn(100,1),columns=['weight'],index=date_range('20000101',periods=100,freq='MS')) In [24]: df.resample('A',how=['max','mean']) Out[24]: weight max mean 2000-12-31 1.958570 -0.312230 2001-12-31 1.739518 0.035701 2002-12-31 2.503437 0.169365 2003-12-31 1.115315 0.149279 2004-12-31 2.190617 -0.087536 2005-12-31 1.286224 0.037669 2006-12-31 1.674017 0.147676 2007-12-31 2.107169 -0.064962 2008-12-31 -0.163863 -0.572363 [9 rows x 2 columns] ```
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BUG/API: allow TimeGrouper with other columns in a groupby (GH3794)
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32
2014-03-01T22:37:45Z
2014-06-15T01:09:02Z
2014-03-15T14:33:32Z
CONTRIBUTOR
null
closes #3794 ``` In [3]: df Out[3]: Branch Buyer Date Quantity 0 A Carl 2013-01-01 13:00:00 1 1 A Mark 2013-01-01 13:05:00 3 2 A Carl 2013-10-01 20:00:00 5 3 A Carl 2013-10-02 10:00:00 1 4 A Joe 2013-10-01 20:00:00 8 5 A Joe 2013-10-02 10:00:00 1 6 A Joe 2013-12-02 12:00:00 9 7 B Carl 2013-12-02 14:00:00 3 [8 rows x 4 columns] In [4]: df.groupby([pd.Grouper(freq='1M',key='Date'),'Buyer']).sum() Out[4]: Quantity Date Buyer 2013-01-31 Carl 1 Mark 3 2013-10-31 Carl 6 Joe 9 2013-12-31 Carl 3 Joe 9 [6 rows x 1 columns] In [5]: df = df.set_index('Date') In [6]: df['Date'] = df.index + pd.offsets.MonthEnd(2) In [9]: df.groupby([pd.Grouper(freq='6M',key='Date'),'Buyer']).sum() Out[9]: Quantity Date Buyer 2013-02-28 Carl 1 Mark 3 2014-02-28 Carl 9 Joe 18 [4 rows x 1 columns] In [10]: df.groupby([pd.Grouper(freq='6M',level='Date'),'Buyer']).sum() Out[10]: Quantity Date Buyer 2013-01-31 Carl 1 Mark 3 2014-01-31 Carl 9 Joe 18 [4 rows x 1 columns] ```
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28,571,943
MDU6SXNzdWUyODU3MTk0Mw==
6,517
panel count function will raise TypeError when the value of dataframe is None
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2
2014-03-02T02:58:34Z
2014-03-03T10:02:00Z
2014-03-03T10:02:00Z
NONE
null
import pandas as pd import numpy as np df1=pd.DataFrame(index=range(8),columns=['a','b','c']) #a empty dataframe df1.ix[:]=None #assign NULL panel1=pd.Panel(dict(df1=df1)) df1.count() #good panel1.count() #raise exception now the exception is rasied,it says,TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' how to repair it?any replies will be appreciated,thank you
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MDU6SXNzdWUyODU3NDU5Ng==
6,518
applymap returns empty list even though function is boolean
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2
2014-03-02T06:59:48Z
2014-03-02T19:15:41Z
2014-03-02T19:15:41Z
CONTRIBUTOR
null
Weird behaviour with apply map, the below function should always return a bool. ``` In [11]: df = pd.DataFrame({'feat 1': {0: [], 1: 6, 2: None}, 'feat 2': {0: [], 1: 8, 2: 10}, 'feat 3': {0: 5.0, 1: 3.0, 2: np.nan}}) In [14]: df.applymap(lambda x: x == [] or x is None) Out[14]: feat 1 feat 2 feat 3 0 True True False 1 False False False 2 True False False In [15]: df.applymap(lambda x: x is None or x == []) Out[15]: feat 1 feat 2 feat 3 0 True True [] 1 False False [] 2 True False [] ``` Strange use case, but maybe there is something weird here. http://stackoverflow.com/questions/22124537/read-json-populates-with-empty-lists-how-to-remove-those-rows/22124735#22124735
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6,519
Allow the use of buffers and None in to_hdf and read_hdf.
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9
2014-03-02T12:22:23Z
2014-07-02T14:14:18Z
2014-04-05T23:57:22Z
CONTRIBUTOR
null
This patch basically enables the expression ``` pd.read_hdf(BytesIO(df.to_hdf(key="/data")), key="/data") == df ```
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MDU6SXNzdWUyODU4NTc2Nw==
6,520
Issues with FRED Tests
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2014-03-02T19:04:43Z
2014-03-02T19:18:49Z
2014-03-02T19:18:49Z
CONTRIBUTOR
null
I am having tests fail with the following, code I do not believe that I touched: ``` FAIL: test_fred_multi (pandas.io.tests.test_data.TestFred) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1135, in wrapper return t(*args, **kwargs) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/io/tests/test_data.py", line 455, in test_fred_multi assert_frame_equal(received, expected, check_less_precise=True) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/util/testing.py", line 509, in assert_frame_equal check_less_precise=check_less_precise) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/util/testing.py", line 458, in assert_series_equal assert_almost_equal(left.values, right.values, check_less_precise) File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2554) File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1796) File "testing.pyx", line 140, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2387) AssertionError: expected 217.47800 but got 217.46600 ====================================================================== FAIL: test_fred_parts (pandas.io.tests.test_data.TestFred) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1135, in wrapper return t(*args, **kwargs) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/io/tests/test_data.py", line 424, in test_fred_parts self.assertEqual(df.ix['2010-05-01'][0], 217.23) AssertionError: 217.29900000000001 != 217.23 ```
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6,521
DOC: Add common error message to byte-ordering gotcha.
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1
2014-03-02T20:32:18Z
2014-06-26T12:43:08Z
2014-03-02T20:53:43Z
CONTRIBUTOR
null
A friend of mine got tripped up by [this gotcha](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#byte-ordering-issues). We document it well, but Googling the error message doesn't turn up our documentation, so it took him a lot of digging to get there. This commit adds an example error message that byte-ordering issues raise, in hopes of making this easier to Google.
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28,587,949
MDU6SXNzdWUyODU4Nzk0OQ==
6,522
read_csv should promote int values to float64 if column contains NaNs
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3
2014-03-02T20:47:55Z
2016-08-22T13:13:34Z
2016-08-22T13:13:34Z
CONTRIBUTOR
null
Versions: WinXP, pd 13.1, np-1.7.1, numexpr-2.2.2 Consider the following code snippit: from io import StringIO import pandas as pd import numpy as np data = 'a;b;c\n1;2;3\n4;NaN;6\n7;8;9' df = pd.read_csv(StringIO(data), sep = ';', dtype = np.float64) print(df.b) df2 = pd.read_csv(StringIO(data), sep = ';', dtype = np.int) It terminates as follows: In [77]: run test 0 2 1 NaN 2 8 ## Name: b, dtype: float64 Exception Traceback (most recent call last) c:\python33\lib\site-packages\IPython\utils\py3compat.py in execfile(fname, glob , loc) 98 loc = loc if (loc is not None) else glob 99 with open(fname, 'rb') as f: --> 100 exec(compile(f.read(), fname, 'exec'), glob, loc) 101 102 # Refactor print statements in doctests. C:\Dokumente und Einstellungen\stefan\Eigene Dateien\dev\economics\stats\imf\tes t.py in <module>() 11 12 df2 = pd.read_csv(StringIO(data), ---> 13 sep = ';', dtype = np.int) 14 print(df2.b) 15 c:\python33\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffe r, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipi nitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfoo ter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, c onverters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, com pact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lin es, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, da yfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format) 418 infer_datetime_format=infer_datetime_format) 419 --> 420 return _read(filepath_or_buffer, kwds) 421 422 parser_f.__name__ = name c:\python33\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds) 223 return parser 224 --> 225 return parser.read() 226 227 _parser_defaults = { c:\python33\lib\site-packages\pandas\io\parsers.py in read(self, nrows) 624 raise ValueError('skip_footer not supported for iteratio n') 625 --> 626 ret = self._engine.read(nrows) 627 628 if self.options.get('as_recarray'): c:\python33\lib\site-packages\pandas\io\parsers.py in read(self, nrows) 1068 1069 try: -> 1070 data = self._reader.read(nrows) 1071 except StopIteration: 1072 if nrows is None: c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader.read (pandas\parser.c:7110)() c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader._rea d_low_memory (pandas\parser.c:7334)() c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader._rea d_rows (pandas\parser.c:8159)() c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader._con vert_column_data (pandas\parser.c:9367)() c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader._con vert_tokens (pandas\parser.c:9929)() c:\python33\lib\site-packages\pandas\parser.pyd in pandas.parser.TextReader._con vert_with_dtype (pandas\parser.c:10412)() Exception: Integer column has NA values In [78]:
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MDU6SXNzdWUyODU5NTg5MA==
6,523
dataframe select by datetimeitem
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4
2014-03-03T02:42:02Z
2014-04-06T14:58:42Z
2014-04-06T14:58:42Z
NONE
null
http://stackoverflow.com/questions/22087411/dataframe-select-by-datetimeitem?noredirect=1#comment33500463_22087411
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28,601,637
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6,524
Grouby select multiple columns
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9
2014-03-03T06:37:19Z
2014-03-09T08:36:41Z
2014-03-03T14:19:51Z
CONTRIBUTOR
null
Is this supported? ``` g[['X', 'Y']] # do groupby stuff with just these columns ``` http://stackoverflow.com/q/22139053/1240268
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28,618,754
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6,525
BUG: fix DataFarame column slice indexer
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1
2014-03-03T12:36:45Z
2014-06-14T08:38:16Z
2014-03-03T14:10:37Z
CONTRIBUTOR
null
The code pulled in in #6123 didn't account for slice indexers which return views, not copies in `take_ref_locs` function. As a result, slice across columns corrupted `_ref_locs` of the parent block (provided `_ref_locs` existed). This PR should fix that. ``` python In [1]: df = pd.DataFrame(np.random.rand(5,3)) In [2]: df.dtypes Out[2]: 0 float64 1 float64 2 float64 dtype: object In [3]: df.iloc[:,2:] Out[3]: 2 0 0.524160 1 0.072608 2 0.289047 3 0.494401 4 0.235275 [5 rows x 1 columns] In [4]: _3.dtypes Out[4]: 2 float64 dtype: object In [5]: df.dtypes Out[5]: 0 float64 1 float64 2 None dtype: object In [6]: pd.__version__ Out[6]: '0.13.1-346-g30d3cba' ```
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28,621,725
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6,526
DataFrame with dtype=object converts Numpy Arrays to Series
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1
2014-03-03T13:32:43Z
2014-05-21T16:14:06Z
2014-03-03T13:37:53Z
CONTRIBUTOR
null
Hi, i am not sure if this behaviour is a bug introduced in 0.13.1 or if it is supposed to be a new feature. If you store a numpy array into a DataFrame with `dtype=object` it is automatically converted into a Series instead of just staying the original array: in: `mytable = pd.DataFrame(data={'data':[np.array([1,2,3])]}, index=None, columns=None, dtype=object, copy=False)` in: `type(mytable['data'][0])` out: `<class 'pandas.core.series.Series'>` In version 0.12.0 having the exact same statements, `mytable['data'][0]` would be of type `numpy.ndarray`. I would vote in favour of the old behaviour because stuff I put into an `object` frame should to remain the original object and not be autoconverted. Thanks!
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Inplace operation on PeriodIndex cast it to Int64Index
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1
2014-03-03T14:13:14Z
2014-07-23T21:28:50Z
2014-07-23T21:28:50Z
CONTRIBUTOR
null
``` python In [1]: dates = pd.period_range('01-Jan-2015', '01-Jan-2016', freq='M') ...: series = pd.TimeSeries(np.arange(dates.size), dates) ...: In [2]: series Out[2]: 2015-01 0 2015-02 1 2015-03 2 2015-04 3 2015-05 4 2015-06 5 2015-07 6 2015-08 7 2015-09 8 2015-10 9 2015-11 10 2015-12 11 2016-01 12 Freq: M, dtype: int32 In [3]: series.index += 1 In [4]: series Out[4]: 541 0 542 1 543 2 544 3 545 4 546 5 547 6 548 7 549 8 550 9 551 10 552 11 553 12 dtype: int32 In [5]: series.index Out[5]: Int64Index([541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553], dtype='int64') In [6]: pd.__version__ Out[6]: '0.13.1-339-g6c3755b' ```
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NaN values impact binary or operations asymmetrically
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10
2014-03-03T15:21:38Z
2019-12-02T12:48:15Z
2019-12-02T12:48:07Z
CONTRIBUTOR
null
I am not sure if this is on purpose, but as far as I know, `or` operation should be symmetric ( I mean `a | b == b | a` ); not in pandas: ``` >>> a = pd.Series( [True] ) >>> b = pd.Series( [np.nan] ) >>> a | b 0 True dtype: bool >>> b | a 0 False dtype: bool ``` is there any reason pandas is treating `NaN` values asymmetrically in here? also, this is a weird outcome as well: ``` >>> ( b == True ) | ( a == True ) 0 True dtype: bool ``` and this: ``` >>> b.astype( bool ) 0 True dtype: bool ``` this even has internal inconsistency; so before in `b | a`, `NaN` was treated as `False`, but now here it is `True`;
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28,629,199
MDU6SXNzdWUyODYyOTE5OQ==
6,529
np.datetime64 values cast to datetime.date iff dtype == M8
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6
2014-03-03T15:24:25Z
2014-03-03T19:13:31Z
2014-03-03T19:13:31Z
CONTRIBUTOR
null
This seems a little inconsistent to me. IMHO, it would be good to be able to rely on the the fact that if you specify a numpy array of _any_ datetime64 type the result will still be a datetime64 type and not cast to a different type dependant on the exact dtype passed in. ``` python In [1]: dates = pd.date_range('01-Jan-2015', '01-Dec-2015', freq='M') ...: values1 = dates.view(np.ndarray).astype('M8[D]') ...: values2 = dates.view(np.ndarray).astype('datetime64[ns]') ...: series1 = pd.TimeSeries(values1, dates) ...: series2 = pd.TimeSeries(values2, dates) ...: In [2]: series1 Out[2]: 2015-01-31 2015-01-31 2015-02-28 2015-02-28 2015-03-31 2015-03-31 2015-04-30 2015-04-30 2015-05-31 2015-05-31 2015-06-30 2015-06-30 2015-07-31 2015-07-31 2015-08-31 2015-08-31 2015-09-30 2015-09-30 2015-10-31 2015-10-31 2015-11-30 2015-11-30 Freq: M, dtype: object In [3]: series2 Out[3]: 2015-01-31 2015-01-31 2015-02-28 2015-02-28 2015-03-31 2015-03-31 2015-04-30 2015-04-30 2015-05-31 2015-05-31 2015-06-30 2015-06-30 2015-07-31 2015-07-31 2015-08-31 2015-08-31 2015-09-30 2015-09-30 2015-10-31 2015-10-31 2015-11-30 2015-11-30 Freq: M, dtype: datetime64[ns] In [4]: series1.values Out[4]: array([datetime.date(2015, 1, 31), datetime.date(2015, 2, 28), datetime.date(2015, 3, 31), datetime.date(2015, 4, 30), datetime.date(2015, 5, 31), datetime.date(2015, 6, 30), datetime.date(2015, 7, 31), datetime.date(2015, 8, 31), datetime.date(2015, 9, 30), datetime.date(2015, 10, 31), datetime.date(2015, 11, 30)], dtype=object) In [5]: series2.values Out[5]: array(['2015-01-31T00:00:00.000000000+0000', '2015-02-28T00:00:00.000000000+0000', '2015-03-31T01:00:00.000000000+0100', '2015-04-30T01:00:00.000000000+0100', '2015-05-31T01:00:00.000000000+0100', '2015-06-30T01:00:00.000000000+0100', '2015-07-31T01:00:00.000000000+0100', '2015-08-31T01:00:00.000000000+0100', '2015-09-30T01:00:00.000000000+0100', '2015-10-31T00:00:00.000000000+0000', '2015-11-30T00:00:00.000000000+0000'], dtype='datetime64[ns]') In [6]: pd.__version__ Out[6]: '0.13.1-339-g6c3755b' ```
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6,530
BUG: Regression from 0.13 in the treatment of numpy datetime64 non-ns dtypes in Series creation (GH6429)
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0
2014-03-03T16:53:04Z
2014-06-27T23:40:56Z
2014-03-03T19:13:31Z
CONTRIBUTOR
null
closes #6529
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MDExOlB1bGxSZXF1ZXN0MTMxMjIzNzk=
6,531
BUG/TST: fix several issues with slice bound checking code
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12
2014-03-03T17:04:55Z
2014-06-12T19:22:01Z
2014-03-05T11:20:16Z
CONTRIBUTOR
null
This patch removes more of what's left after removing pandas-specific customizations to slice indexing. BUG/TST: `obj.iloc[:,-4:-10]` should return empty slice (because slice.step > 0 and slice.start > slice.stop) There's no way to describe this by setting stop to a value in `[0; len]` interval, it should remain negative (see [this ticket in python bugtracker](http://bugs.python.org/issue11842)]. Proposed fix to this issue is to remove the custom logic altogether. A side-effect of that is correct handling of negative steps in presence of out-of-bounds slices, e.g. `obj.iloc[:,10:4:-1]` would've incorrectly returned empty slice. BUG: fix exception raised by `Series([1,2,3]).iloc[10:]`. After I've cleaned up suspicious code that handled empty-sequence keys in #6440, this piece of code started causing trouble: ``` python if start >= l: return self._getitem_axis(tuple(),axis=axis) ``` CLN: #6299 & #6443 rendered `core.indexing._check_slice_bounds` function useless and it's now gone. Next step would be to drop unused `raise_on_error` parameter in slice-related functions. Not sure, how do I put down all these relations into changelog, advice on that is welcome.
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index and items switched in series error message
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3
2014-03-03T19:11:00Z
2014-03-04T10:21:51Z
2014-03-04T05:51:21Z
CONTRIBUTOR
null
Running this code: ``` pd.Series([1, 2], index=[1, 2, 3, 4, 5]) ``` Raises this value error: ``` ValueError: Wrong number of items passed 5, indices imply 2 ``` I expected to see this: ``` ValueError: Wrong number of items passed 2, index implies 5 ``` Comparing my traceback with the current code it looks like an easy fix- just switch the order of the tuple in line 65 of [core/internals.py](https://github.com/pydata/pandas/blob/master/pandas/core/internals.py) Using Anaconda distribution: ``` In [6]: show_versions() INSTALLED VERSIONS ------------------ Python: 2.7.6.final.0 OS: Darwin Release: 13.1.0 Processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.13.0 Cython: 0.20 Numpy: 1.8.0 Scipy: 0.13.3 statsmodels: 0.5.0 patsy: 0.2.1 scikits.timeseries: Not installed dateutil: 1.5 pytz: 2013b bottleneck: Not installed PyTables: 3.1.0 numexpr: 2.3 matplotlib: 1.3.1 //anaconda/lib/python2.7/site-packages/openpyxl/__init__.py:31: UserWarning: The installed version of lxml is too old to be used with openpyxl warnings.warn("The installed version of lxml is too old to be used with openpyxl") openpyxl: 1.8.2 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: Not installed sqlalchemy: 0.9.2 lxml: 3.2.3 bs4: 4.3.1 html5lib: Not installed bigquery: Not installed apiclient: Not installed ```
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6,533
API change in groupby head and tail
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15
2014-03-03T23:54:07Z
2014-06-27T15:35:00Z
2014-03-06T00:21:26Z
CONTRIBUTOR
null
BUG/API groupby head and tail act like filter, since they dont aggregage fixes column selection Breaking API change to groupby head and tail, these were never aggregated output, so should not have the group labels as the index, it should always filter. (It was a legacy from when this was `g.apply(lambda x: x.head(n))`.) ``` as_index : boolean, default True For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output ``` Also fixes the column selection for head and tail part of #5755 related #6524 part of #5264. cc @jreback @TomAugspurger
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deprecation warning
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2014-03-04T03:11:31Z
2014-03-04T03:18:19Z
2014-03-04T03:18:19Z
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getting the following warning using pandas 0.13.1 on python 2.7.6 ``` /home/apratap/softwares/anaconda/lib/python2.7/site-packages/pandas/computation/ops.py:62: DeprecationWarning: object() takes no parameters return supr_new(klass, name, env, side=side, encoding=encoding) /home/apratap/softwares/anaconda/lib/python2.7/site-packages/pandas/computation/ops.py:62: DeprecationWarning: object() takes no parameters return supr_new(klass, name, env, side=side, encoding=encoding) ``` df = pandas.DataFrame({'a':[1,2], 'foo':['bar', 'bazzo']}) df.query('a == 1')
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6,535
BUG: informative error message with Series ValueError
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2
2014-03-04T03:51:05Z
2014-06-16T02:20:16Z
2014-03-04T05:51:10Z
CONTRIBUTOR
null
Fixes issue #6532.
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6,536
iloc with empty list raises IndexError
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2
2014-03-04T13:46:17Z
2014-03-06T23:01:26Z
2014-03-06T23:01:26Z
CONTRIBUTOR
null
When you slice a dataframe with an empty list you get an IndexError instead of an empty dataframe. Slicing with a non-empty list works fine. Tested in pandas 0.13.1. This is a regression from pandas 0.11.0. ``` python df = pd.DataFrame(np.arange(25.0).reshape((5,5)), columns=list('abcde')) ``` Slice with non-empty list. ``` python [5 rows x 5 columns] df.iloc[[1,2,3]] Out[34]: a b c d e 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 [3 rows x 5 columns] ``` Slice with empty list. ``` python df.iloc[[]] Traceback (most recent call last): File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/ipython-1.1.0_1_ahl1-py2.7.egg/IPython/core/interactiveshell.py", line 2830, in run_code exec code_obj in self.user_global_ns, self.user_ns File "<ipython-input-35-8806137779f8>", line 1, in <module> df.iloc[[]] File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 1028, in __getitem__ return self._getitem_axis(key, axis=0) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 1238, in _getitem_axis return self._get_loc(key, axis=axis) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 73, in _get_loc return self.obj._ixs(key, axis=axis) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 1588, in _ixs result = self.reindex(i, takeable=True) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 2162, in reindex **kwargs) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 1565, in reindex takeable=takeable).__finalize__(self) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 2117, in _reindex_axes fill_value, limit, takeable=takeable) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 2126, in _reindex_index takeable=takeable) File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/index.py", line 1233, in reindex return self[target], target File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/index.py", line 624, in __getitem__ result = arr_idx[key] IndexError: arrays used as indices must be of integer (or boolean) type ```
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Can't select from MultiIndex using .xs()
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2014-03-04T17:32:12Z
2014-03-04T17:32:12Z
CONTRIBUTOR
null
``` python df_param.index.levels[3] >>> Index([u'a', u't', u't_anti', u'v_anti', u'v_pro', u'v_stop'], dtype='object') df_param.xs(u'a', level=3) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-103-99e9d1eaffc4> in <module>() ----> 1 df_param.xs(u'a', level=3) /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in xs(self, key, axis, level, copy, drop_level) 1278 if axis == 1 and loc_is_slice: 1279 indexer = slice(None), indexer -> 1280 result = self.ix[indexer] 1281 setattr(result, result._get_axis_name(axis), new_ax) 1282 return result /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key) 52 pass 53 ---> 54 return self._getitem_tuple(key) 55 else: 56 return self._getitem_axis(key, axis=0) /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup) 595 def _getitem_tuple(self, tup): 596 try: --> 597 return self._getitem_lowerdim(tup) 598 except IndexingError: 599 pass /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup) 690 raise e1 691 try: --> 692 loc = ax0.get_loc(tup[0]) 693 except KeyError: 694 raise e1 /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key) 3096 return result 3097 else: -> 3098 return self._get_level_indexer(key, level=0) 3099 3100 def get_loc_level(self, key, level=0, drop_level=True): /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in _get_level_indexer(self, key, level) 3233 def _get_level_indexer(self, key, level=0): 3234 level_index = self.levels[level] -> 3235 loc = level_index.get_loc(key) 3236 labels = self.labels[level] 3237 /home/ipython/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key) 1015 loc : int if unique index, possibly slice or mask if not 1016 -> 1017 return self._engine.get_loc(_values_from_object(key)) 1018 1019 def get_value(self, series, key): /home/ipython/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3620)() /home/ipython/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3294)() TypeError: ``` Am I using it correctly or is this supposed to work like that?
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28,717,874
MDU6SXNzdWUyODcxNzg3NA==
6,538
weekofyear gives different results on a DatetimeIndex (if tz is given)
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1
2014-03-04T16:12:10Z
2017-05-01T11:39:28Z
2017-05-01T11:34:20Z
CONTRIBUTOR
null
Hello, The following code ``` dates = ["2013/12/29", "2013/12/30", "2013/12/31"] dates = pandas.DatetimeIndex(dates, tz="Europe/Brussels") print dates.weekofyear print [d.weekofyear for d in dates] ``` gives ``` [52 1 1] [52, 52, 1] ``` i.e. calling weekofyear on the datetimeindex directly or iterating on the different dates of the datetimeindex gives different results. Notice that if we remove the tz in the DatetimeIndex, the results are consistant. Sébastien
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6,539
np.sort(series) returns ndarray
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4
2014-03-04T16:58:30Z
2014-07-10T18:47:08Z
2014-03-04T17:52:33Z
CONTRIBUTOR
null
Applying numpy sort function to a Series returns an ndarray when you would expect a Series (and got a Series in pandas 0.11.0). Tested in pandas 0.13.1. ``` python s = pd.Series(np.arange(5.)[::-1]) np.sort(s) Out[68]: array([ 0., 1., 2., 3., 4.]) ``` This doesn't happen for all numpy functions e.g. ``` python np.power(s, 2) Out[70]: 0 16 1 9 2 4 3 1 4 0 dtype: float64 type(_) Out[71]: pandas.core.series.Series ```
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28,725,807
MDU6SXNzdWUyODcyNTgwNw==
6,540
Change groupby value_counts (from fall through behaviour)
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5
2014-03-04T17:49:10Z
2020-07-28T21:09:13Z
2020-07-28T21:09:13Z
CONTRIBUTOR
null
The fall through value_counts (for Series) is a bit strange, I think better result would be (with the standard options): Can put together if people think it's good. ``` In [132]: df = pd.DataFrame([['a_link', 'dofollow'], ['a_link', 'dofollow'], ['a_link', 'nofollow'], ['b_link', 'javascript']], columns=['link', 'type']) In [133]: g = df.groupby(['link', 'type']) In [134]: g.value_counts() AttributeError: 'DataFrameGroupBy' object has no attribute 'value_counts' In [135]: g.link.value_counts() # redundant level Out[135]: link type a_link dofollow a_link 2 nofollow a_link 1 b_link javascript b_link 1 dtype: int64 Following would make sense for DataFrameGroupby to: In [136]: pd.Series([len(g.groups[i]) for i in g.grouper.result_index], g.grouper.result_index) Out[136]: link type a_link dofollow 2 nofollow 1 b_link javascript 1 dtype: int64 ``` Note: as_index doesn't make sense here so would be ignored.
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6,541
BUG: duplicate indexed assignement
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0
2014-03-04T17:49:22Z
2014-03-04T19:10:20Z
2014-03-04T19:10:20Z
CONTRIBUTOR
null
from SO: http://stackoverflow.com/questions/22178642/assignment-modification-of-values-in-an-indexed-subframe-in-pandas ``` data = {'me':list('rttti'),'foo': list('aaade'), 'bar': arange(5)*1.34+2, 'bar2': arange(5)*-.34+2} df = pd.DataFrame(data).set_index('me') df.loc['r',['bar','bar2']]*=2.0 df.loc['t','bar']*=2.5 df.loc['t',['bar','bar2']]*=2.0 ```
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BUG: Bug in setitem with a duplicate index and an alignable rhs (GH6541)
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0
2014-03-04T18:33:03Z
2014-07-11T04:46:38Z
2014-03-04T19:10:20Z
CONTRIBUTOR
null
closes #6541
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inconsistent types resulting from Timestamp arithmetic
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2
2014-03-04T18:43:39Z
2014-03-05T22:16:05Z
2014-03-05T22:16:05Z
CONTRIBUTOR
null
Adding vs subtracting a datetime.timedelta from a Timestamp yields different return types: ``` python In [1]: timestamp = pd.Timestamp(datetime.datetime(2014, 3, 4)) In [2]: timestamp + datetime.timedelta(seconds=1) Out[2]: Timestamp('2014-03-04 00:00:01', tz=None) In [3]: timestamp - datetime.timedelta(seconds=1) Out[3]: datetime.datetime(2014, 3, 3, 23, 59, 59) ``` It's an easy fix, I have the code change done and I am just polishing up additional tests to document this behavior and ensure nothing else is broken. In #4547 I commented I was looking at another issue but hadn't created an issue for it, this is that issue. I realized it's probably better to have an issue, for release notes and the like.
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Fix irregular Timestamp arithmetic types #6543
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14
2014-03-04T20:11:49Z
2014-06-16T22:01:56Z
2014-03-05T22:15:50Z
CONTRIBUTOR
null
closes #6543 This is my first PR so I may have done a few things wrong. If I should have added my tests to a different file/location, please let me know. I should point out that this code is also tested by many other sections of the pandas codebase, on my first pass I hadn't captured the "Timestamp - datetime" case and all sorts of things blew up. Better now. :)
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Allow timestamp option for StataWriter.write_file()
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4
2014-03-04T20:33:38Z
2014-03-07T00:01:16Z
2014-03-07T00:01:16Z
CONTRIBUTOR
null
This is a combined feature request & minor bug notice. Feature Request: I would like to be able to write code that produces, byte-for-byte, reproducible outputs. To that end I want to write Stata dta files with a blank (or constant) timestamp. It would be nice to allow write_file() to accept a timestamp (or some option to zero it out). Bug: In an attempt to do this myself, I made my own version of StataWriter.write_file() where the only difference is I call (underscore)write_header() internal function with a constant timestamp. But that produces the following bug. ``` python import pandas as pd import numpy as np from pandas.io.stata import StataWriter import datetime df = pd.DataFrame(np.random.randn(6,4),index=list('abcdef'),columns=list('ABCD')) writer = StataWriter('ouput.dta', df) fktime_stamp = datetime.datetime.now() writer._write_header(time_stamp=fktime_stamp) # rest of write_file() ``` produces the following error ``` File "C:\Program Files\Python27\lib\site-packages\pandas\io\stata.py", line 1057, in _write_header elif not isinstance(time_stamp, datetime): TypeError: isinstance() arg 2 must be a class, type, or tuple of classes and types ``` My system details are. ``` >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: AMD64 Family 16 Model 6 Stepping 3, AuthenticAMD byteorder: little LC_ALL: None LANG: None pandas: 0.13.1 Cython: None numpy: 1.8.1 scipy: None statsmodels: None IPython: None sphinx: None patsy: None scikits.timeseries: None dateutil: 2.2 pytz: None bottleneck: None tables: None numexpr: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None sqlalchemy: None lxml: None bs4: None html5lib: None bq: None apiclient: None ```
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Inconsistent integer column indexing
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3
2014-03-04T22:38:57Z
2014-03-05T01:02:29Z
2014-03-05T01:02:29Z
NONE
null
When columns have an integer as their name, indexing them becomes inconsistent: ``` python >>> import pandas as pd >>> df = pd.DataFrame({1:[1,1],2:[2,2],'a':['a','a']}) >>> df 1 2 a 0 1 2 a 1 1 2 a [2 rows x 3 columns] >>> df[1] 0 1 1 1 Name: 1, dtype: int64 >>> df[['a',1]] a 1 0 a 1 1 a 1 [2 rows x 2 columns] >>> df[[1,2]] 2 a 0 2 a 1 2 a [2 rows x 2 columns] ``` Desired behavior would be: ``` python >>> df[[1,2]] 1 2 0 1 2 1 1 2 [2 rows x 2 columns] ``` This is in pandas 0.13.1
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New DataFrame display information?
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29
2014-03-05T00:17:37Z
2014-05-14T01:02:39Z
2014-05-14T00:47:57Z
CONTRIBUTOR
null
We are using DataFrames to hold information in a lot places now. E.g., ANOVA tables ``` df sum_sq mean_sq F PR(>F) C(Fitness) 2 672 336.000000 16.961538 0.000041 Residual 21 416 19.809524 NaN NaN [2 rows x 5 columns] ``` Where'd that bottom `[]` business come from? Is this new? Isn't this something that's better included in the `info` method?
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BUG: Bug in setitem with loc on mixed integer Indexes (GH6546)
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0
2014-03-05T00:25:42Z
2014-06-12T11:59:19Z
2014-03-05T01:02:29Z
CONTRIBUTOR
null
closes #6546
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Overflow adding pd.timedelta to pd.Timestamp
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2014-03-05T00:52:15Z
2015-08-29T01:39:57Z
2015-08-29T01:39:57Z
NONE
null
xref #9442 # Assume following code ``` python import pandas as pd additions = [1e9, 1e10] for ns in additions: added = pd.Timestamp(0) + pd.to_timedelta(str(ns), unit='ns') print added ``` This will work fine on Linux 64 bits. However, it won't work on Windows (both 32 and 64 bits). As for now, the following solution works fine but is not as elegant as the API nothing tells me that Timestamp(0).value will be nanoseconds in the next versions of API ``` python import pandas as pd additions = [1e9, 1e10] for ns in additions: added = pd.Timestamp(pd.Timestamp(0).value + ns) print added ``` I believe to know what is causing the bug, but I have no clue on how to set up the compilation environment on windows. I would suggest to change the `astype(int)` to `astype(long)` on the following line : https://github.com/pydata/pandas/blob/v0.13.1/pandas/tslib.pyx#L659 Anybody could try that fix or giving me insight on how to setup compilation environement on windows so I could try it myself.
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MDU6SXNzdWUyODc3NTY5MA==
6,550
DataFrame.rolling.mean resets windows with NaN
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12
2014-03-05T09:27:47Z
2020-05-08T19:21:16Z
null
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hello all, I posted a question on SO a few days ago regarding unexpected behaviour with rolling_mean(). http://stackoverflow.com/questions/22146105/compute-a-sequential-rolling-mean-in-pandas-as-array-function Looking at the sourse code this could have implications to a host functions that call rolling_mean(). Wondered what people's thought's were? Not sure if this could be called a bug but it certainly unexpected. Is it possible to implement a sparse option that uses the dropna() apply solution suggested behzad.nouri in the link ? Regards and many thanks for all the hard work on this package Copy paste example: ``` In [52]: import numpy as np ...: from pandas import * ...: ...: foo = DataFrame(np.arange(0.0,13.0)) ...: foo['1'] = np.arange(13.0,26.0) ...: foo.ix[4:6,0] = np.nan ...: foo.ix[4:7,1] = np.nan ...: ...: /anaconda3/envs/pandas-dev/bin/ipython:6: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated /anaconda3/envs/pandas-dev/bin/ipython:7: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated from IPython import start_ipython In [53]: foo.rolling(4).mean() Out[53]: 0 1 0 NaN NaN 1 NaN NaN 2 NaN NaN 3 1.5 14.5 4 NaN NaN 5 NaN NaN 6 NaN NaN 7 NaN NaN 8 NaN NaN 9 NaN NaN 10 8.5 NaN 11 9.5 22.5 12 10.5 23.5 ```
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6
2014-03-05T12:03:51Z
2014-10-15T13:40:48Z
2014-03-06T23:01:26Z
CONTRIBUTOR
null
This should fix #6536. There's a side issue though with loc/ix indexers that somehow discard column names for empty indexers.
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13
2014-03-05T12:59:50Z
2014-10-04T20:02:08Z
2014-10-04T20:02:08Z
CONTRIBUTOR
null
This issue was encountered when fixing #6536. It seems that `.loc` indexer with empty list key seems to lose names of respective axes: ``` python In [2]: pd.DataFrame(np.arange(9).reshape(3,3), columns=pd.Series(list('abc'), name='foobar')) Out[2]: foobar a b c 0 0 1 2 1 3 4 5 2 6 7 8 [3 rows x 3 columns] In [3]: _2.iloc[:,[]] Out[3]: Empty DataFrame Columns: [] Index: [0, 1, 2] [3 rows x 0 columns] In [4]: _2.iloc[:,[]].columns.names Out[4]: FrozenList([u'foobar']) In [5]: _2.loc[:,[]].columns.names Out[5]: FrozenList([None]) ``` It's not important, but currently #6551 has two tests skipped because of this bug.
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ENH: Allow timestamp and data label to be set when exporting to Stata
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9
2014-03-05T13:46:45Z
2014-07-16T08:57:07Z
2014-03-07T00:01:16Z
CONTRIBUTOR
null
closes #6545 Added code which allows the time stamp and the data label to be set using either StataWriter or to_stata. Also simplified reading these values using StataReader by removing null bytes from the string values read. Added basic test for both. Also fixed one small bug where variables could be stored using Stata reserved words.
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MDU6SXNzdWUyODc5Mzk3MA==
6,554
DOC: option to set the default table io.hdf.default_format starts in 0.13.0
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1
2014-03-05T14:41:19Z
2014-04-06T17:45:52Z
2014-04-06T17:45:52Z
CONTRIBUTOR
null
should be mentioned that it starts in 0.13.0 http://pandas.pydata.org/pandas-docs/stable/io.html#table-format
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6,555
Series.quantile() should convert input dtype to float
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1
2014-03-05T18:41:50Z
2014-03-06T23:35:30Z
2014-03-06T23:35:30Z
CONTRIBUTOR
null
Would love it if Series.quantile() converted the dtype to float (as DataFrame.quantile() appears to do). As it is, I get the following error with dtype=object: ``` In [8]: low Out[8]: ... Name: p, Length: 2218, dtype: object In [9]: low.quantile(0.33) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /ase/src/ase/analysis/conservation.py in <module>() ----> 1 low.quantile(0.33) /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/series.pyc in quantile(self, q) 1323 return pa.NA 1324 result = _quantile(valid_values, q * 100) -> 1325 if result.dtype == _TD_DTYPE: 1326 from pandas.tseries.timedeltas import to_timedelta 1327 return to_timedelta(result) AttributeError: 'float' object has no attribute 'dtype' ```
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28,826,556
MDU6SXNzdWUyODgyNjU1Ng==
6,556
Speed up for groupby filter for special case of len(x) < n ?
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5
2014-03-05T21:17:26Z
2018-07-06T21:32:28Z
2018-07-06T21:32:28Z
CONTRIBUTOR
null
@dsm054 posted ridiculous fast version for this, perhaps we can wrap it in an API with filter? http://stackoverflow.com/questions/22208562/replace-rarely-occurring-values-in-a-pandas-dataframe#comment33717682_22208838 cc @danielballan
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6,557
Performance regression in combine_first()
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2014-03-05T21:55:05Z
2014-03-06T00:14:59Z
2014-03-05T22:03:15Z
CONTRIBUTOR
null
I took another look at the testing that lead to #6479 and am still seeing ~73% slowdown. Benchmarking my application with v0.12.0 against 549a3902e2311c1c9cc3b065effe1f67d510475e found a 48x slowdown in `Series.combine_first` when using a `DatetimeIndex` with `MonthEnd` offsets. This is also present in v0.13.1, so it wasn't introduced by the fix to #6479 . The test case: ``` python from pandas import * rows = 2000 a = Series(np.random.random(rows), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M')) b = Series(np.random.random(rows), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M')) a.ix[-500:] = float('nan') %timeit a.combine_first(b) ``` And the comparison: ![perf_head](https://f.cloud.github.com/assets/440095/2339023/80451e14-a4b0-11e3-9b4e-b746ad9a0c8f.png) This regression does not exist for daily indices. Probably also exists for DataFrames and other offset frequencies. I'll create some vbenches later.
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6,558
BUG: Series.quantile raising on an object dtype (GH6555)
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0
2014-03-05T22:35:59Z
2014-06-16T07:04:38Z
2014-03-06T23:35:30Z
CONTRIBUTOR
null
closes #6555
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28,834,252
MDU6SXNzdWUyODgzNDI1Mg==
6,559
pandas groupby apply
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6
2014-03-05T22:56:31Z
2014-03-06T23:08:59Z
2014-03-06T23:08:59Z
NONE
null
df1.groupby(['deal_name']).apply(np.sum).to_html('D:\test.html') when apply functions to a groupby-ed dataframe, the html output is: <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>deal_name</th> <th>curr_bal</th> <th>orig_bal</th> </tr> <tr> <th>deal_name</th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th>RJ1410</th> <td> RJ1410RJ1410RJ1410RJ1410RJ1410RJ1410RJ1410RJ14...</td> <td> 12817608.5</td> <td> 13215877.1</td> </tr> </tbody> </table> the 2nd row is blank. is there a way to make it like this without reset_index making the dataframe flat? Potentially I need to group by two columns. <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th>deal_name</th> <th>deal_name</th> <th>curr_bal</th> <th>orig_bal</th> </thead> <tbody> <tr> <th>RJ1410</th> <td> RJ1410RJ1410RJ1410RJ1410RJ1410RJ1410RJ1410RJ14...</td> <td> 12817608.5</td> <td> 13215877.1</td> </tr> </tbody> </table>
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MDExOlB1bGxSZXF1ZXN0MTMyNDA4NTk=
6,560
BUG: preserve frequency across Timestamp addition/subtraction (#4547)
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14
2014-03-06T00:23:30Z
2014-06-27T01:15:56Z
2014-03-07T00:04:40Z
CONTRIBUTOR
null
closes #4547 My second PR, on basically the same code. Assuming people agree with my comments on #4547 (that users adding timedeltas that don't match frequencies better know what they are doing), then this would be my submission. I'm also assuming that something is up with Travis and it is not anything I have done - nosetests passes fine for me though Travis still dies more or less at startup, while it didn't on older code. That problem is true of builds not including any code I have added, e.g. https://travis-ci.org/pydata/pandas/jobs/20167915 . Maybe something in PR #6068, or does this kind of thing happen from time to time?
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28,842,765
MDU6SXNzdWUyODg0Mjc2NQ==
6,561
problem writing DateTime data to sqlite with sql.write_frame
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9
2014-03-06T01:26:16Z
2014-04-25T14:34:48Z
2014-04-25T14:34:48Z
NONE
null
Although this problem was [ostensibly solved](https://github.com/pydata/pandas/issues/6015), it still seems to affect the [latest](https://github.com/pydata/pandas/commit/1bab0a2d806dccf1b5bf6bf501d38b5f1a9b3bc7) commit as of the present time; the following code throws an `InterfaceError` at the `write_frame()` call; changing the dtype of the date column to something other than DateTime (e.g., by removing the parameter `parse_dates=[1]` in the call to `read_csv()`) circumvents the problem: ``` import StringIO import sqlite3 import pandas.io.sql data_csv = u"""foo,2012-01-01 foo,2012-01-02 bar,2011-01-01 qux,2011-01-01 """ buf = StringIO.StringIO(data_csv) df = pandas.read_csv(buf, header=None, names=['name', 'date'], parse_dates=[1]) conn = sqlite3.connect(':memory:') cursor = conn.cursor() cursor.execute('create table data (name string, date date)') pandas.io.sql.write_frame(df, 'data', conn, 'sqlite', 'append', False) ```
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6,562
to_datetime loses freq on DatetimeIndex with offset
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7
2014-03-06T06:04:33Z
2020-03-31T04:22:07Z
null
CONTRIBUTOR
null
It would be cool if the offset were kept. Perhaps even an error should be raised if not all of the `Timestamp`s have the same `offset`: ``` In [36]: dti = pd.to_datetime([pd.Timestamp("2014-1-1", offset="M"),]) dti Out[36]: <class 'pandas.tseries.index.DatetimeIndex'> [2014-01-01] Length: 1, Freq: None, Timezone: None ``` See #6560
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6,563
UTF-8 and read_csv: addressing data frame columns gives KeyError
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6
2014-03-06T11:08:54Z
2015-03-08T16:38:22Z
2015-03-08T16:38:22Z
NONE
null
When reading a csv, generated by Microsoft SQL Server management studio in UTF-8 encoding by following line of code: ``` df = pd.read_csv(file.csv) ``` gives problems when specific data frame columns are addressed. Reading the csv itself doesn't give any problem or warning. And it is possible to view the data frame (showing the proper header names). But when you want to address a specific column by its header name, the following error occurs: KeyError: 'no item named _header name_' When opening the file with a text editor and saving it in ANSI format, the issue isn't there anymore. This workflow helped to overcome this issue by accident, since I was not aware of any encoding issues at all. It would be a big time saver for a user when he is made aware of possible encoding issues at the moment of reading a file. If a column is shown properly and when you address this column by its name resulting in a KeyError is confusing and not helpful.
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MDU6SXNzdWUyODg5Nzg0NQ==
6,564
nosetests pandas error
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4
2014-03-06T17:28:36Z
2014-03-24T22:19:38Z
2014-03-06T17:32:41Z
NONE
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I got the following two errors running "nosetests pandas": ## FAIL: test_fred_multi (pandas.io.tests.test_data.TestFred) Traceback (most recent call last): File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/util/testing.py", line 1135, in wrapper return t(_args, *_kwargs) File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/io/tests/test_data.py", line 455, in test_fred_multi assert_frame_equal(received, expected, check_less_precise=True) File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/util/testing.py", line 509, in assert_frame_equal check_less_precise=check_less_precise) File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/util/testing.py", line 458, in assert_series_equal assert_almost_equal(left.values, right.values, check_less_precise) File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2464) File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1716) File "testing.pyx", line 140, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2300) AssertionError: expected 217.47800 but got 217.46600 # ## FAIL: test_fred_parts (pandas.io.tests.test_data.TestFred) Traceback (most recent call last): File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/util/testing.py", line 1135, in wrapper return t(_args, *_kwargs) File "/Users/hbi16088/python-virtual-env/basic/lib/python2.7/site-packages/pandas/io/tests/test_data.py", line 424, in test_fred_parts self.assertEqual(df.ix['2010-05-01'][0], 217.23) AssertionError: 217.29900000000001 != 217.23
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6,565
Unstacked data in unexpected order
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2014-03-06T17:56:16Z
2014-03-06T23:15:39Z
2014-03-06T23:15:39Z
NONE
null
After unstacking data that were grouped using groupby the data appears in an unexpected order. The groupby data are in the ascending order, but after unstacking this is no longer true. The following code demonstrates: import numpy as np import pandas as pd print pd.**version** # random data data = pd.DataFrame() data['a'] = np.random.random(1000)_10. - 2 data['b'] = np.random.random(1000)_10. - 2 data['c'] = np.random.random(1000)*10. - 2 # bins in a and b ba = np.linspace(-5., 5, 8) bb = np.linspace(-5., 5, 9) # groupby bins groups = data.groupby([pd.cut(data['a'], ba), pd.cut(data['b'], bb)]) # mask, needs at least 10 to get robust std estimate msk = groups.count()['c'] > 10 # std(c) per bin, these are ordered as expected stdc = groups['c'].agg([np.std, len])[msk] print stdc # now unstack result = stdc['std'].unstack() # and the results are not ordered as expected print result and the result is: b (-1.25, 0] (-2.5, -1.25] (0, 1.25] (1.25, 2.5] \ a (-0.714, 0.714] 2.878018 NaN 3.019754 2.908735 (-2.143, -0.714] 2.490683 NaN 2.809243 2.754959 (0.714, 2.143] 2.656060 2.940749 2.421975 2.677205 (2.143, 3.571] 3.128538 2.879978 3.038488 3.191041 (3.571, 5] 2.534340 2.943076 3.215626 2.687091 b (2.5, 3.75] (3.75, 5] a (-0.714, 0.714] 3.328777 3.305938 (-2.143, -0.714] 2.716552 3.090601 (0.714, 2.143] 3.093307 1.669262 (2.143, 3.571] 2.885055 3.295693 (3.571, 5] 3.333355 2.972684 [5 rows x 6 columns] This seems to happen if the bins cross zero.
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TST: TravisCI failures
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6
2014-03-06T20:31:07Z
2014-03-15T16:07:01Z
2014-03-06T22:41:35Z
CONTRIBUTOR
null
It looks like Travis is failing to install the dependencies recently. In `ci/install.sh`, we run ``` pip install $PIP_ARGS -r ci/requirements-${wheel_box}.txt ``` where `PIP_ARGS` (I think) is something like `-I --use-wheel --find-links=http://cache27diy-cpycloud.rhcloud.com/2.7/ --allow-external --allow-insecure ` depending on which version. Which produces this error (sometimes): ``` python Downloading/unpacking python-dateutil==2.1 (from -r ci/requirements-2.7.txt (line 1)) http://cache27diy-cpycloud.rhcloud.com/2.7/ uses an insecure transport scheme (http). Consider using https if cache27diy-cpycloud.rhcloud.com has it available Downloading python_dateutil-2.1-py27-none-any.whl (unknown size): 118kB downloaded Cleaning up... Exception: Traceback (most recent call last): File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/basecommand.py", line 122, in main status = self.run(options, args) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/commands/install.py", line 274, in run requirement_set.prepare_files(finder, force_root_egg_info=self.bundle, bundle=self.bundle) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/req.py", line 1173, in prepare_files self.unpack_url(url, location, self.is_download) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/req.py", line 1320, in unpack_url retval = unpack_http_url(link, location, self.download_cache, self.download_dir, self.session) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/download.py", line 587, in unpack_http_url unpack_file(temp_location, location, content_type, link) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/util.py", line 621, in unpack_file unzip_file(filename, location, flatten=not filename.endswith(('.pybundle', '.whl'))) File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pip/util.py", line 491, in unzip_file zip = zipfile.ZipFile(zipfp) File "/usr/lib/python2.7/zipfile.py", line 714, in __init__ self._GetContents() File "/usr/lib/python2.7/zipfile.py", line 748, in _GetContents self._RealGetContents() File "/usr/lib/python2.7/zipfile.py", line 763, in _RealGetContents raise BadZipfile, "File is not a zip file" BadZipfile: File is not a zip file ``` I've reproduced this locally. Can we verify that the wheels hosted at `cache27diy-cpycloud.rhcloud.com/2.7/` were built correctly? I'm guessing `cpcloud` refers to @cpcloud?
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28,919,028
MDExOlB1bGxSZXF1ZXN0MTMyODQ4OTU=
6,567
BLD: change wheel url to pandas.pydata.org
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0
2014-03-06T22:01:25Z
2014-06-23T13:11:39Z
2014-03-06T22:41:36Z
CONTRIBUTOR
null
BLD: remove scikit-timeseries from the deps and builds closes #6566
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MDU6SXNzdWUyODk0NzYwNg==
6,568
Option to set large_repr to info(verbose=False) missing
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13
2014-03-07T09:16:44Z
2014-05-14T22:25:41Z
2014-05-14T22:25:41Z
CONTRIBUTOR
null
In v0.13.1 the default representation for pd.DataFrame changed. I would like to be able to restore the previous default (see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#dataframe-repr-changes) The option display.large_repr only allows to set info(verbose=True) as default. The default in previous version of pandas was info(verbose=False) however. The effect is that displaying a DataFrame with a large number of columns creates a lot of output. Am I missing an easy way to change this behavior? Otherwise I suggest adding an option, e.g. `truncate`, `info_short`, `info_long`. display.large_repr: [default: truncate] [currently: info] : 'truncate'/'info' ``` For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a truncated table (the default from 0.13), or switch to the view from df.info() (the behaviour in earlier versions of pandas). ``` pd.show_versions() ## INSTALLED VERSIONS commit: None python: 2.7.6.final.0 python-bits: 64 OS: Windows OS-release: 8 machine: AMD64 processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.13.1 Cython: None numpy: 1.8.0 scipy: 0.13.3 statsmodels: 0.5.0 IPython: 1.2.0 sphinx: 1.2.1 patsy: 0.2.1 scikits.timeseries: None dateutil: 2.2 pytz: 2013.9 bottleneck: 0.8.0 tables: 3.1.0 numexpr: 2.3 matplotlib: 1.3.1 openpyxl: 1.8.3 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: 0.5.2 sqlalchemy: None lxml: 3.3.1 bs4: 4.3.2 html5lib: 0.999 bq: None apiclient: None
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6,569
ENH/BUG groupby nth now filters, works with DataFrames
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8
2014-03-07T17:50:43Z
2014-07-03T18:44:46Z
2014-03-07T20:24:22Z
CONTRIBUTOR
null
fixes #5552 partial for #5264 ``` In [101]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) In [102]: g = df.groupby('A') In [103]: g.nth(0) Out[103]: A B 0 1 NaN 2 5 6 In [104]: g.nth(1) Out[104]: A B 1 1 4 In [105]: g.nth(-1) Out[105]: A B 1 1 4 2 5 6 In [106]: g.nth(0, dropna='any') # old behaviour-like Out[106]: B A 1 4 5 6 In [107]: g.nth(1, dropna='any') # old behaviour-like Out[107]: B A 1 NaN 5 NaN ```
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28,987,607
MDExOlB1bGxSZXF1ZXN0MTMzMjIzNTQ=
6,570
FIX use selected_obj rather the obj throughout groupby
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15
2014-03-07T18:33:48Z
2014-06-30T09:34:37Z
2014-03-09T19:09:40Z
CONTRIBUTOR
null
fixes many parts of for #5264... Changes lots of `self.obj` to `self._selected_obj`. probably should vbench before merging? cc @jreback @TomAugspurger
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28,987,918
MDU6SXNzdWUyODk4NzkxOA==
6,571
Unable to access multidimensional ndarray in a Series (regression)
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2
2014-03-07T18:37:55Z
2014-03-07T19:06:08Z
2014-03-07T18:43:05Z
NONE
null
``` python import numpy as np import pandas as pd arr = np.array([[8,8],[9,9]]) s = pd.Series([3, arr]) s[1] ``` Results in ``` Exception Traceback (most recent call last) <ipython-input-34-88de191fe097> in <module>() ----> 1 s[1] /usr/lib/python2.7/dist-packages/pandas/core/series.pyc in __getitem__(self, key) 491 result = self.index.get_value(self, key) 492 if isinstance(result, np.ndarray): --> 493 return self._constructor(result,index=[key]*len(result)).__finalize__(self) 494 return result 495 except InvalidIndexError: /usr/lib/python2.7/dist-packages/pandas/core/series.pyc in __init__(self, data, index, dtype, name, copy, fastpath) 218 else: 219 data = _sanitize_array(data, index, dtype, copy, --> 220 raise_cast_failure=True) 221 222 data = SingleBlockManager(data, index, fastpath=True) /usr/lib/python2.7/dist-packages/pandas/core/series.pyc in _sanitize_array(data, index, dtype, copy, raise_cast_failure) 2598 elif subarr.ndim > 1: 2599 if isinstance(data, pa.Array): -> 2600 raise Exception('Data must be 1-dimensional') 2601 else: 2602 subarr = _asarray_tuplesafe(data, dtype=dtype) Exception: Data must be 1-dimensional ``` It used to work and my older code stopped working. Do you have any suggestions for a workaround? ## INSTALLED VERSIONS commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 3.12-1-amd64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_GB.UTF-8 pandas: 0.13.1 Cython: 0.20 numpy: 1.7.1 scipy: 0.12.0 statsmodels: 0.4.2 IPython: 1.2.1 sphinx: 1.2.1 patsy: None scikits.timeseries: None dateutil: 1.5 pytz: 2012c bottleneck: None tables: 2.4.0 numexpr: 2.2.2 matplotlib: 1.3.1 openpyxl: 1.7.0 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: None sqlalchemy: None lxml: 3.3.1 bs4: 4.2.1 html5lib: 0.999 bq: None apiclient: None
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ValueError: Cannot compare tz-naive and tz-aware timestamps
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2
2014-03-07T19:10:21Z
2014-07-21T11:44:04Z
2014-07-21T11:43:55Z
NONE
null
Trying to generate a time series based on timezone raises exception. ``` >>> import pandas >>> pandas.date_range('2013-01-01T00:00:00+05:30','2014-03-07T23:59:59+05:30',freq='AS') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages/pandas/tseries/index.py", line 1794, in date_range closed=closed) File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages/pandas/tseries/index.py", line 196, in __new__ infer_dst=infer_dst) File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages/pandas/tseries/index.py", line 406, in _generate index = _generate_regular_range(start, end, periods, offset) File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages/pandas/tseries/index.py", line 1750, in _generate_regular_range dates = list(xdr) File "/Users/cruiser/work/metroleads/lib/python2.7/site-packages/pandas/tseries/offsets.py", line 1871, in generate_range if periods is None and end < start: File "tslib.pyx", line 611, in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:10872) File "tslib.pyx", line 640, in pandas.tslib._Timestamp._assert_tzawareness_compat (pandas/tslib.c:11186) ValueError: Cannot compare tz-naive and tz-aware timestamps ``` ``` pandas==0.13.1 ```
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ENH: Default for Excel sheetname
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5
2014-03-07T20:14:08Z
2014-03-12T13:12:25Z
2014-03-12T13:12:25Z
CONTRIBUTOR
null
Excel workbooks have 'Sheet1' as the default sheet. Can `pd.read_excel` have this as a default? `pandas.io.excel.read_excel(io, sheetname='Sheet1', **kwds)` This makes pandas more accessible to the user transitioning from Excel by avoiding the common error below: ``` In [9]: test = pd.read_excel('/Users/clark/Desktop/test.xlsx') --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-9-14880f6f18e4> in <module>() ----> 1 test = pd.read_excel('/Users/clark/Desktop/test.xlsx') TypeError: read_excel() takes exactly 2 arguments (1 given) ```
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xs is filling nan in index with its last item, as if sorted ascending, in the resulting index
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2
2014-03-07T20:29:09Z
2014-03-09T15:15:14Z
2014-03-09T15:15:00Z
NONE
null
Illustration: ``` python acc = [ ('a','abcde',1), ('b','bbcde',2), ('y','yzcde',25), ('z','xbcde',24), ('z',None,26), ('z','zbcde',25), ('z','ybcde',26), ] df1 = pd.DataFrame(acc, columns=['a1','a2','cnt']).set_index(['a1','a2']) ``` ``` In [476]: df1 Out[476]: cnt a1 a2 a abcde 1 b bbcde 2 y yzcde 25 z xbcde 24 NaN 26 zbcde 25 ybcde 26 [7 rows x 1 columns] In [477]: df1.xs('z',level='a1') Out[477]: cnt a2 xbcde 24 zbcde 26 zbcde 25 ybcde 26 [4 rows x 1 columns] ``` I was expecting: ``` cnt a2 xbcde 24 NaN 26 zbcde 25 ybcde 26 ``` because I thought it would preserve the index of df1. Sorting explicitly doesn't seem to affect the result: ``` In [478]: df1.sort('cnt',ascending=False) Out[478]: cnt a1 a2 z ybcde 26 NaN 26 zbcde 25 y yzcde 25 z xbcde 24 b bbcde 2 a abcde 1 [7 rows x 1 columns] In [479]: df1.sort('cnt',ascending=False).xs('z',level='a1') Out[479]: cnt a2 ybcde 26 zbcde 26 zbcde 25 xbcde 24 [4 rows x 1 columns] ``` It might be related to [forward filling](https://github.com/pydata/pandas/issues/5669#issuecomment-30170618), but then I think it would be: ``` cnt a2 ybcde 26 ybcde 26 zbcde 25 xbcde 24 ``` which still isn't what I was expecting. Versions and dependencies: ``` In [480]: pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.2.final.0 python-bits: 64 OS: Darwin OS-release: 12.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 pandas: 0.13.1 Cython: 0.17.2 numpy: 1.6.2 scipy: 0.13.3 statsmodels: 0.5.0 IPython: 1.1.0 sphinx: 1.2 patsy: 0.2.1 scikits.timeseries: None dateutil: 1.5 pytz: 2012h bottleneck: None tables: 3.0.0 numexpr: 2.0.1 matplotlib: 1.3.1 openpyxl: None xlrd: 0.8.0 xlwt: None xlsxwriter: None sqlalchemy: None lxml: None bs4: None html5lib: None bq: None apiclient: None ```
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6,575
ENH: including offset/freq in Timestamp repr (#4553)
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31
2014-03-07T22:19:12Z
2014-06-24T18:38:06Z
2014-03-10T20:01:30Z
CONTRIBUTOR
null
fixes #4553 Hopefully the `test_repr()` implementation I included was sufficient. I see some `test_repr()` instances in the code that are more minimal - literally just making sure that repr(obj) doesn't blow up. And some which are pretty detailed, which makes me worry they might be a bit fragile. I went more middle-of-the-road. ``` python In [1]: from pandas import Timestamp In [2]: date = '2014-03-07' In [3]: tz = 'US/Eastern' In [4]: freq = 'M' In [5]: repr(Timestamp(date)) # date_only_repr Out[5]: "Timestamp('2014-03-07 00:00:00')" In [6]: repr(Timestamp(date, tz=tz)) # date_tz_repr Out[6]: "Timestamp('2014-03-07 00:00:00-0500', tz='US/Eastern')" In [7]: repr(Timestamp(date, offset=freq)) # date_freq_repr Out[7]: "Timestamp('2014-03-07 00:00:00', offset='M')" In [8]: repr(Timestamp(date, tz=tz, offset=freq)) # date_tz_freq_repr Out[8]: "Timestamp('2014-03-07 00:00:00-0500', tz='US/Eastern', offset='M')" In [9]: repr(Timestamp('2014-03-13 00:00:00-0400', tz=None)) # date_with_utc_offset_repr Out[9]: "Timestamp('2014-03-13 00:00:00-0400')" ```
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API: default value for read_excel sheet
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12
2014-03-08T06:23:16Z
2014-06-18T20:59:40Z
2014-03-12T13:12:25Z
CONTRIBUTOR
null
closes #6573 Implements a default sheetname of 0 for `read_excel` #6573. Before: `read_excel(io, sheetname, **kwds)` After: `read_excel(io, sheetname=0, **kwds)`
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6,577
CLN: Change assert_([not] isinstance(a,b)) to specialized forms
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6
2014-03-08T15:30:22Z
2014-07-16T08:57:26Z
2014-03-09T14:48:34Z
CONTRIBUTOR
null
Work on #6175. Changes instances of assert_([not] isinstance(a,b)) to specialized assert[Not]IsInstance(a, b).
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6,578
FIX raise when groupby selecting cols not in frame
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15
2014-03-09T09:14:10Z
2014-07-16T08:57:30Z
2014-05-02T12:51:52Z
CONTRIBUTOR
null
KeyError part of #5264
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BUG: Bug in .xs with a nan in level when dropped (GH6574)
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0
2014-03-09T14:46:56Z
2014-06-22T14:52:08Z
2014-03-09T15:15:00Z
CONTRIBUTOR
null
closes #6574
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6,580
CLN: Finish changing assert_(...) to specialized forms
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2014-03-09T15:41:55Z
2014-06-30T15:30:00Z
2014-03-09T16:05:42Z
CONTRIBUTOR
null
Finishes #6175, to the extent that everything remaining can be mapped to assertTrue or assertFalse, unless larger refactorings are desired.
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DEPR: Clean up list of deprecations from prior versions
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2014-03-09T23:47:17Z
2020-01-03T14:13:55Z
2020-01-03T14:13:54Z
CONTRIBUTOR
null
Log for previous deprecations, once they are actually removed, move the issue to #13777 We try to keep these for *three* major versions as actual deprecations. e.g. deprecate in 0.17, 0.18 & 0.19 get the warning, removed in 0.20. ## 0.24.0 - [x] #24596 `__array__` for tzaware Series/Index (0.24.0)
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ENH: Generalize cut/melt to handle datetime input
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19
2014-03-10T01:03:50Z
2018-07-06T21:28:02Z
2018-07-06T21:28:02Z
NONE
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see also #6434 Originally from: http://stackoverflow.com/questions/22286930/is-it-possible-to-use-cut-on-a-collection-of-datetimes?noredirect=1#comment33862629_22286930 Would it be possible to enhance the cut function to handle datetime inputs? For example, say I have the following setup: ``` import pandas as pd import numpy as np np.random.seed(0) obs_list = ['John', 'Paul', 'George', 'Ringo'] date_indices = [pd.date_range('2014-01-01', '2014-02-01', freq=f) for f in ['1d', '3h', '25min']] dfs = [pd.DataFrame({'observer': np.random.choice(obs_list, len(di)), 'value': np.random.randn(len(di))+ (100*i)}, index=di) for i, di in enumerate(date_indices)] bigframe = pd.concat(dfs) ``` I would like to split up `bigframe` into 7 "bins" and explore the properties within each bin. I'd like the bins to be sequential and for each to contain (approxmiately) the same number of data points. Ideally, I'd be able to do something like: ``` means = bigframe.groupby(pd.cut(bigframe.index, 7))['value'].mean() ``` I think I can get something like that by doing: ``` bigframe['dt_value'] = bigframe.index.map(lambda x: x.value) means = bigframe.groupby(pd.cut(bigframe['dt_value'], 7))['value'].mean() ``` but that doesn't seem particularly elegant. And it winds up with bins labelled with the datetime values instead of the more-nicely-readable datetimes themselves.
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DOC: Cookbook example on reading CSV by chunking gives error
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5
2014-03-10T11:26:56Z
2014-04-23T16:26:27Z
2014-04-23T16:26:27Z
CONTRIBUTOR
null
URL linked in the cookbook: http://stackoverflow.com/questions/11622652/large-persistent-dataframe-in-pandas/12193309#12193309 Traceback: ``` python File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 929, in concat verify_integrity=verify_integrity) File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 944, in __init__ '"{0}"'.format(type(objs).__name__)) AssertionError: first argument must be a list-like of pandas objects, you passed an object of type "TextFileReader" ``` To be precise: Concatenation does not work in this example.
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Custom-business-days offsets very slow
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2014-03-18T10:08:23Z
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CONTRIBUTOR
null
The [custom-business-days](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#custom-business-days-experimental) are currently significantly slower (around factor 4) compared to pd.offsets.BusinessDay(). Without actually specifying any custom business days: ``` import datetime as dt import pandas as pd date = dt.datetime(2011,1,1) cday = pd.offsets.CustomBusinessDay() %timeit date + pd.offsets.BusinessDay() # 6.59 µs %timeit date + cday # 26.1 µs ``` Profiling pd.offsets.CustomBusinessDay.apply shows that only around 13% of the time is spent in np.busday_offset. The majority of time is spent casting the dates from datetime to datetime64 etc. I'm not so familiar with the code but one idea would be to work in datetime by default and try to stick with it as much as possible. The method could then look something like this: ``` def apply(self, other): if not isinstance(other,datetime): other = other.astype(dt.datetime) if self.n <= 0: roll = 'forward' else: roll = 'backward' dt_str = other.strftime('%Y-%m-%d') result = np.busday_offset(dt_str,self.n,roll=roll,busdaycal=self.busdaycalendar) result = result.astype(dt.datetime) if not self.normalize: result = dt.datetime.combine(result,other.time()) if self.offset: print self.offsets result = result + self.offset return result ``` While this might not be a perfect comparison because I left out some conversion code, the changes yield a sizable speedup. ``` %timeit date + cday # 14.9 µs ``` Ultimately I would like to have a `UsBday` offset. The code looks like this: ``` import datetime as dt import numpy as np import pandas as pd class UsBday(object): def __init__(self): self.gen_cal() def gen_cal(self): holidays = [] for hlday in ['new_years','mlk_day', 'presidents_day','good_friday', 'memorial_day','independence_day',' labor_day','thanksgiving','xmas_holiday']: hlday_func = getattr(self,hlday) tmp_holidays = [ hlday_func(year) for year in range(1950,dt.datetime.today().year+2) ] holidays.extend(tmp_holidays) self.cal = np.busdaycalendar(holidays=holidays) self.bday_offset = pd.offsets.CustomBusinessDay(holidays=holidays) def nxt_bday(self,dt): nxt_bday = np.busday_offset( dt.strftime('%Y-%m-%d'),offsets=0, roll='forward',busdaycal=self.cal) return nxt_bday # - holiday definitions - # @staticmethod def new_years(year): cand = dt.datetime(year,1,1) dt_str = cand.strftime('%Y-%m-%d') res = np.busday_offset(dt_str,offsets=0,roll='forward') return res @staticmethod def mlk_day(year): # third monday in January dt_str = dt.datetime(year,1,1).strftime('%Y-%m-%d') res = np.busday_offset(dt_str, 2, roll='forward', weekmask='Mon') return res @staticmethod def presidents_day(year): # third monday February dt_str = dt.datetime(year,2,1).strftime('%Y-%m-%d') res = np.busday_offset(dt_str, 2, roll='forward', weekmask='Mon') return res @staticmethod def good_friday(year): from dateutil.easter import easter easter_sun = easter(year) gr_fr = easter_sun - 2 * pd.offsets.Day() return gr_fr.strftime('%Y-%m-%d') @staticmethod def memorial_day(year): # final Monday of May dt_str = dt.datetime(year,1,1).strftime('%Y-%m-%d') res = np.busday_offset(dt_str, -1, roll='forward', weekmask='Mon') return res @staticmethod def independence_day(year): # July 4th cand = dt.datetime(year,7,4).strftime('%Y-%m-%d') return cand @staticmethod def labor_day(year): # first Monday in September res = np.busday_offset(str(year) + '-09',offsets=0, roll='forward',weekmask='Mon') return res @staticmethod def thanksgiving(year): # fourth thursday in november res = np.busday_offset(str(year) + '-11',offsets=-1, roll='forward',weekmask='Thu') return res @staticmethod def xmas_holiday(year): cand = dt.datetime(year,12,25) while not np.is_busday(cand.strftime('%Y-%m-%d')): if cand.weekday() == 6: cand += pd.offsets.BDay() elif cand.weekday() == 5: cand -= pd.offsets.BDay() else: cand += pd.offsets.BDay() return cand.strftime('%Y-%m-%d') us_bday = UsBday() us_offset = .us_bday.bday_offset %timeit date + us_offset: 26.1 µs # Using numpy directly %timeit us_bday.nxt_bday(date) # 7.85 µs ``` My first intuition when I noticed that custom business days are slower was that this is due to the large list of holidays passed to numpy. The timings at the end of the code block, however, show that adding a custom business day with realistic holidays does not alter the performance by much. The main speed difference results from interfacing with numpy and is therefore a Pandas issue. I know that CustomBusinessDays is in experimental mode. I hope this feedback can help improve it because I think it is an important feature in Pandas. Also perhaps it would be nice to ship certain custom calendars, for example for the US, directly with Pandas.
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MDU6SXNzdWUyOTEwMDU1OQ==
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groupby - transform not working with multiple columns
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2
2014-03-10T14:47:55Z
2014-06-17T12:08:52Z
2014-06-17T12:08:52Z
CONTRIBUTOR
null
I came across this issue while looking at the [this](http://stackoverflow.com/questions/22298542) question on stackoverflow; say I have a data-frame like: ``` >>> df tag val1 val2 0 B 0 0 1 A 1 2 2 A 2 4 3 B 3 6 4 B 4 8 ``` and I want to reverse columns `val1` and `val2` within each `tag`; this works like a charm: ``` >>> grb = df.groupby('tag') >>> df['val1'] = grb['val1'].transform(lambda ts: ts.loc[::-1]) >>> df['val2'] = grb['val2'].transform(lambda ts: ts.loc[::-1]) >>> df tag val1 val2 0 B 4 8 1 A 2 4 2 A 1 2 3 B 3 6 4 B 0 0 ``` but, if I want to do this at the same time for columns `val1` and `val2`, it breaks: ``` >>> grb = df.groupby('tag') >>> grb['val1', 'val2'].transform(lambda obj: obj.loc[::-1]) val1 val2 0 0 0 1 1 2 2 2 4 3 3 6 4 4 8 [5 rows x 2 columns] ``` obviously I will not get the correct answer if I overwrite `df` with the new data ( or append the new columns ); however, this ``` >>> grb = df.groupby('tag') >>> grb['val1', 'val2'].transform(lambda obj: obj.values[::-1]) val1 val2 0 4 8 1 2 4 2 1 2 3 3 6 4 0 0 [5 rows x 2 columns] ``` works as expected; which makes me guess this has something to do with the index being aligned with the original data-frame, whereas in here it should be ignored;
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MDU6SXNzdWUyOTEwMDU3Nw==
6,586
Plot issue, timeseries always honored,
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2
2014-03-10T14:48:06Z
2017-11-24T10:34:26Z
2017-11-24T10:34:22Z
NONE
null
When work with tick data, and the density of data is not time evenly distributed. When I use dataframe plot function, the default plot always honor by time, not by the number of data points. I have to convert the datetimeindex back to string, and plot correspondingly, isn't it handy to make an extra flag to plot with number of data evenly spaced and still locate the right time label on x axis? For example the 3rd and 4th graph in this link which plot with the same data http://stackoverflow.com/questions/22258162/python-pandas-plot-with-uneven-timeseries-index-with-count-evenly-distributed
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fillna() on Series or DataFrame containing datetime64 mess the values
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2
2014-03-10T15:47:31Z
2014-03-10T18:14:52Z
2014-03-10T18:14:52Z
NONE
null
s = pd.Series([pd.NaT, pd.NaT, '2013-08-05 15:30:00.**000001**']) print s 0 NaT 1 NaT 2 2013-08-05 15:30:00.**000001** dtype: datetime64[ns] print s.fillna(method='backfill') 0 2013-08-05 15:30:00.**000001024** 1 2013-08-05 15:30:00.**000001024** 2 2013-08-05 15:30:00.**000001024** dtype: datetime64[ns] This happens with pandas 0.13.1 and numpy 1.8.0
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6,588
BUG: Bug in fillna with method = bfill/ffill and datetime64[ns] dtype (GH6587)
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0
2014-03-10T17:16:19Z
2014-07-16T08:57:38Z
2014-03-10T18:14:52Z
CONTRIBUTOR
null
closes #6587
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29,122,750
MDU6SXNzdWUyOTEyMjc1MA==
6,589
CLN: remove print statements when sep=None is passed to read_csv
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0
2014-03-10T19:01:51Z
2014-03-12T14:22:51Z
2014-03-12T14:22:51Z
CONTRIBUTOR
null
http://stackoverflow.com/questions/22308688/using-python-parser-to-sniff-delimiter-spammed-to-stdout or control via `verbose` keyword
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MDU6SXNzdWUyOTEyNzkwMQ==
6,590
BUG: impossible to select string with special character from DataFrame via query
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3
2014-03-10T20:06:48Z
2014-03-12T12:44:24Z
2014-03-12T12:44:24Z
NONE
null
Field (columns) not accept special character to filter via query, sample: Source: ``` df = DataFrame({'Licença': [1, 2, 3, 4]}) df.query("Licença == 1") ``` Error: ``` Traceback (most recent call last): File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/tornado/web.py", line 1192, in _stack_context_handle_exception raise_exc_info((type, value, traceback)) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/tornado/web.py", line 1375, in wrapper result = method(self, *args, **kwargs) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/tornado/gen.py", line 146, in wrapper result = func(*args, **kwargs) File "/home/avelino/projects/mining/mining/views.py", line 83, in open df = df.query(df_generate(df, self.get_argument(f), f)) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/core/frame.py", line 1803, in query res = self.eval(expr, **kwargs) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/core/frame.py", line 1854, in eval return _eval(expr, **kwargs) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/computation/eval.py", line 207, in eval truediv=truediv) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/computation/expr.py", line 762, in __init__ self.terms = self.parse() File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/computation/expr.py", line 781, in parse return self._visitor.visit(self.expr) File "/home/avelino/.virtualenvs/mining/lib/python2.7/site-packages/pandas/computation/expr.py", line 442, in visit node = ast.fix_missing_locations(ast.parse(clean)) File "/opt/python2.7/lib/python2.7/ast.py", line 37, in parse return compile(source, filename, mode, PyCF_ONLY_AST) File "<unknown>", line 1 Licença == 'Ativo por kit' ```
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6,591
BUG: replace iterrows with itertuples in sql insert (GH6509)
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5
2014-03-10T20:57:44Z
2014-06-20T10:46:15Z
2014-03-11T12:05:51Z
MEMBER
null
Fixes #6509. Should still add a test
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6,592
Improve performance for custom business days (GH6584)
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9
2014-03-10T22:48:22Z
2014-06-13T22:07:35Z
2014-03-18T10:08:23Z
CONTRIBUTOR
null
closes #6584 Tests are passing. Incrementing datetime with custom dateoffset is at about twice the speed now. This could probably be optimized more.
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FIX filter selects selected columns
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2014-03-11T00:40:38Z
2014-07-02T01:18:18Z
2014-03-11T00:54:21Z
CONTRIBUTOR
null
fixes #6512 TST for selected groupby add resample ohlc and filter add couple of tests for #5264
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ohlc not available for groupby/etc
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2014-03-11T00:52:10Z
2016-04-18T18:13:04Z
2016-04-18T18:13:04Z
CONTRIBUTOR
null
Was this available at some stage (it's looking in `self._cython_functions` for it rather than just saying no dice with a plain ol' AttributeError): ``` In [1]: rng = pd.date_range('2012', periods=3) In [2]: df = pd.DataFrame([[1, 2], [1, 3], [5, 6]], rng, columns=['A', 'B']) In [3]: g = df.groupby('A') In [4]: g.foo() AttributeError: 'DataFrameGroupBy' object has no attribute 'foo' In [5]: g.ohlc() KeyError: 'ohlc' In [6]: g.ohlc(rule='D') TypeError: ohlc() got an unexpected keyword argument 'rule' ``` Can do atm using `g.resample('D', how='ohlc')`.
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MDU6SXNzdWUyOTE0Nzk3NQ==
6,595
current master compile fails
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2014-03-11T01:42:18Z
2014-03-11T02:15:19Z
2014-03-11T01:57:23Z
CONTRIBUTOR
null
$ git log -1 commit a265c5c676039af160890eca949a0a3562277fe6 Merge: 26e8fa8 022ab40 Author: Andy Hayden <xxxx> Date: Mon Mar 10 17:54:21 2014 -0700 ``` Merge pull request #6593 from hayd/groupby_filter_filter FIX filter selects selected columns ``` Here's the error log from a `python setup.py install`. This has worked before, but I did have Canopy updates in the mean time, so I don't want to exclude that they messed up my Python setup. ``` bash running install running bdist_egg running egg_info writing requirements to pandas.egg-info/requires.txt writing pandas.egg-info/PKG-INFO writing top-level names to pandas.egg-info/top_level.txt writing dependency_links to pandas.egg-info/dependency_links.txt reading manifest file 'pandas.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no files found matching 'README.rst' no previously-included directories found matching 'doc/build' warning: no previously-included files matching '*.so' found anywhere in distribution warning: no previously-included files matching '*.pyd' found anywhere in distribution warning: no previously-included files matching '*.pyc' found anywhere in distribution warning: no previously-included files matching '.DS_Store' found anywhere in distribution writing manifest file 'pandas.egg-info/SOURCES.txt' installing library code to build/bdist.linux-x86_64/egg running install_lib running build_py copying pandas/version.py -> build/lib.linux-x86_64-2.7/pandas running build_ext skipping 'pandas/index.c' Cython extension (up-to-date) skipping 'pandas/algos.c' Cython extension (up-to-date) building 'pandas.algos' extension gcc -pthread -fno-strict-aliasing -DNDEBUG -g -O2 -fPIC -Ipandas/src/klib -Ipandas/src -I/u/paige/maye/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/include -I/u/paige/maye/Canopy/appdata/canopy-1.3.0.1715.rh5-x86_64/include/python2.7 -c pandas/algos.c -o build/temp.linux-x86_64-2.7/pandas/algos.o In file included from /u/paige/maye/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1760, from /u/paige/maye/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17, from /u/paige/maye/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4, from pandas/algos.c:314: /u/paige/maye/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" gcc -pthread -shared -Wl,-rpath,$ORIGIN/../lib -g -L/home/builder/jenkins/workspace/Python_Masters/ARCH/x86_64/label/Linux/core/lib build/temp.linux-x86_64-2.7/pandas/algos.o -L/home/builder/jenkins/workspace/Python_Masters/ARCH/x86_64/label/Linux/core/lib -lpython2.7 -o build/lib.linux-x86_64-2.7/pandas/algos.so /usr/bin/ld: cannot find -lpython2.7 collect2: ld returned 1 exit status error: command 'gcc' failed with exit status 1 ```
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Gbq unicode support
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2
2014-03-11T04:25:01Z
2014-06-14T18:12:35Z
2014-03-14T02:32:54Z
CONTRIBUTOR
null
Resolves Issue https://github.com/pydata/pandas/issues/5940 .
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6,597
FIX: Bug whereby array_equivalent was not correctly comparing Float64Ind...
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1
2014-03-11T12:26:58Z
2014-07-16T08:57:49Z
2014-03-11T12:56:40Z
CONTRIBUTOR
null
Currently, ``` >>> import pandas.core.common as com >>> com.array_equivalent(Float64Index([0, np.nan]), Float64Index([0, np.nan])) False ``` Although the current pandas code base does not use `array_equivalent` to compare Float64Indexes, leaving `array_equivalent` in its current state may be a bug waiting to happen. This PR attempts to fix the problem by using `pd.isnull` for all arrays of dtype `object`. In a previous PR I tried this and got terrible perf results. Since then I've discovered that my machine does not have enough memory to run the full perf test suit without page faults. If I rerun `test_perf.sh` for just a few Benchmarks, I can avoid the page faults and get consistent results. Running `/usr/bin/time -v ./test_perf.sh -b master -t fix-equivalent` yielded two tests with ratio > 1.1. ``` ------------------------------------------------------------------------------- reindex_fillna_pad | 0.5784 | 0.5034 | 1.1490 | packers_write_pack | 15.2360 | 7.1851 | 2.1205 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- ``` which I believe were due to page faults. When I reran perf on just these tests using `/usr/bin/time -v ./test_perf.sh -b master -t fix-equivalent -r "reindex_fillna_pad|packers_write_pack"` I got ``` ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- reindex_fillna_pad_float32 | 0.4633 | 0.4590 | 1.0093 | packers_write_pack | 7.9544 | 7.8390 | 1.0147 | reindex_fillna_pad | 0.7290 | 0.7180 | 1.0154 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- ```
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SQL: move df.to_sql to generic + update with new functionality
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2
2014-03-11T12:50:00Z
2014-07-02T11:06:35Z
2014-03-16T14:37:42Z
MEMBER
null
Initial commit for this. Questions: - To catch Panels (and raiseNotImplementedError), should I do this in sql.to_sql or in NDFrame.to_sql? - To support Series, I can just call `to_frame` on it. Is this OK? And should add tests for this.
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CLN/API refactor drop and filter and deprecate select
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57
2014-03-11T16:29:40Z
2015-01-19T22:25:36Z
2015-01-18T21:41:38Z
CONTRIBUTOR
null
closes #4818 - need to unify api for drop and filter - should they take args for regex (regex, flags, case) - should we keep select or filter? closes #6189 xref #8530 xref #8594
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https://api.github.com/repos/pandas-dev/pandas/issues/6600
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https://github.com/pandas-dev/pandas/issues/6600
29,197,567
MDU6SXNzdWUyOTE5NzU2Nw==
6,600
Series.pop() removes additional keys not specified
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{ "closed_at": "2014-05-30T12:17:57Z", "closed_issues": 755, "created_at": "2014-01-03T20:19:04Z", "creator": null, "description": "2014-02-3: previous version released", "due_on": "2014-05-31T07:00:00Z", "html_url": "https://github.com/pandas-dev/pandas/milestone/24", "id": 526062, "labels_url": "https://api.github.com/repos/pandas-dev/pandas/milestones/24/labels", "node_id": "MDk6TWlsZXN0b25lNTI2MDYy", "number": 24, "open_issues": 0, "state": "closed", "title": "0.14.0", "updated_at": "2016-12-29T13:57:13Z", "url": "https://api.github.com/repos/pandas-dev/pandas/milestones/24" }
1
2014-03-11T17:10:23Z
2014-03-11T19:00:54Z
2014-03-11T19:00:54Z
NONE
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``` python import pandas as pd df = pd.DataFrame({ 'A': 0, 'B': range(5), 'C': 0, }) k = df.iloc[4] k.pop('B') print k ``` ``` out: A 0 Name: 4, dtype: int64 ``` ``` expected: A 0 C 0 Name: 4, dtype: int64 ``` ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.4.final.0 python-bits: 64 OS: Linux OS-release: 3.8.0-33-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: de_DE.UTF-8 pandas: 0.13.1 Cython: 0.17.4 numpy: 1.7.1 scipy: 0.11.0 statsmodels: 0.5.0 IPython: 0.13.2 sphinx: 1.1.3 patsy: 0.2.1 scikits.timeseries: None dateutil: 1.5 pytz: 2012c bottleneck: None tables: 2.4.0 numexpr: 2.0.1 matplotlib: 1.2.1 openpyxl: 1.7.0 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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