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https://api.github.com/repos/pandas-dev/pandas/issues/1601
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5,531,549
MDU6SXNzdWU1NTMxNTQ5
1,601
Year partial string indexing doesn't work right with freq='M' PeriodIndex
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5,532,380
MDU6SXNzdWU1NTMyMzgw
1,602
Series repr incorrect if name==0
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"2012-07-10T19:58:53Z"
"2012-07-11T16:46:22Z"
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MEMBER
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``` In [13]: Series(range(5), name=0) Out[13]: 0 0 1 1 2 2 3 3 4 4 In [14]: Series(range(5), name=1) Out[14]: 0 0 1 1 2 2 3 3 4 4 Name: 1 ```
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MDU6SXNzdWU1NTMyNjc0
1,603
Panel .ix indexing bug
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0
"2012-07-10T20:12:13Z"
"2012-07-11T17:57:19Z"
"2012-07-11T17:57:19Z"
MEMBER
null
Major axis is DatetimeIndex ``` pdata.ix[:, -1, :] ```
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Plotting non-periodic timeseries not working
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4
"2012-07-11T11:59:31Z"
"2012-07-11T17:36:04Z"
"2012-07-11T17:36:04Z"
CONTRIBUTOR
null
import pandas as pd from dateutil.relativedelta import relativedelta stamps = [p for p in pd.DatetimeIndex(freq='60Min',start=pd.datetime(2012,6,12),periods=8)] series = pd.TimeSeries([1,2,3,4,5,6,7,9], index=stamps) series = series[:-1].append(pd.Series(series[-1],index=[series.index[-1]-relativedelta(seconds=1000)])) series.plot() The x axis becomes pretty bogus. Is this known, or am I perhaps doing something wrong?
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1,605
datetools.parser.parse
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2
"2012-07-11T13:30:22Z"
"2012-07-11T14:10:29Z"
"2012-07-11T14:10:29Z"
NONE
null
In [1]: import pandas In [2]: pandas.datetools.parser.parse('2011, 7, 11') The above code works in pandas 0.70. But it doesn't work in 0.80 and I got an error bellow: TypeError: unbound method parse() must be called with parser instance as first argument (got str instance instead)
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5,550,414
MDU6SXNzdWU1NTUwNDE0
1,606
Sparsify console output issue
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0
"2012-07-11T15:09:47Z"
"2012-07-11T20:40:25Z"
"2012-07-11T20:40:25Z"
MEMBER
null
Should not have that AAA hole ``` In [31]: result Out[31]: sp1 sp2 sp3 method replicate site year EDGE 1 AAA 2006 2 4 6 BBB 2006 2 4 6 2 AAA 2006 2 4 6 RIFFLE 1 2006 2 4 6 BBB 2006 2 4 6 2 AAA 2006 2 4 6 In [32]: result.index Out[32]: MultiIndex [('EDGE', 1, 'AAA', 2006) ('EDGE', 1, 'BBB', 2006) ('EDGE', 2, 'AAA', 2006) ('RIFFLE', 1, 'AAA', 2006) ('RIFFLE', 1, 'BBB', 2006) ('RIFFLE', 2, 'AAA', 2006)] ```
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5,553,897
MDU6SXNzdWU1NTUzODk3
1,607
Copy 0.7.0 what's new note re: integer indexes to gotchas page
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"2012-07-11T17:41:35Z"
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MEMBER
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5,555,007
MDExOlB1bGxSZXF1ZXN0MTc3MDcxMw==
1,608
BUG: unordered dup index does not yield scalar value
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0
"2012-07-11T18:27:19Z"
"2014-08-11T16:36:32Z"
"2012-07-11T20:59:51Z"
CONTRIBUTOR
null
#1586
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5,555,024
MDU6SXNzdWU1NTU1MDI0
1,609
Nonunique integer index bug
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2
"2012-07-11T18:28:03Z"
"2012-07-12T02:37:20Z"
"2012-07-12T02:37:20Z"
CONTRIBUTOR
null
related to #1586 In [8]: s = Series(range(5), index=[2, 0, 0, 1, 1]) In [9]: s Out[9]: 2 0 0 1 0 2 1 3 1 4 In [10]: s[2] ... KeyError: 0
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1,610
Console-width detection should be interactive sessions only
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8
"2012-07-12T05:52:51Z"
"2013-11-21T22:43:05Z"
"2012-11-14T18:15:08Z"
CONTRIBUTOR
null
I was surprised to see that my program's standard output changed when I unplugged my laptop from my external display this afternoon! I suggest that the terminal-width detection feature should only operate in an interactive python session. In non-interactive usage the stdout should depend only on the print options currently in effect. See PR #453
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Misleading __str__/__repr__ for DatatimeIndex objects with less than 3 items
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1
"2012-07-12T08:36:49Z"
"2013-04-22T14:38:01Z"
"2012-07-12T15:12:29Z"
NONE
null
Hi, this isn't such a big issue, but I guess it's worth reporting anyway. I'm using the development version '0.8.1.dev-0e5fc44'. DatetimeIndex `__str__` and `__repr__` methods always return a string that contains a list-like representation of the contents in this fashion: [first_item, ..., last_item]. That format is used even when the DatetimeIndex has 1 or 2 elements, which should be represented like [only_item] and [first_item, last_item] respectively. Example: ``` In [32]: to_datetime(["2012-01-01"]) Out[32]: <class 'pandas.tseries.index.DatetimeIndex'> [2012-01-01 00:00:00, ..., 2012-01-01 00:00:00] Length: 1, Freq: None, Timezone: None ``` Thanks and regards.
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additional docs for complex date parsing in read_*
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0
"2012-07-12T11:23:59Z"
"2012-07-12T15:43:28Z"
"2012-07-12T15:43:28Z"
CONTRIBUTOR
null
note order of application of parse_dates, index_col
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ENH: Conditionally import Rpy stuff.
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1
"2012-07-12T15:15:24Z"
"2012-07-12T16:29:08Z"
"2012-07-12T16:29:08Z"
CONTRIBUTOR
null
load_data. so helpful.
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recursive 301 redirect from sourceforge -> pydata
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The old docs on sourceforge.net still have much higher google juice than the new ones at pydata.org. E.g. I just googled "pandas rpy" and hit #1 is pandas.sourceforge.net/r_interface.html, hit #4 is pandas.pydata.org/pandas-docs/dev/r_interface.html. So I keep clicking the first link, and then realize that I'm looking at outdated docs, then hit back, etc., and it's gradually becoming rather annoying. The ideal fix would be a 301 redirect, which would pass all your google juice on to the new site. Not sure if sourceforge allows the relevant .htaccess rules: https://support.google.com/webmasters/bin/answer.py?hl=en&answer=93633 Or maybe via php: http://sourceforge.net/apps/trac/sourceforge/wiki/Project%20web#Re-directingtoHostedApps
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Trouble with factors when there are nans
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1
"2012-07-12T21:21:14Z"
"2012-08-10T16:11:23Z"
"2012-08-10T16:11:15Z"
CONTRIBUTOR
null
``` python from pandas.rpy.common import load_data prestige = load_data('Prestige', 'car') ```
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1,616
BUG: new converters for sub-second plotting
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2
"2012-07-13T01:28:53Z"
"2014-06-23T20:32:29Z"
"2012-07-13T03:10:56Z"
CONTRIBUTOR
null
#1599
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1,617
Document the behavior of idxmin when there are multiple rows containing the minimum value.
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1
"2012-07-13T13:43:08Z"
"2012-07-18T14:33:36Z"
"2012-07-18T14:33:24Z"
NONE
null
I've added an example to the docs which demonstrates the behavior of idxmin() for a column where multiple rows contain the minimum value (and also the column contains a NaN value). The current documentation is ambigous (to me) on this edge case. From http://pandas.pydata.org/, a pull request seemed like the most appropriate way to contribute to the docs. Please let me know if there is a better way or how I can improve the request. And thanks for a great tool!
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12
"2012-07-13T21:21:42Z"
"2016-05-17T23:06:24Z"
"2016-05-17T23:06:23Z"
CONTRIBUTOR
null
common functions and recipes that fit onto a single sheet
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Don't downsample mixed freq data on axes
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"2012-07-13T21:25:23Z"
"2012-07-19T19:33:29Z"
"2012-07-19T19:33:29Z"
CONTRIBUTOR
null
plot low freq data, then plot high freq data, the high freq data is currently being down sampled. Need to store data on the axes and then upsample existing data and re-plot.
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3
"2012-07-14T10:04:40Z"
"2016-10-12T23:03:05Z"
"2012-07-21T16:07:53Z"
NONE
null
Hi, it seems that when DataFrame, Series and maybe other objects contain non-ascii characters inside non-unicode strings the `__repr__` method is not able to give the correct column alignment to its values. However, we see that this issue does not affect unicode strings. I'm using pandas '0.8.1.dev-70c3deb' in a Linux box. Sample code: ``` python # -*- coding: utf-8 -*- from pandas import Series, DataFrame df1 = DataFrame([["aaaa", 1], ["bbbb", 2]]) df2 = DataFrame([["aaää", 1], ["bbbb", 2]]) df3 = DataFrame([[u"aaää", 1], ["bbbb", 2]]) # Comparison between "similar dataframes" print df1 print print df2 print print df3 print # Other cases: s1 = Series(["ä", "bbbb", "ßß"]) print print s1 ``` This results in: ``` 0 1 0 aaaa 1 1 bbbb 2 0 1 0 aaää 1 1 bbbb 2 0 1 0 aaää 1 1 bbbb 2 0 ä 1 bbbb 2 ßß ``` Thanks and regards.
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5,622,038
MDExOlB1bGxSZXF1ZXN0MTc5NzA0Mw==
1,621
Add vectorized string methods to Series
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6
"2012-07-14T18:18:17Z"
"2014-06-23T10:59:14Z"
"2012-07-15T22:48:59Z"
MEMBER
null
I'd appreciate any feedback on the API (@takluyver and others), but this makes a lot of sense to me and I'm already finding my fingers falling right onto it. I swear I'm going to start doing more pull requests
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5,623,573
MDU6SXNzdWU1NjIzNTcz
1,622
Bug resampling quarterly period data to annual
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0
"2012-07-14T22:57:43Z"
"2012-07-15T23:24:51Z"
"2012-07-15T23:24:51Z"
MEMBER
null
``` In [10]: rng = period_range('2000Q1', periods=10, freq='Q-DEC') In [11]: rng Out[11]: <class 'pandas.tseries.period.PeriodIndex'> freq: Q-DEC [2000Q1, ..., 2002Q2] length: 10 In [12]: ts = Series(np.arange(10), index=rng) In [13]: ts Out[13]: 2000Q1 0 2000Q2 1 2000Q3 2 2000Q4 3 2001Q1 4 2001Q2 5 2001Q3 6 2001Q4 7 2002Q1 8 2002Q2 9 Freq: Q-DEC In [14]: ts.resample('A') --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/wesm/Dropbox/talks/scipy2012/tutorial/<ipython-input-14-d20eb06885e9> in <module>() ----> 1 ts.resample('A') /home/wesm/code/pandas/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base) 187 fill_method=fill_method, convention=convention, 188 limit=limit, base=base) --> 189 return sampler.resample(self) 190 191 def first(self, offset): /home/wesm/code/pandas/pandas/tseries/resample.pyc in resample(self, obj) 72 73 if self.kind is None or self.kind == 'period': ---> 74 return self._resample_periods(obj) 75 else: 76 obj = obj.to_timestamp(how=self.convention) /home/wesm/code/pandas/pandas/tseries/resample.pyc in _resample_periods(self, obj) 233 else: 234 raise ValueError('Frequency %s cannot be resampled to %s' --> 235 % (axlabels.freq, self.freq)) 236 237 ValueError: Frequency Q-DEC cannot be resampled to <1 YearEnd: kwds={'month': 12}, month=12> ```
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1,623
Add aggregate method to DataFrame
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MEMBER
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a la groupby-aggregate
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DatetimeIndex unnecessarily copies int64 array
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"2012-07-15T20:58:38Z"
"2012-07-15T23:08:12Z"
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5,629,991
MDU6SXNzdWU1NjI5OTkx
1,625
scatter_matrix doesn't label the axes right on a DataFrame of time series
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"2012-07-15T22:04:46Z"
"2012-07-19T19:07:56Z"
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http://imgur.com/BGUgT
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MDU6SXNzdWU1NjMwMDQ1
1,626
What should frame.groupby([]) return?
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"2012-07-15T22:14:00Z"
"2012-07-18T23:16:43Z"
"2012-07-18T23:16:43Z"
MEMBER
null
as suggested on http://stackoverflow.com/questions/11492215/how-to-do-a-groupby-on-an-empty-set-of-columns-in-pandas, maybe should return just the DataFrame, or raise an exception (if it doesn't already)
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5,630,567
MDU6SXNzdWU1NjMwNTY3
1,627
Strange dates in DatetimeIndex after upgrade
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"2012-07-15T23:37:27Z"
"2012-07-16T02:05:12Z"
"2012-07-16T02:05:12Z"
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Dates started displaying incorrectly after I upgraded to pandas 0.8.0. For example, using the date_range example below from the documentation, 1/1/2011 shows up as 2051-05-17. My installed packages include numpy 1.7.0.dev-3cb783e-20120427, pytz 2012c, python-dateutil 2.1, DateUtils 0.5.2. Python 2.7.3 (default, Apr 19 2012, 00:55:09) [GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] on darwin Type "help", "copyright", "credits" or "license" for more information. `>>> import pandas` `>>> pandas.__version__` `'0.8.0'` `>>> from pandas import date_range` `>>> rng = date_range('1/1/2011', periods=72, freq='H')` `>>> rng[:5]` `<class 'pandas.tseries.index.DatetimeIndex'>` `[2051-05-17 05:40:40.331386880, ..., 2051-10-30 21:40:40.331386880]` `Length: 5, Freq: H, Timezone: None` `>>>`
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5,633,524
MDU6SXNzdWU1NjMzNTI0
1,628
merge with empty DataFrame IndexError
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2
"2012-07-16T07:12:22Z"
"2013-03-07T18:18:14Z"
"2012-07-21T14:01:59Z"
NONE
null
``` In [1]: from pandas import * In [2]: a = DataFrame(columns=['column1'], data=[1]) In [3]: b = DataFrame(columns=['column1']) In [4]: a.merge(b, on=['column1'], how='left') IndexError Traceback (most recent call last) IndexError: index out of range for array ```
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1,629
reading xslx returns incorrect data due to bug in openpyxl load_workbook being called with use_iterators=True
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8
"2012-07-16T13:19:03Z"
"2013-10-03T03:30:36Z"
"2013-09-05T06:55:20Z"
CONTRIBUTOR
null
The situation is described in https://bitbucket.org/ericgazoni/openpyxl/issue/124/rawcellis_date-returns-false-positive and results in datetime being returned instead of float causing incorrect data. Could this method argument be exposed to the ExcelFile **init** with a default of True? using False will work around the issue, presumably at the cost of performance. Iteration code would then be needed in pandas ExcelFile._parse_xlsx ... As this bug has existed for months in openpyxl without comment and code is still described as "very raw" in openpyxl https://bitbucket.org/ericgazoni/openpyxl/src/0082a961cf8b/openpyxl/reader/iter_worksheet.py#cl-27 Alternatively, if xlrd 0.8.0 is released with xlsx support, pandas could use that instead.
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5,639,012
MDU6SXNzdWU1NjM5MDEy
1,630
Panel slice assigning does not reindex correctly?
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2
"2012-07-16T13:31:42Z"
"2016-10-12T23:03:05Z"
"2012-09-20T03:00:44Z"
CONTRIBUTOR
null
Suppose I have a=Panel(randn(3,10,2)) b=Series(randn(10)) b.sort() now the index of b has a different order, yet same values as the major_index of a if I do a.ix[0,:,0]=b the slice of a gets assigned the values of b without reindexing first, but somehow the index is till kept track of: ``` b Out[220]: 4 -2.465417 8 -2.202345 3 -1.173703 5 -0.802436 7 -0.307515 0 -0.045943 1 0.155771 6 0.367755 9 0.458332 2 1.629641 a.ix[0,:,0]=b a.ix[0,:,0] Out[223]: 0 -2.465417 1 -2.202345 2 -1.173703 3 -0.802436 4 -0.307515 5 -0.045943 6 0.155771 7 0.367755 8 0.458332 9 1.629641 a.ix[0,:,0]==b Out[224]: 0 True 1 True 2 True 3 True 4 True 5 True 6 True 7 True 8 True 9 True ```
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1,631
Resample loses index name
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MDU6SXNzdWU1NjYzNDk1
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DateTimeIndex displayed incorrectly.
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2
"2012-07-17T13:20:06Z"
"2012-07-20T17:57:37Z"
"2012-07-20T17:57:37Z"
NONE
null
I am using the latest pandas from git and numpy 1.7.0-dev. See Out [27] below: ``` In [25]: df = pandas.DataFrame({'t': [datetime.datetime.now()], 'x': [0]}).set_index('t') In [26]: df Out[26]: x t 2012-07-17 09:18:21.993665 0 In [27]: df.index Out[27]: <class 'pandas.tseries.index.DatetimeIndex'> [1840-03-20 01:03:32.867732032] Length: 1, Freq: None, Timezone: None In [28]: df.index.asi8 Out[28]: array([1342516701993665000]) ``` Edit: It's worse with datetime64. ``` In [31]: df = pandas.DataFrame({'t': [np.datetime64(datetime.datetime.now())], 'x': [0]}).set_index('t') In [32]: df Out[32]: x t 1840-03-23 05:51:57.596732 0 In [33]: df.index Out[33]: <class 'pandas.tseries.index.DatetimeIndex'> [1964-11-16 11:52:40.252490752] Length: 1, Freq: None, Timezone: None In [34]: df.index.asi8 Out[34]: array([-4095338882403267968]) ```
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1,633
Retain index name on resample
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5,666,807
MDU6SXNzdWU1NjY2ODA3
1,634
Handle NAs after upsampling existing data on plotting
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1
"2012-07-17T15:34:33Z"
"2012-07-19T19:58:21Z"
"2012-07-19T19:58:21Z"
CONTRIBUTOR
null
dropping NA right now but need to be consistent with simple case (masking NAs to show gaps)
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5,674,169
MDU6SXNzdWU1Njc0MTY5
1,635
Resample with PeriodIndex should allow third option
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5
"2012-07-17T20:39:33Z"
"2014-07-13T09:29:02Z"
"2014-03-09T15:20:46Z"
CONTRIBUTOR
null
Suppose I have: prng = period_range('1/1/1999', freq='M', periods=3) s = Series(np.random.randn(len(prng)), prng) s.resample('D') should by default go from 1/1/1999 to 3/31/1999 as opposed to 1/31/1999 - 3/31/1999 ('e') or 1/1/1999-3/1/1999 ('s')
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5,684,452
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1,636
`kwargs` are ignored in `Series.plot`
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2
"2012-07-18T09:32:20Z"
"2012-07-19T19:38:58Z"
"2012-07-19T19:38:58Z"
CONTRIBUTOR
null
It seems that some `kwargs` (like `mew` and `ms`) given to the `Series.plot` method are being ignored. I'm running pandas.0.8.0 and matplotlib 1.1.0 on Archlinux, Python 2.7.3 with ipython 0.12.1, and starting ipython via `ipython2 --pylab=qt`. The following code produces the erroneous behaviour. The mpl plotting works just fine, whereas the pandas plotting ignores `ms` and `mew` arguments. ``` import matplotlib as mpl from numpy import random import pandas print pandas.__version__ test = pandas.Series(random.randn(120), index=pandas.date_range(start="2000-01-01", end="2009-12-31", freq=pandas.datetools.MonthBegin())) mpl.pyplot.figure() # this produces the correct markers mpl.pyplot.plot(test, '.', ms=12.) mpl.pyplot.figure() # this ignores the ms kwarg test.plot(style='.', ms=12.) ``` It would be okay to use matplotlib's plotting function, but I'm dealing with time series, and I don't think matplotlib's support for dates is too convenient. Is this a bug, or am I just doing it wrong?
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1,637
Support dateutil 2.1
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"2012-07-19T19:07:43Z"
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5,698,114
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MS and other frequencies in to_timestamp
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1
"2012-07-18T19:55:01Z"
"2014-05-28T23:24:29Z"
"2014-05-28T23:24:29Z"
MEMBER
null
e.g. ``` i = period_range('1/1/2000', periods = 4, freq = 'M') ts = Series(range(4), index = i) ts.to_timestamp('MS') ```
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Dataframe.ix behavior inconsistent (depends on index type)
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"2012-07-18T20:48:48Z"
"2012-07-19T19:33:23Z"
"2012-07-19T18:00:24Z"
NONE
null
I would expect that ix[n] would return the n-th row in a data frame (doc is missing so I'm not sure). This seems to work as such with an index that is not integer based (say dates). But it fails with an int-based index that is not a continuous range starting at 0 (and there is no reason it should). Example with date index: dates = np.asarray(date_range('1/1/2000', periods=8)) d = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D']) d.ix[1] returns the second row correctly. Example with int index: numbers = range(4,12) n = DataFrame(randn(8, 4), index=numbers, columns=['A', 'B', 'C', 'D']) n.ix[1] Returns a key error. A bug, or so it seems.
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5,700,614
MDExOlB1bGxSZXF1ZXN0MTgzMjE4NA==
1,640
Plotting bug fixes and new features
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0
"2012-07-18T21:51:31Z"
"2014-06-12T17:50:46Z"
"2012-07-19T19:24:22Z"
CONTRIBUTOR
null
also a start on the release notes
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5,703,535
MDU6SXNzdWU1NzAzNTM1
1,641
ExcelWriter not working
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"2012-07-19T01:08:57Z"
"2012-07-19T01:47:42Z"
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I get the below exception. writer = pandas.ExcelWriter('output.xlsx') Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2538, in run_code exec code_obj in self.user_global_ns, self.user_ns File "<ipython-input-1-2e9db80bb8db>", line 1, in <module> writer = pandas.ExcelWriter('output.xlsx') File "/usr/lib/pymodules/python2.7/pandas/io/parsers.py", line 709, in **init** from openpyxl import Workbook ImportError: cannot import name Workbook Perhaps 'from openpyxl.workbook import Workbook' will help? This was suggested here: http://stackoverflow.com/questions/10439539/cannot-import-openpyxl-to-python-on-osx
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Cannot assign to df.icol(n), df.irow(n)
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"2012-07-19T09:20:16Z"
"2016-10-12T23:03:05Z"
"2013-09-20T16:52:31Z"
NONE
null
I ran into a situation where I want to transform a column/row based on its position and I noticed that I can't do this without using df[df.columns[n]] instead of df.icol(n) Would this be an easy fix? Assigning results in ``` SyntaxError: can't assign to function call ```
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Hierarchical Series to data frame
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"2012-07-19T15:10:35Z"
"2012-07-19T17:38:26Z"
"2012-07-19T17:38:26Z"
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I'm following Wes McKinney's pandas tutorial. At one point he runs the code result=boys.groupby('name').prop.describe() result and gets a table. When I run the same code I get a series with hierarchical index. I suspect the problem comes from using a more recent version of pandas or scipy. Is there a way to convert this into a table (making the second index into the column names?) Or is there some argument I can pass to describe() to make a table?
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Unconverted 'name' of Series object from DataFrame of time series row
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"2012-07-19T20:09:52Z"
"2013-12-04T00:57:58Z"
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MEMBER
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``` In [6]: import pandas.util.testing as tm In [7]: df = tm.makeTimeDataFrame() In [8]: df.ix['2000-02-02'].name Out[8]: '2000-02-02' ```
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Business month start (BMS) bug
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``` In [10]: date_range('1/1/2000', periods=10, freq='BMS') Out[10]: <class 'pandas.tseries.index.DatetimeIndex'> [2000-02-01 00:00:00, ..., 2000-11-01 00:00:00] Length: 10, Freq: BMS, Timezone: None ```
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Interpolate with method='values' and DatetimeIndex fails
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String timestamp aliases don't work with tz-aware data
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Resample with how='median' broken in some cases
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A bug in pandas.concat when the given dict contains None
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"2012-07-19T20:59:23Z"
"2012-07-20T15:38:11Z"
"2012-07-20T15:38:11Z"
CONTRIBUTOR
null
``` python In [2]: import pandas In [3]: df0 = pandas.DataFrame([[10, 20, 30], [10, 20, 30], [10, 20, 30]]) df0 Out [3]: 0 1 2 0 10 20 30 1 10 20 30 2 10 20 30 In [4]: pandas.concat(dict(a=None, b=df0, c=df0[:2], d=df0[:1], e=df0)) Out [4]: 0 1 2 a 0 10 20 30 1 10 20 30 2 10 20 30 b 0 10 20 30 1 10 20 30 c 0 10 20 30 d 0 10 20 30 1 10 20 30 2 10 20 30 ``` Checked with the current master e1129b11b1fe0016748d3d279000a2a71db8dee5 and 0.7.3
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5,724,812
MDExOlB1bGxSZXF1ZXN0MTg0MzExMA==
1,650
Series.describe() fails for empty and None series.
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4
"2012-07-19T21:10:41Z"
"2014-06-17T15:25:59Z"
"2012-07-20T00:29:04Z"
NONE
null
Running `describe()` raises and exception for `Series([])` and `Series([None])`. See the following examples: ``` In [416]: Series([]).describe() --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-416-162185f07510> in <module>() ----> 1 Series([]).describe() /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in describe(self, percentile_width) 1363 'max'] 1364 -> 1365 data = [self.count(), self.mean(), self.std(), self.min(), 1366 self.quantile(lb), self.median(), self.quantile(ub), 1367 self.max()] /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in min(self, axis, out, skipna, level) 1067 if level is not None: 1068 return self._agg_by_level('min', level=level, skipna=skipna) -> 1069 return nanops.nanmin(self.values, skipna=skipna) 1070 1071 @Substitution(name='maximum', shortname='max', /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/nanops.pyc in f(values, axis, skipna, **kwds) 41 result = alt(values, axis=axis, skipna=skipna, **kwds) 42 except Exception: ---> 43 result = alt(values, axis=axis, skipna=skipna, **kwds) 44 45 return result /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/nanops.pyc in _nanmin(values, axis, skipna) 152 result = __builtin__.min(values) 153 else: --> 154 result = values.min(axis) 155 156 return _maybe_null_out(result, axis, mask) ValueError: zero-size array to minimum.reduce without identity In [388]: s = pandas.Series([None]) In [389]: s.describe() --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-389-97bd840d09f5> in <module>() ----> 1 s.describe() /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in describe(self, percentile_width) 1334 1335 objcounts = Counter(self.dropna().values) -> 1336 top, freq = objcounts.most_common(1)[0] 1337 data = [self.count(), len(objcounts), top, freq] 1338 IndexError: list index out of range In [395]: s = pandas.Series([np.nan, np.nan]) In [396]: s.describe() Out[396]: count 0 mean NaN std NaN min NaN 25% NaN 50% NaN 75% NaN max NaN ``` I have included `Series([np.nan])` because its output is affected by the pull request. After the pull request the output is as follows: ``` In [3]: Series([]).describe() Out[3]: count 0 In [4]: Series([None]).describe() Out[4]: count 0 unique 0 In [5]: Series([np.nan]).describe() Out[5]: count 0 ``` The pull request fixes the `ValueError` from the call to `values.min(axis)` and the `IndexError` from accessing `most_common(1)[0]` by only returning a small amount of count data when the Series object is empty or contains all NaN or None values. Is this the right approach? It changes the output for Series objects with all NaN values. Test coverage was written but I couldn't figure out how to run the tests. Any hints? Regards, Todd
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5,725,557
MDU6SXNzdWU1NzI1NTU3
1,651
Request: Multi-line header for to_csv when column is MultiIndex
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3
"2012-07-19T21:44:43Z"
"2013-05-19T14:42:09Z"
"2013-05-19T14:42:09Z"
CONTRIBUTOR
null
Having tuple as string in header line is not what I expected. Any particular reason for this behavior?: ``` python In [1]: import pandas import numpy from itertools import product df = pandas.DataFrame(numpy.random.randint(low=10, high=100, size=(2, 4))) df.columns = pandas.MultiIndex.from_tuples(list(product('ab', 'xy'))) print df a b x y x y 0 86 55 85 20 1 89 32 71 18 In [2]: import sys df.to_csv(sys.stdout, sep='\t') ('a', 'x') ('a', 'y') ('b', 'x') ('b', 'y') 0 86 55 85 20 1 89 32 71 18 ``` It would be nice if there is an option to output multi-line header with `df.to_csv`, like what `print df` or `df.to_html` does. Or even make it as default. Also, `header_format` option to format would be nice, e.g.: ``` python df.to_csv(sys.stdout, sep='\t', header_format='{0}.{1}') a.x a.y b.x b.y 0 86 55 85 20 1 89 32 71 18 ```
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1,652
Request: allow slice object in DataFrame.reindex
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4
"2012-07-20T17:37:52Z"
"2014-05-28T23:22:17Z"
"2014-05-28T23:22:17Z"
CONTRIBUTOR
null
`irow` and `icol` allow slice. Why not `reindex` too? Note that not allowing slice object in `DataFrame.reindex` makes the following phrase in the document incorrect: > Some might conclude that ix and reindex are 100% equivalent based on this. This is indeed true except in the case of integer indexing. -- http://pandas.pydata.org/pandas-docs/dev/gotchas.html#reindex-versus-ix-gotchas
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doc: sphinx objects needed
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1
"2012-07-20T20:07:40Z"
"2013-09-07T17:58:05Z"
"2013-09-07T17:58:05Z"
CONTRIBUTOR
null
Add a Sphinx object for DataFrame: http://permalink.gmane.org/gmane.comp.python.pystatsmodels/8905 :class:`pandas.DataFrame`
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6
"2012-07-20T22:20:17Z"
"2014-06-18T10:10:13Z"
"2012-10-11T23:27:38Z"
CONTRIBUTOR
null
#1620 checks whether there are non-ascii characters and tries to convert them if possible allows encoding to be set in set_printoptions (default UTF8)
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5,747,851
MDU6SXNzdWU1NzQ3ODUx
1,655
Request for "EasyFix" tag
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2
"2012-07-20T22:34:34Z"
"2012-07-20T22:42:54Z"
"2012-07-20T22:42:54Z"
NONE
null
Any low-hanging issues laying around? Things that are relatively straightforward but kind of time-consuming?
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5,754,832
MDU6SXNzdWU1NzU0ODMy
1,656
Better vectorized string slicing / get notation
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0
"2012-07-21T17:19:33Z"
"2012-07-22T19:13:24Z"
"2012-07-22T19:13:24Z"
MEMBER
null
like: `df.col.str[:10]` versus current `df.col.str.slice(slice(0, 10))`
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MDU6SXNzdWU1NzU2Mjc3
1,657
More consistent na_values handling in read_csv
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13
"2012-07-21T21:04:46Z"
"2019-03-05T20:15:44Z"
"2012-08-20T06:25:19Z"
NONE
null
The current handling of the `na_values` argument to `read_csv` is strangely different depending on what kind of value you pass to `na_values`. If you pass None, the default NA values are used. If you pass a dict mapping column names to values, then those values will be used for those columns, totally overriding the default NA values, while for columns not in the dict, the default values will be used. If you pass some other kind of iterable, it uses the _union_ of the passed values _and_ the default values as the NA values. This behavior is confusing because sometimes the passed values override the defaults, but other times they just add to the defaults. It's also contrary to the documentation at http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files, which says: "If you pass an empty list or an empty list for a particular column, no values (including empty strings) will be considered NA." But passing an empty list doesn't result in no values being considered NA. In fact, passing an empty list does nothing, since the empty list is unioned with the default NA values, so the default NA values are just used anyway. Currently there is no easy way to pass a list of NA values which overrides the default for all columns. You can pass a dict, but then you have to specify the defaults per column. If you pass a list, you're not overriding the defaults, you're adding to them. This makes for confusing behavior when reading CSV files with string data in which strings like "na" and "nan" are valid data and should be read as their literal string values. There should be a way to pass an all-column set of NA values that overrides the defaults. One possibility would be to have two arguments, something like `all_na_values` and `more_na_values`, to specify overriding and additional values, respectively. Another possibility would be to expose the default (currently the module-level `_NA_VALUES` in parsers.py), and allow users to add to it it they want to add more NA values (e.g., `read_csv(na_values=set(['newNA']) | pandas.default_nas)`.
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MDU6SXNzdWU1Nzc2NTkz
1,658
Bar Plot does not allow to change linewidth
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6
"2012-07-23T11:16:43Z"
"2019-06-23T23:03:17Z"
"2012-07-25T05:25:27Z"
CONTRIBUTOR
null
linewidth for barplots (ie the edge lines of the bars) appears to be fixed at 1. Would it be possible to allow it to be set freely?
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MDU6SXNzdWU1Nzg2NjE1
1,659
Add flags option to all string methods accepting regex string
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0
"2012-07-23T19:18:32Z"
"2012-07-23T19:32:42Z"
"2012-07-23T19:32:42Z"
MEMBER
null
Methods like findall/match need to be able to take `re.IGNORECASE`
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"Memoize" computations with hash table in pandas.core.strings
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0
"2012-07-23T19:36:57Z"
"2019-12-18T18:41:16Z"
null
MEMBER
null
Avoid memory bloat by keeping a table of object ids observed. Will require some Cython code
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MDU6SXNzdWU1Nzg3MTU3
1,661
Add option to Series.map for NA handling
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SQL connection improvements
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13
"2012-07-23T20:36:17Z"
"2013-07-08T20:22:54Z"
"2013-07-08T20:22:54Z"
CONTRIBUTOR
null
From pydata mailing list: Been enjoying pandas, and using it some with SQL. I've posted a gist at git://gist.github.com/3164289.git with code that sniffs DataFrame types, creates a table if needed, and writes the dataframe to Sqlite, MySQL, Oracle, or PostgreSQL. It is lightly tested -- used on a couple of ad hoc jobs so far -- and does not save or restore indexes, but hope you'll find it useful.
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Styling in DataFrame.to_excel
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14
"2012-07-23T23:23:29Z"
"2017-04-20T01:02:13Z"
"2017-04-20T01:02:13Z"
MEMBER
null
from mailing list ``` Hello everyone, I wonder if it's possible to apply style to the outputed excel file or should I have to code a similar function with my style ? Maybe by overloadind ? ```
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MDU6SXNzdWU1NzkzNTE5
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Cut/qcut with labels=False should probably return Categorical
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1
"2012-07-24T01:35:24Z"
"2013-09-21T12:53:40Z"
"2013-09-21T12:53:40Z"
MEMBER
null
Otherwise reindexing may be necessary in some cases
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5,796,905
MDU6SXNzdWU1Nzk2OTA1
1,665
rot parameter ignored in line plot
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0
"2012-07-24T07:48:55Z"
"2016-10-12T23:03:05Z"
"2012-07-25T05:25:10Z"
CONTRIBUTOR
null
a=DataFrame(randn(10,2),index=range(10,100,9),columns=('a','b')) a.plot(rot=30) Out[67]: <matplotlib.axes.AxesSubplot at 0x11e4eaa90> the resulting chart's xaxis ticklabels remain unrotated. (pandas 0.8.1)
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MDU6SXNzdWU1Nzk5MTcx
1,666
Xaxis autoformat issue - self._use_dynamic_x should be self._use_dynamic_x()
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0
"2012-07-24T10:11:23Z"
"2016-10-12T23:03:06Z"
"2012-07-25T05:24:58Z"
NONE
null
The following ``` from pandas import * import datetime DataFrame([1,1], index = [ datetime.datetime(2012,1,1), datetime.datetime(2012,9,1) ] ).plot() ``` gives a plot where the xaxis is not readable. I think the reason is that in `tools\plotting.py` the `condition` (which decides to autofmt) is false. ``` condition = (not self._use_dynamic_x and df.index.is_all_dates and not self.subplots or (self.subplots and self.sharex)) ``` Should the first line not be `self._use_dynamic_x()` instead? [See http://stackoverflow.com/questions/11619144/pandas-xaxis-auto-format-issue for discussions]
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5,802,116
MDU6SXNzdWU1ODAyMTE2
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Non-Disjoint GroupBy
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3
"2012-07-24T13:19:41Z"
"2018-12-18T16:05:44Z"
"2018-12-18T16:05:36Z"
NONE
null
I think I once read something similar here or on the mailing list but I did not find it with these keywords. Let's say I want to have aggregates over a groups that fulfill any of a given set of conditions (e.g., a stock whose volatility is higher than x or the return lower than y and I am interested in how many stocks I hold with vol > x and how many with return< y ). so my df is something like stock vol return #of stocks A 40 -10 10 B 20 10 1000 etc. and I want the result condition # of stocks vol > x n return < y m where we have of course that m + n may be greater than df[#of stocks].sum(). Now, my stocks are grouped into different portfolios so I may have a portfolio column and this functionality should allow to combine this kind of group by with a "normal " group by. So portfolio condition # of stocks P vol > x m P return < y n Q vol >x o Q return < y p Is this something that has been thought about?
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1,668
BUG: plotting daily data with two-day gaps
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2
"2012-07-24T14:19:49Z"
"2012-07-24T22:13:18Z"
"2012-07-24T22:13:18Z"
CONTRIBUTOR
null
Hi, you should be able to copy and paste this in a ipython --pylab session: ``` python import pandas index = pandas.date_range(start='2001-1-1', end='2001-1-10', freq='D') data = index.day ts = pandas.TimeSeries(index=index, data=data) tsWithGaps = ts[:3].append(ts[5:]) figure() tsWithGaps.plot(style='o') figure() tsWithGaps.asfreq('D').plot(style='o') ``` I tried this snippet with 0.8.2.dev-7c54141 (i.e. latest git version as I write). The two plots below come from an improved version of the snippet which prints the timeseries on the plot. The first plot I get looks weird (wrong label for January 5th + double data point on the 8th) http://imgur.com/LFJup,aYLYJ#0 The timeseries is printed on the plot and looks perfectly fine. The second plot with the explicitly resampled ts is fine: http://imgur.com/LFJup,aYLYJ#1 Trying to tweak the parameters a 3-day gap looks fine and you get x axis labelling like plt.plot_date. She 2-day gaps seems always broken, although in a more subtle way than the plot above (generally the label before the gap is wrong but I don't get double data points). It looks like a timeseries with 2-day gaps is close enough to a regular index timeseries (maybe because of week-ends) and the index labelling goes wrong somewhere.
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5,804,349
MDU6SXNzdWU1ODA0MzQ5
1,669
Inferred freq bug
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4
"2012-07-24T14:44:02Z"
"2012-07-24T21:58:54Z"
"2012-07-24T21:58:54Z"
CONTRIBUTOR
null
In [13]: tsWithGaps.index[3] Out[13]: <Timestamp: 2001-01-06 00:00:00> In [14]: tsWithGaps.index[3].dayofweek Out[14]: 5 In [15]: tsWithGaps.index.inferred_freq Out[15]: 'B'
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5,804,957
MDU6SXNzdWU1ODA0OTU3
1,670
Cursor x axis value is not shown when using regular timeseries
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3
"2012-07-24T15:06:08Z"
"2013-09-21T13:42:10Z"
"2013-09-21T13:42:10Z"
CONTRIBUTOR
null
Hi, you should be able to paste the following snippet in a ipython --pylab session ``` python import pandas index = pandas.date_range(start='2001-1-1', end='2001-1-10', freq='D') ts = pandas.TimeSeries(index=index, data=index.day) figure() ts.plot() figure() ts[:3].append(ts[6:]).plot() ``` Although the labelling is definitely nicer in the regular timeseries case, the cursor x axis value is not shown (bottom right corner with the tk pylab backend) http://imgur.com/WCe6P,o8jge#0: The irregular timeseries with the standard matlplotlib plot_date labelling does show a value for the cursor x axis value: http://imgur.com/WCe6P,o8jge#1 I tried with the qt backend and I get the same results so it appears to be backend independent. Not a huge deal but I find the cursor x axis value quite useful from time to time when interactively inspecting a plot (zooming, panning, etc...).
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MDU6SXNzdWU1ODA1NzIz
1,671
Improper handling of Series in Series constructor
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2
"2012-07-24T15:36:53Z"
"2012-08-13T17:31:21Z"
"2012-08-11T21:47:43Z"
MEMBER
null
This looks like a regression that did not have a unit test (pretty sure this used to do the right thing) ``` index1 = ['d', 'b', 'a', 'c'] index2 = sorted(index1) s1 = Series([4, 7, -5, 3], index=index1) s2 = Series(s1, index=index2) ``` cc @hughdbrown
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5,818,693
MDU6SXNzdWU1ODE4Njkz
1,672
Strings like 5-2001 broken in parser
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"2012-07-24T22:24:09Z"
"2012-07-24T22:31:02Z"
"2012-07-24T22:31:02Z"
MEMBER
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It's my fault
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5,822,131
MDU6SXNzdWU1ODIyMTMx
1,673
bug with time fields when using timezones
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10
"2012-07-25T02:54:17Z"
"2012-09-26T21:27:29Z"
"2012-09-26T21:13:17Z"
NONE
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``` # the following code fails dr = date_range('2012-01-01','2012-01-10',freq = 'D', tz = 'Hongkong') dr.hour # whereas this doesn't dr = date_range('2012-01-01','2012-01-10',freq = 'D') dr.hour ``` The reason that this is an important use case is that often you have data stored in UTC but want to sample at a particular time of day in another region. For some dataframe y whose index is timezone aware, I would like to be able to say x = y[y.index.hour == 1] Instead I need to write: ``` hours = [dt.hour for dt in y.index] x = y[hours == 1] ``` this is ok but the list comprehension step takes ages because it is created a new timestamp for each underlying numpy datetime...
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1,674
issue with changing frequencies when timezones are involved
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1
"2012-07-25T06:20:39Z"
"2012-08-12T03:24:09Z"
"2012-08-12T03:24:09Z"
NONE
null
the following code fails: ``` dr = date_range('2011-12-01','2012-07-20',freq = 'D', tz = 'US/Eastern') dr.asfreq('T') ``` it appears that the asfreq code checks whether the timezone of the start and end date are the same. In this particular example, the daylight savings offsets are different at the beginning and end so the test for equality fails. Is this intended behaviour or a bug?
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5,826,307
MDU6SXNzdWU1ODI2MzA3
1,675
concatenated DataFrames with duplicated columns cannot be printed
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1
"2012-07-25T09:30:27Z"
"2012-09-18T21:10:47Z"
"2012-09-18T21:10:47Z"
CONTRIBUTOR
null
In the following, the concatenation happens, columns c and d are duplicated, but printing the dataframe raises error: ``` import pandas df1 = pandas.DataFrame([[1,2], [3,4]], columns=['a','b'], index=[1,2]) df2 = pandas.DataFrame([[1,2], [3,4]], columns=['c','d'], index=[1,2]) df_all = pandas.concat([df1, df2], axis=1) print(df_all) df4 = pandas.DataFrame([[1,2], [3,4]], columns=['a','b'], index=[3,4]) df_all = pandas.concat([df_all, df4], axis=0) print(df_all) df5 = pandas.DataFrame([[1,2], [3,4]], columns=['c','d'], index=[3,4]) df_all = pandas.concat([df_all, df5], axis=1) print(df_all) ```
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1,676
timezone aware DatetimeIndex from datetime.datetime (regression)
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"2012-09-18T16:38:42Z"
"2012-09-18T16:38:42Z"
CONTRIBUTOR
null
The following code worked in pandas 0.8.0b1, but not longer in 0.8.1: ``` import pandas as pnd from datetime import datetime from dateutil.tz import tzoffset values = [188.5, 328.25] index = [datetime(2012, 5, 11, 11, tzinfo=tzoffset(None, 7200)), datetime(2012, 5, 11, 12, tzinfo=tzoffset(None, 7200))] series = pnd.Series(data = values, index = index) ``` What would you propose as a workaround? Python 2.7.2, Numpy 1.6.1
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5,838,742
MDU6SXNzdWU1ODM4NzQy
1,677
GroupBy bug with time series
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3
"2012-07-25T19:51:50Z"
"2012-08-10T15:58:20Z"
"2012-08-10T15:58:00Z"
MEMBER
null
from mailing list `````` a = pd.DataFrame({'dt':pd.datetime(2012,4,25,9,30,0,393000),'size':50,'price':87.67},index=[pd.datetime(2012,4,25,9,30,0,393000)]) dt = a.index[0] minutely = pd.date_range(dt,dt,freq='5T') grouped = a.groupby(minutely.asof) grouped['size'].last() # error: len() of unsized object``` ``````
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.ix with MultiIndex indexing buglet
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0
"2012-07-25T22:03:04Z"
"2012-08-13T01:54:03Z"
"2012-08-13T01:54:02Z"
MEMBER
null
from mailing list: ``` In [42]: frame Out[42]: Ohio Colorado Green Red Green a 1 0 1 2 2 3 4 5 b 1 6 7 8 2 9 10 11 In [43]: frame.ix[:,0] Out[43]: a 1 0 2 3 b 1 6 2 9 Name: ('Ohio', 'Green') In [44]: frame.icol(0) Out[44]: a 1 0 2 3 b 1 6 2 9 Name: ('Ohio', 'Green') In [45]: frame.ix[:,1] Out[45]: Ohio Colorado Green Red Green a 0 1 2 b 6 7 8 In [46]: frame.icol(1) Out[46]: a 1 1 2 4 b 1 7 2 10 Name: ('Ohio', 'Red') ```
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5,845,584
MDU6SXNzdWU1ODQ1NTg0
1,679
What should Series do when passed generator?
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"2012-08-12T03:39:46Z"
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5,855,974
MDU6SXNzdWU1ODU1OTc0
1,680
Series with datetime64 dtype causes failure in dict passed to DataFrame
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"2012-08-12T03:57:56Z"
"2012-08-12T03:57:56Z"
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works: ``` In [9]: d = pandas.DataFrame({'A': 'foo', 'B': ts.values}, index=dr) In [10]: d.head() Out[10]: A B 2011-01-07 foo 2011-01-07 00:00:00 2011-01-14 foo 2011-01-14 00:00:00 2011-01-21 foo 2011-01-21 00:00:00 2011-01-28 foo 2011-01-28 00:00:00 2011-02-04 foo 2011-02-04 00:00:00 ``` does not work: ``` d = pandas.DataFrame({'A': 'foo', 'B': ts.values}, index=dr) ```
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5,856,085
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Concat datetime64 bug
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"2012-08-12T17:50:07Z"
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MEMBER
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``` dr = pandas.date_range('2011/1/1', '2012/1/1', freq='W-FRI') a = pandas.DataFrame() c = pandas.DataFrame({'A': 'foo', 'B': dr}, index=dr) a.append(c) ```
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5,856,129
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1,682
DatetimeIndex with time zone loses tzinfo in DataFrame constructor
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"2012-08-10T19:14:17Z"
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MEMBER
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``` dr = pandas.date_range('2011/1/1', '2012/1/1', freq='W-FRI') dr_tz = dr.tz_localize('US/Eastern') e = pandas.DataFrame({'A': 'foo', 'B': dr_tz}, index=dr) ```
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5,856,174
MDU6SXNzdWU1ODU2MTc0
1,683
Tz-aware DatetimeIndex shift issue
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MEMBER
null
``` dr = pandas.date_range('2011/1/1', '2012/1/1', freq='W-FRI') dr_tz = dr.tz_localize('US/Eastern') dr_tz.shift(1, '10T') ```
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DOC: update DataFrame.xs docstring and add examples
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"2012-07-26T19:16:10Z"
"2014-07-25T01:41:15Z"
"2012-09-08T21:30:02Z"
CONTRIBUTOR
null
Update of DataFrame.xs docstring. Added some examples also, maybe too verbose?
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1,685
plotting._stringify not unicode safe
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/Users/germuska/.virtualenvs/data/lib/python2.6/site-packages/pandas/tools/plotting.pyc in _stringify(x) 1073 return '|'.join(str(y) for y in x) 1074 else: -> 1075 return str(x) 1076 1077 UnicodeEncodeError: 'ascii' codec can't encode character u'\u2019' in position 1: ordinal not in range(128) If labels can't be unicode, perhaps can a clearer error be thrown?
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Performance of DataFrame.resample with a lot of non-unique datetime index values
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"2012-07-26T22:38:57Z"
"2012-08-12T19:55:41Z"
"2012-08-12T19:55:27Z"
CONTRIBUTOR
null
I've noticed that the first time I resample a DataFrame with a datetime index, it's REALLY slow compared to doing exactly the same thing in pure Python. My use case is analyzing logs from a web application server. The index datetimes indicate when an event happened, and there are many per second (so non-unique), and millions of rows per day (I'm totally fine with having separate days in separate dataframes). Once I've done a resample to a particular frequency (`.resample("T")`), I can do another resample with a different `how=` and it's nice and snappy, so I suspect there's an inefficiency somewhere in the code that figures out the resampled row groupings, and that those groupings are cached. From iPython (for %time): ``` python from pandas import DataFrame from datetime import datetime from itertools import groupby datelist = sum([[datetime(2012,7,26,0,x)]*1000 for x in range(10)], []) a = DataFrame([1]*len(datelist), index=datelist) # This is slower than it should be %time a.resample("T", how=len) # CPU times: user 1.49 s, sys: 0.01 s, total: 1.50 s # Wall time: 1.50 s # Out[6]: # 0 #2012-07-26 00:00:00 1000 #2012-07-26 00:01:00 1000 #2012-07-26 00:02:00 1000 #2012-07-26 00:03:00 1000 #2012-07-26 00:04:00 1000 #2012-07-26 00:05:00 1000 #2012-07-26 00:06:00 1000 #2012-07-26 00:07:00 1000 #2012-07-26 00:08:00 1000 #2012-07-26 00:09:00 1000 # This is unexpectedly fast (guess: caching) %time a.resample("T", how=len) # CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s # Wall time: 0.00 s # Out[7]: # 0 #2012-07-26 00:00:00 1000 #2012-07-26 00:01:00 1000 #2012-07-26 00:02:00 1000 #2012-07-26 00:03:00 1000 #2012-07-26 00:04:00 1000 #2012-07-26 00:05:00 1000 #2012-07-26 00:06:00 1000 #2012-07-26 00:07:00 1000 #2012-07-26 00:08:00 1000 #2012-07-26 00:09:00 1000 def by_minute(dt): return (dt.year, dt.month, dt.day, dt.hour, dt.minute) def how((key, values)): return datetime(*key), len(list(values)) # This is how fast I think .resample() SHOULD be. :) %time map(how, groupby(datelist, key=by_minute)) # CPU times: user 0.01 s, sys: 0.00 s, total: 0.01 s # Wall time: 0.01 s # Out[9]: # [(datetime.datetime(2012, 7, 26, 0, 0), 1000), # (datetime.datetime(2012, 7, 26, 0, 1), 1000), # (datetime.datetime(2012, 7, 26, 0, 2), 1000), # (datetime.datetime(2012, 7, 26, 0, 3), 1000), # (datetime.datetime(2012, 7, 26, 0, 4), 1000), # (datetime.datetime(2012, 7, 26, 0, 5), 1000), # (datetime.datetime(2012, 7, 26, 0, 6), 1000), # (datetime.datetime(2012, 7, 26, 0, 7), 1000), # (datetime.datetime(2012, 7, 26, 0, 8), 1000), # (datetime.datetime(2012, 7, 26, 0, 9), 1000)] ``` Thanks!
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scatter_matrix mislabels axes with DataFrame index
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python 2.7.3, pandas 0.8.0, matplotlib 1.1.0: The example code at http://pandas.pydata.org/pandas-docs/stable/visualization.html#scatter-plot-matrix produces axis labels which are integers 0-9 instead of -4 to 4 or so (as determined by the data extent). These appear to be taken from the DataFrame index, as can be seen by setting the index explicitly: ``` python df = DataFrame(np.random.randn(6, 4), index=['x','y','z','p','d','q'], columns=['a', 'b', 'c', 'd']) scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='kde') ``` produces http://imgur.com/oGFdM
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AssertionError with df.resample(how="median")
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3
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"2013-10-04T18:30:44Z"
"2012-08-11T22:48:10Z"
CONTRIBUTOR
null
I've reproduced _something_ using `how="median"`, perhaps #1648. It seems to hit when there are discontinuities in the resampling (i.e. minutes with no records when downsampling). Both pandas 0.8.1 and 0.8.2.dev-f5a74d4 don't like it: ``` python import pandas as pd from datetime import datetime df = pd.DataFrame([1, 2], index=[datetime(2012,1,1,0,0,0), datetime(2012,1,1,0,5,0)]) df.resample("T", how="median") # Throws AssertionError ```
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MDU6SXNzdWU1ODc0MDQ4
1,689
string methods that return boolean arrays should return bool if 'nan' is in series (instead of 'nan')
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2
"2012-07-27T11:07:45Z"
"2012-09-09T00:42:17Z"
"2012-09-09T00:42:17Z"
NONE
null
``` python In [32]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) In [33]: s.str.startswith('A') Out[33]: 0 True 1 False 2 False 3 True 4 False 5 NaN 6 False 7 False 8 False In [34]: s[s.str.startswith('A')] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-34-f0d46b8c76ff> in <module>() ----> 1 s[s.str.startswith('A')] /net/home4/bmueller/.virtualenvs/myenv/lib/python2.6/site-packages/pandas/core/series.pyc in __getitem__(self, key) 447 # special handling of boolean data with NAs stored in object 448 # arrays. Since we can't represent NA with dtype=bool --> 449 if _is_bool_indexer(key): 450 key = self._check_bool_indexer(key) 451 key = np.asarray(key, dtype=bool) /net/home4/bmueller/.virtualenvs/myenv/lib/python2.6/site-packages/pandas/core/common.pyc in _is_bool_indexer(key) 499 if not lib.is_bool_array(key): 500 if isnull(key).any(): --> 501 raise ValueError('cannot index with vector containing ' 502 'NA / NaN values') 503 return False ValueError: cannot index with vector containing NA / NaN values ```
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5,874,480
MDU6SXNzdWU1ODc0NDgw
1,690
frequency of time series read with `read_csv`
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10
"2012-07-27T11:37:57Z"
"2015-12-13T20:39:07Z"
"2014-02-14T03:29:53Z"
NONE
null
I f I read a regular time series (with a fixed frequency) with read_csv, the resulting DataFrame has no freuqency. ``` python In [82]: lines = ['2012-07-27T13:%02d, %d' % (min, index) for index, min in enumerate(range(1, 59))] In [83]: input = StringIO.StringIO('\n'.join(lines)) In [84]: df = pd.read_csv(input, parse_dates=True, index_col=0, names='A') In [85]: df.index Out[85]: <class 'pandas.tseries.index.DatetimeIndex'> [2012-07-27 13:01:00, ..., 2012-07-27 13:58:00] Length: 58, Freq: None, Timezone: None ``` What I would like to have, is a possibility to set the frequency without resampling (or maybe read_csv could guess a frequency).
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ENH: add more possible bool values to read_csv #1295
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3
"2012-07-27T13:45:58Z"
"2014-06-16T10:11:58Z"
"2012-09-12T15:04:52Z"
CONTRIBUTOR
null
Added more possible bool values when parsing a file. New values include upper and lower case True/False and Yes/No.
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5,877,149
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1,692
Timezone Conversion Bug
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2
"2012-07-27T14:15:52Z"
"2012-08-12T21:24:43Z"
"2012-08-12T21:23:56Z"
NONE
null
It seems that it is not possible to use static timezones with pandas: http://nullege.com/codes/search/pytz.tzinfo.StaticTzInfo ``` python pandas.DatetimeIndex([dt(2012, 1, 1)], tz=pytz.timezone('EST')) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-483-37a722361cf0> in <module>() ----> 1 pandas.DatetimeIndex([dt(2012, 1, 1)], tz=pytz.timezone('EST')) /usr/lib64/python2.7/site-packages/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds) 224 ints = subarr.view('i8') 225 --> 226 subarr = lib.tz_localize_to_utc(ints, tz) 227 subarr = subarr.view(_NS_DTYPE) 228 /usr/lib64/python2.7/site-packages/pandas/lib.so in pandas.lib.tz_localize_to_utc (pandas/src/tseries.c:38322)() /usr/lib64/python2.7/site-packages/pandas/lib.so in pandas.lib._get_transitions (pandas/src/tseries.c:37001)() AttributeError: 'EST' object has no attribute '_utc_transition_times' ```
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read_csv: Tz-aware datetime.datetime cannot be converted to datetime64
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0
"2012-07-27T14:25:01Z"
"2012-08-10T18:35:52Z"
"2012-08-10T18:35:52Z"
CONTRIBUTOR
null
This may be related to some of the other TimeZone issues. This worked in 0.8, but now raises an error in 0.8.1: ``` import pandas import StringIO data = StringIO.StringIO("Date,x\n2012-06-13T01:39:00Z,0.5") pandas.read_csv(data,index_col=0, parse_dates=True) ``` Error: ``` ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True ```
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MDU6SXNzdWU1ODc4NzY1
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Unexpected behavior on Series.irow (Series.iget) with non-unique index
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1
"2012-07-27T15:27:56Z"
"2012-08-10T19:05:55Z"
"2012-08-10T19:05:54Z"
NONE
null
Hi, `Series.irow(i)` (and also `Series.iget(i)`), with `i` being an integer, returns more than one value on Series with non-unique indexes if the index at location `i` has duplicates. This is not the behavior I expected and it is also different from the behavior shown by `DataFrame.irow(i)`, since it only returns the row located at position `i` regardless of any existing index duplicates. Also the documentation for `Series.irow`/`Series.iget` specifies that an "int" parameter should return a "scalar" value, so I guess this might be a bug. Here is some sample code I've run using pandas '0.8.2.dev-f5a74d4': ``` In [52]: s = Series([0, 1, 2], index=[0, 1, 0]) In [53]: s.irow(0) # here I was expecting 0 Out[53]: 0 0 0 2 In [54]: s.irow(1) Out[54]: 1 In [55]: s.irow(2) # here I was expecting 2 Out[55]: 0 0 0 2 ``` and its DataFrame counterpart: ``` In [56]: df = DataFrame([[0], [1], [2]], index=[0, 1, 0]) In [57]: df.irow(0) Out[57]: 0 0 Name: 0 In [58]: df.irow(1) Out[58]: 0 1 Name: 1 In [59]: df.irow(2) Out[59]: 0 2 Name: 0 ``` Thanks and regards.
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"2012-07-27T16:16:11Z"
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MEMBER
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1,696
NaN-columns is plotted as all 0 column in pandas
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"2012-07-27T16:19:33Z"
"2012-08-12T19:05:36Z"
"2012-08-12T19:05:36Z"
NONE
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http://stackoverflow.com/questions/11689668/nan-columns-is-plotted-as-a-all-0-column-in-pandas A column full of NaNs is plotted as full of zeros.
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0
"2012-07-27T21:03:25Z"
"2012-08-12T00:53:10Z"
"2012-08-12T00:53:09Z"
NONE
null
Here is the bug to reproduce the bug/unexpected behavior: ``` python from pandas import DataFrame from pandas import MultiIndex midx = MultiIndex.from_tuples([('f1', 's1'),('f1','s2'),('f2', 's1'),('f2', 's2'),('f3', 's1'),('f3','s2')]) df = DataFrame([[1,2,3,4,5,6],[7,8,9,10,11,12]], columns= midx) df1 = df.select(lambda u: u[0] in ['f2', 'f3'], axis=1) df1_group = df1.groupby(axis=1, level=0) print df1_group.groups print df1_group.sum() ``` # When running the code, we can see that df1 is: ``` f1 f2 f3 s1 s2 s1 s2 s1 s2 0 1 2 3 4 5 6 1 7 8 9 10 11 12 ``` And df1 is selected from subblocks of df: ``` f2 f3 s1 s2 s1 s2 0 3 4 5 6 1 9 10 11 12 ``` After grouping df1 by the first level of multiindex of the columns, we can see df1_group.groups is: `{'f2': [('f2', 's1'), ('f2', 's2')], 'f3': [('f3', 's1'), ('f3', 's2')]}` However, when apply a sum function to aggregate the columns inside each group, as in the example code, df1_group.sum() results in: ``` f1 f2 f3 0 NaN 7 11 1 NaN 19 23 ``` It seems it tries to do the aggregation using the columns of df instead of df1 so the columns of the resulting dataframe include the label 'f1', which doesn't exist in df1.
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Interpolating with method='time' incorrect for resolution higher than daily
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1
"2012-07-28T19:57:26Z"
"2012-08-12T03:49:05Z"
"2012-08-12T03:48:53Z"
CONTRIBUTOR
null
The following code (pandas 0.8.0b2): ``` import pandas as pd index = pd.date_range('1/1/2012', periods=4, freq='12D') ts = pd.Series([0, 12, 24, 36], index) new_index = index.append(index + pd.DateOffset(days=1)).order() ts.reindex(new_index).interpolate(method='time') ``` correctly returns: 2012-01-01 0 2012-01-02 1 2012-01-13 12 2012-01-14 13 2012-01-25 24 2012-01-26 25 2012-02-06 36 2012-02-07 36 But when exchanging days for hours: ``` index = pd.date_range('1/1/2012', periods=4, freq='12H') ts = pd.Series([0, 12, 24, 36], index) new_index = index.append(index + pd.DateOffset(hours=1)).order() ts.reindex(new_index).interpolate(method='time') ``` the result is not correct: 2011-01-01 00:00:00 0 2011-01-01 01:00:00 12 2011-01-01 12:00:00 12 2011-01-01 13:00:00 12 2011-01-02 00:00:00 24 2011-01-02 01:00:00 36 2011-01-02 12:00:00 36 2011-01-02 13:00:00 36
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1,699
Avoid extra calls to calendar.monthrange.
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1
"2012-07-29T05:23:26Z"
"2014-07-01T03:27:19Z"
"2012-08-12T19:44:39Z"
CONTRIBUTOR
null
daysinmonth isn't used beyond setting cal, which is only used if calendar_end or business_end. Putting the if check in improved performance by a factor of 10. Once all of the flags are false, they can never become true (because of the &= operations), and short-circuiting the rest of the loop in that case saved an additional 20% runtime in the provided example code (see #1686). Much simpler than rewriting in Cython for now. monthrange is relatively expensive and was being called 3x per index datetime when doing a .resample("T"...) operation. Thanks for the help from Erik Welch!
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DataFrame repr fails for nonunique columns
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0
"2012-07-29T14:07:20Z"
"2012-08-10T17:26:23Z"
"2012-08-10T17:26:23Z"
CONTRIBUTOR
null
``` In [14]: df.columns Out[14]: Index([Ticker, Company Name, Position, $ Value, Position, $ Value, % of Equity, Current Price $, Closing Price $, Current Price, Closing Price, Px Change, Daily P&L $, Daily Perf., Total P/L 2008], dtype=object) In [15]: repr(df) \--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-15-d04570933a95> in <module>() ----> 1 repr(df) /Users/changshe/code/pandas/pandas/core/frame.pyc in __repr__(self) 579 buf = StringIO() 580 if self._need_info_repr_(): --> 581 self.info(buf=buf, verbose=self._verbose_info) 582 else: 583 self.to_string(buf=buf) /Users/changshe/code/pandas/pandas/core/frame.pyc in info(self, verbose, buf) 1310 lines.append(self.columns.summary(name='Columns')) 1311 -> 1312 counts = self.get_dtype_counts() 1313 dtypes = ['%s(%d)' % k for k in sorted(counts.iteritems())] 1314 lines.append('dtypes: %s' % ', '.join(dtypes)) /Users/changshe/code/pandas/pandas/core/frame.pyc in get_dtype_counts(self) 1342 for _, series in self.iterkv(): 1343 # endianness can cause dtypes to look different -> 1344 dtype_str = str(series.dtype) 1345 if dtype_str in counts: 1346 counts[dtype_str] += 1 /Users/changshe/code/pandas/pandas/core/frame.pyc in __getattr__(self, name) 1697 return self[name] 1698 raise AttributeError("'%s' object has no attribute '%s'" % -> 1699 (type(self).__name__, name)) 1700 1701 def __setattr__(self, name, value): AttributeError: 'DataFrame' object has no attribute 'dtype' ```
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