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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/pandas-dev/pandas/issues/1601
|
https://api.github.com/repos/pandas-dev/pandas
|
https://api.github.com/repos/pandas-dev/pandas/issues/1601/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/1601/comments
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https://api.github.com/repos/pandas-dev/pandas/issues/1601/events
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https://github.com/pandas-dev/pandas/issues/1601
| 5,531,549 |
MDU6SXNzdWU1NTMxNTQ5
| 1,601 |
Year partial string indexing doesn't work right with freq='M' PeriodIndex
|
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| 0 | 2012-07-10T19:22:58Z | 2012-07-11T18:57:09Z | 2012-07-11T18:57:09Z |
MEMBER
| null |
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https://api.github.com/repos/pandas-dev/pandas/issues/1601/timeline
| null | null | null |
|
https://api.github.com/repos/pandas-dev/pandas/issues/1602
|
https://api.github.com/repos/pandas-dev/pandas
|
https://api.github.com/repos/pandas-dev/pandas/issues/1602/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/1602/comments
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https://api.github.com/repos/pandas-dev/pandas/issues/1602/events
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https://github.com/pandas-dev/pandas/issues/1602
| 5,532,380 |
MDU6SXNzdWU1NTMyMzgw
| 1,602 |
Series repr incorrect if name==0
|
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| false | null |
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| 0 | 2012-07-10T19:58:53Z | 2012-07-11T16:46:22Z | 2012-07-11T16:46:22Z |
MEMBER
| null |
```
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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| null | null | null |
https://api.github.com/repos/pandas-dev/pandas/issues/1603
|
https://api.github.com/repos/pandas-dev/pandas
|
https://api.github.com/repos/pandas-dev/pandas/issues/1603/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/1603/comments
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|
https://github.com/pandas-dev/pandas/issues/1603
| 5,532,674 |
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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https://api.github.com/repos/pandas-dev/pandas/issues/1604
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https://github.com/pandas-dev/pandas/issues/1604
| 5,546,555 |
MDU6SXNzdWU1NTQ2NTU1
| 1,604 |
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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datetools.parser.parse
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[] | null | 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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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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Copy 0.7.0 what's new note re: integer indexes to gotchas page
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| 0 | 2012-07-11T17:41:35Z | 2012-07-11T19:39:40Z | 2012-07-11T19:39:40Z |
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MDExOlB1bGxSZXF1ZXN0MTc3MDcxMw==
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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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MDU6SXNzdWU1NTY1MTM4
| 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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MDU6SXNzdWU1NTc0NjA4
| 1,614 |
recursive 301 redirect from sourceforge -> pydata
|
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[] | null | 2 | 2012-07-12T15:31:29Z | 2012-10-06T21:49:47Z | 2012-10-06T21:49:47Z |
NONE
| null |
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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MDU6SXNzdWU1NTgyNDM3
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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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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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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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Pandas cheat sheet
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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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| 1 | 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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| 5,619,342 |
MDU6SXNzdWU1NjE5MzQy
| 1,620 |
__repr__ wrong column alignment with non-ascii characters
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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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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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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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a la groupby-aggregate
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MDU6SXNzdWU1NjI5NTg3
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DatetimeIndex unnecessarily copies int64 array
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| 0 | 2012-07-15T20:58:38Z | 2012-07-15T23:08:12Z | 2012-07-15T23:08:12Z |
MEMBER
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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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| 0 | 2012-07-15T22:04:46Z | 2012-07-19T19:07:56Z | 2012-07-19T19:07:56Z |
MEMBER
| null |
http://imgur.com/BGUgT
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MDU6SXNzdWU1NjMwMDQ1
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What should frame.groupby([]) return?
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| 2 | 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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MDU6SXNzdWU1NjMwNTY3
| 1,627 |
Strange dates in DatetimeIndex after upgrade
|
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[] | null | 0 | 2012-07-15T23:37:27Z | 2012-07-16T02:05:12Z | 2012-07-16T02:05:12Z |
NONE
| null |
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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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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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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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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Resample loses index name
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| 0 | 2012-07-17T04:28:40Z | 2012-07-18T14:35:00Z | 2012-07-18T14:35:00Z |
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MDU6SXNzdWU1NjYzNDk1
| 1,632 |
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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| 5,666,711 |
MDExOlB1bGxSZXF1ZXN0MTgxNjM3MQ==
| 1,633 |
Retain index name on resample
|
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| 0 | 2012-07-17T15:30:43Z | 2012-07-18T14:29:49Z | 2012-07-18T14:29:49Z |
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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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| 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 |
MDU6SXNzdWU1Njg0NDUy
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`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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MDU6SXNzdWU1NjkzMzUx
| 1,637 |
Support dateutil 2.1
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| 0 | 2012-07-18T16:19:29Z | 2012-07-19T19:07:43Z | 2012-07-19T19:07:43Z |
MEMBER
| null |
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| 5,698,114 |
MDU6SXNzdWU1Njk4MTE0
| 1,638 |
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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| 1,639 |
Dataframe.ix behavior inconsistent (depends on index type)
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[] | null | 7 | 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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| 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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| 1,641 |
ExcelWriter not working
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[] | null | 2 | 2012-07-19T01:08:57Z | 2012-07-19T01:47:42Z | 2012-07-19T01:47:42Z |
NONE
| null |
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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MDU6SXNzdWU1NzA4MzIz
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Cannot assign to df.icol(n), df.irow(n)
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| 5 | 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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[] | null | 1 | 2012-07-19T15:10:35Z | 2012-07-19T17:38:26Z | 2012-07-19T17:38:26Z |
NONE
| null |
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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| 0 | 2012-07-19T20:09:52Z | 2013-12-04T00:57:58Z | 2012-07-19T21:38:47Z |
MEMBER
| null |
```
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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| 0 | 2012-07-19T20:11:57Z | 2012-07-19T21:21:46Z | 2012-07-19T21:21:46Z |
MEMBER
| null |
```
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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| 0 | 2012-07-19T20:13:12Z | 2012-07-21T14:01:59Z | 2012-07-21T14:01:59Z |
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MDU6SXNzdWU1NzIzNTQz
| 1,647 |
String timestamp aliases don't work with tz-aware data
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MDU6SXNzdWU1NzIzNTUx
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Resample with how='median' broken in some cases
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| 2 | 2012-07-19T20:14:12Z | 2012-08-11T22:48:43Z | 2012-08-11T22:48:43Z |
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MDU6SXNzdWU1NzI0NTUx
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A bug in pandas.concat when the given dict contains None
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| 0 | 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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MDExOlB1bGxSZXF1ZXN0MTg0MzExMA==
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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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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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| 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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| 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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MDU6SXNzdWU1NzQ3ODUx
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Request for "EasyFix" tag
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[] | null | 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
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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
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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
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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
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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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MDU6SXNzdWU1Nzg3MDA4
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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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Add option to Series.map for NA handling
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| 0 | 2012-07-23T19:43:45Z | 2012-08-12T18:13:54Z | 2012-08-12T18:13:54Z |
MEMBER
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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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MDU6SXNzdWU1NzkxODk1
| 1,663 |
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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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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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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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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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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MDU6SXNzdWU1ODAzNjg3
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BUG: plotting daily data with two-day gaps
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[] | null | 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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MDU6SXNzdWU1ODA0MzQ5
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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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MDU6SXNzdWU1ODA0OTU3
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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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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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MDU6SXNzdWU1ODE4Njkz
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Strings like 5-2001 broken in parser
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| 0 | 2012-07-24T22:24:09Z | 2012-07-24T22:31:02Z | 2012-07-24T22:31:02Z |
MEMBER
| null |
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
| null |
```
# 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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MDU6SXNzdWU1ODIzODAy
| 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 |
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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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MDU6SXNzdWU1ODMwNjc0
| 1,676 |
timezone aware DatetimeIndex from datetime.datetime (regression)
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| 1 | 2012-07-25T14:02:58Z | 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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MDU6SXNzdWU1ODM4NzQy
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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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MDU6SXNzdWU1ODQxNzMx
| 1,678 |
.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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| 1,679 |
What should Series do when passed generator?
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| 0 | 2012-07-26T02:58:38Z | 2012-08-12T03:39:46Z | 2012-08-12T03:39:45Z |
MEMBER
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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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| 0 | 2012-07-26T15:10:18Z | 2012-08-12T03:57:56Z | 2012-08-12T03:57:56Z |
MEMBER
| null |
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 |
MDU6SXNzdWU1ODU2MDg1
| 1,681 |
Concat datetime64 bug
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| 1 | 2012-07-26T15:14:08Z | 2012-08-12T17:50:07Z | 2012-08-12T17:50:07Z |
MEMBER
| null |
```
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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MDU6SXNzdWU1ODU2MTI5
| 1,682 |
DatetimeIndex with time zone loses tzinfo in DataFrame constructor
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| 0 | 2012-07-26T15:15:52Z | 2012-08-10T19:14:17Z | 2012-08-10T19:14:17Z |
MEMBER
| null |
```
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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MDU6SXNzdWU1ODU2MTc0
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Tz-aware DatetimeIndex shift issue
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| 0 | 2012-07-26T15:17:33Z | 2012-08-12T01:05:09Z | 2012-08-12T01:05:09Z |
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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| 1 | 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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plotting._stringify not unicode safe
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| 2 | 2012-07-26T20:27:10Z | 2012-08-06T19:57:43Z | 2012-08-06T19:57:43Z |
NONE
| null |
/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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| 5 | 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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MDU6SXNzdWU1ODY3ODIy
| 1,687 |
scatter_matrix mislabels axes with DataFrame index
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[] | null | 1 | 2012-07-27T00:51:25Z | 2012-07-27T01:13:25Z | 2012-07-27T01:13:25Z |
NONE
| null |
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 | 2012-07-27T02:59:56Z | 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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| 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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| 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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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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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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| 0 | 2012-07-27T16:16:11Z | 2012-08-11T22:24:04Z | 2012-08-11T22:24:04Z |
MEMBER
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MDU6SXNzdWU1ODc5OTQ5
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NaN-columns is plotted as all 0 column in pandas
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| 0 | 2012-07-27T16:19:33Z | 2012-08-12T19:05:36Z | 2012-08-12T19:05:36Z |
NONE
| null |
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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MDU6SXNzdWU1ODg1NzA3
| 1,697 |
Bug/unexpected behaviour when using groupby and aggregation functions with DataFrame
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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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| 5,895,782 |
MDU6SXNzdWU1ODk1Nzgy
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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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| 5,901,581 |
MDExOlB1bGxSZXF1ZXN0MTkxNjM2Mg==
| 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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