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https://api.github.com/repos/pandas-dev/pandas/issues/1901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1901/events | https://github.com/pandas-dev/pandas/pull/1901 | 6,813,578 | MDExOlB1bGxSZXF1ZXN0MjMxNjAyNA== | 1,901 | mention Ubuntu for NeuroDebian | {
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import csv
...
dataframe.to_csv(filepath, quoting=csv.QUOTE_NONNUMERIC)
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Some tests included.
First contribution to this great project !
I hope this could help.
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In [43]: read_csv(StringIO(data2))
Out[43]:
Id StringC
0 7117434 Is it possible to modify drop plot code so that t
In [44]: read_csv(StringIO(data2)).columns
Out[44]: Index([Id, StringCol], dtype=object)
```
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When converting a DataFrame to a recarray using `df.to_records`, date indexes are incorrectly converted.
``` python
>>> import pandas
>>> df = pandas.DataFrame([["one", "two", "three"], ["four", "five", "six"]], index=pandas.date_range("2012-01-01", "2012-01-02"))
>>> df
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https://api.github.com/repos/pandas-dev/pandas/issues/1910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1910/events | https://github.com/pandas-dev/pandas/issues/1910 | 6,860,019 | MDU6SXNzdWU2ODYwMDE5 | 1,910 | DOC: clarify Series.apply docstring | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1911 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1911/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1911/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1911/events | https://github.com/pandas-dev/pandas/issues/1911 | 6,866,545 | MDU6SXNzdWU2ODY2NTQ1 | 1,911 | Output for small decimal numbers is misleading | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-14T02:35:46Z | 2012-09-17T23:30:44Z | 2012-09-17T23:30:28Z | NONE | null | I think that [this](http://stackoverflow.com/questions/12417129/how-do-i-convert-a-column-from-a-pandas-dataframe-from-str-scientific-notation) question on stackoverflow sums up the problem fairly concisely.
In a nutshell, the `0` and `-0` output for very small decimal numbers is misleading and looks a lot like an err... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1912/events | https://github.com/pandas-dev/pandas/issues/1912 | 6,878,955 | MDU6SXNzdWU2ODc4OTU1 | 1,912 | python-dateutil downgrade on 2.7.2 | {
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pip install pandas
```
downgrades my python-dateutil from 2.1 to 1.5. I'm on python 2.7.2, OSX 10.8, distribute==0.6.28, numpy==1.6.2.
Is there any reason to believe that manually upgrading python-dateutil after installing pandas==0.8.1 will break pandas? How could I test the just-installed pandas?
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https://api.github.com/repos/pandas-dev/pandas/issues/1913 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1913/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1913/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1913/events | https://github.com/pandas-dev/pandas/issues/1913 | 6,893,724 | MDU6SXNzdWU2ODkzNzI0 | 1,913 | Constructing a Series with a set returns a set and not a Series | {
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``` python
In [1]: import pandas
In [2]: data = set([1,2,3,4])
In [3]: pandas.Series(data)
Out[3]: set([1, 2, 3, 4])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1914 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1914/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1914/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1914/events | https://github.com/pandas-dev/pandas/issues/1914 | 6,893,765 | MDU6SXNzdWU2ODkzNzY1 | 1,914 | Resample does not work on seconds | {
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Just found the resample functionality on a datetimeIndex series. Wonderful. My second attempt was on second base (attempted 15Sec), similar to 5Min and it did not work....
Is this not implemented ? Is my syntax wrong?
Thanks,
Luc
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Are there cases where using 0, ... | {
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```
df = pandas.DataFrame([1,1,2,2,3,3,4,5], columns=['variable'])
df.drop_duplicates('variable')
```
I believe cfe674e is the offending commit. A string is iterable but we don't want to iterate over it.
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When time series is set as index, line plot uses the TimeSeries_DateFormatter for recognizable date formatting. However, bar plot use the FixedFormatter which outputs the datetime as it is. Can bar plot also use the DateFormatter?
```
In [1]: from pandas import *
In [2]: from numpy.random import *
I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1921/events | https://github.com/pandas-dev/pandas/issues/1921 | 6,909,316 | MDU6SXNzdWU2OTA5MzE2 | 1,921 | AttributeError on import | {
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```
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/__init__.py... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1922/events | https://github.com/pandas-dev/pandas/issues/1922 | 6,909,829 | MDU6SXNzdWU2OTA5ODI5 | 1,922 | Handling for FixedOffsetTimezone in tseries/tools.py | {
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When passing a string column with timestamps like `"2012-04-23 10:00:23.123000-07:00"` (fixed offset timezone) to DataFrame.set_index() , getting exception:
**AttributeError: 'FixedOffsetTimezone' object has no attribute 'zone'**
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1923/events | https://github.com/pandas-dev/pandas/issues/1923 | 6,913,568 | MDU6SXNzdWU2OTEzNTY4 | 1,923 | Have the possibility for Series.unique() to return a Series rather than an array | {
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"followers_url": "https://api.github.com/users/wesm/... | 13 | 2012-09-17T09:06:36Z | 2019-07-26T14:00:46Z | 2012-11-28T03:26:04Z | CONTRIBUTOR | null | I admit I haven't looked at the code so there may be reasons for this, but I've found myself in the need of squeezing out duplicates from a Series but keeping the results as a Series.
Series.unique() however returns an array, so in my code I have to construct a Series twice:
``` python
series = pandas.Series([1,2,3,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1924/events | https://github.com/pandas-dev/pandas/issues/1924 | 6,925,714 | MDU6SXNzdWU2OTI1NzE0 | 1,924 | Result of add/sub/mul/div Series and DataFrame depends on type of index | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-17T16:56:12Z | 2013-01-18T04:50:27Z | 2013-01-18T04:50:20Z | CONTRIBUTOR | null | Hi,
We came across this bug while attempting to demean a DataFrame. Here's typical behavior:
```
aapl = DataReader('AAPL', data_source='yahoo')
aapl - aapl.mean(1)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 251 entries, 2011-09-19 00:00:00 to 2012-09-14 00:00:00
Data columns:
Open 251 non-null val... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1925/events | https://github.com/pandas-dev/pandas/pull/1925 | 6,932,314 | MDExOlB1bGxSZXF1ZXN0MjM2NTc4Mg== | 1,925 | Clean up the C code | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-17T20:40:34Z | 2012-09-17T23:00:48Z | 2012-09-17T23:00:48Z | CONTRIBUTOR | null | Some patches to make `python setup.py build` produce less noise. Compiled with GCC 4.6.3 and Clang 3.0; all tests pass.
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https://api.github.com/repos/pandas-dev/pandas/issues/1926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1926/events | https://github.com/pandas-dev/pandas/pull/1926 | 6,932,576 | MDExOlB1bGxSZXF1ZXN0MjM2NTkxOQ== | 1,926 | Smoke Testing | {
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Current code is certainly not intended to be merged in, just want to ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1927/events | https://github.com/pandas-dev/pandas/pull/1927 | 6,938,465 | MDExOlB1bGxSZXF1ZXN0MjM2ODc3NQ== | 1,927 | ENH: allow pos and neg for skip_footer #1843 | {
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I changed it slightly so it specifies the row at which to start skipping and can handle negative indexing.
Note that this does constitute an API change.
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https://api.github.com/repos/pandas-dev/pandas/issues/1928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1928/events | https://github.com/pandas-dev/pandas/issues/1928 | 6,941,792 | MDU6SXNzdWU2OTQxNzky | 1,928 | One more "'tzoffset' object has no attribute 'zone' " | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-18T05:37:22Z | 2016-10-12T23:03:12Z | 2012-09-19T02:19:05Z | NONE | null | **Version:** 0.9.0.dev_de31cbe (py2.7-macosx-10.6-x86_64)
Related to #1922 (closed)
I could be the only dummy with this kind of data so I might be able to find a few more of these.
Although I think these are typical Postgres tz-aware timestamps.
**Repro**
```
from StringIO import StringIO
table="""ts, d1
2012-05-... | {
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Example:
``` python
df = pd.DataFrame(np.arange(3), index=[['X', 'X', 'Y'], ['a','b','a']])
df.to_html(fid)
```
c... | {
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(version 2: much of the code changes now occur in panel.py; fdpanel.py is a thin wrapper over an n-dim
panel like obj; supporting NDPanel like objects with named higher dimension will be very easy)
instead of using a dic... | {
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HDFStore:
Store total seconds if no name and added timezone parsing if integer
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The excel2num() function I found on StackOverflow. Does that need to be referenced/acknowledged?
I'm happy to put in some unit tests but couldn't find a natural home for it. Most of the ExcelFile tests sit in test_... | {
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Tracing through ... | {
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Specifically, some of its columns contain data which is a tuple of integers, and these integers are different each unpickling. Here's a DataFrame which shows this problem:
http://bec.physics.monash.edu/docs/datafr... | {
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The following works, in both v0.8.1 and v0.9.0-667220a:
```
>>> from pandas.io.data import DataReader
>>> ts = DataReader("AAPL", "yahoo", start="01/01/2000")
>>> ts.head()
Open High Low Close Volume Adj Close
Date ... | {
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"description": "pandas testing functions... | closed | false | null | [] | {
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Anyway, I guess the tests should be skipped instead of throwing an error.
```
======================================================================
ERROR: test_parse_cols_int (pandas.io.tests.test_parsers.TestParsers)
-------------------------------------... | {
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a=DataFrame(randn(20,2),index=[chr(x+65) for x in range(20)])
a.ix[-1]=a.ix[-2]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-49-79d0c89159f2> in <module>()
----> 1 a.ix[-1]=a.ix[-2]
/u... | {
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In [25]: df = pandas.DataFrame(np.random.randn(4,4), columns=list('AABC'))
In [26]: df
Out[26]:
A A B C
0 -0.174905 0.332522 1.134984 -0.201270
1 1.730445 0.382556 -0.607761 1.221815
2 0.513049 0.196231 -1.746732 -0.252282
3 -0.297577 -1.000121 -0.090442 -2.129467
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```
gcc-4.2 -DNDEBUG -g -Os -Wall -Wstrict-prototypes -pipe -arch x86_64 -I/Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/nump... | {
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```
import pandas
from pandas.io.parsers import read_csv
timezone = 'America/Atikokan'
filename = 'forecast.csv'
data = read_csv(filename, index_col=0, header=None, squeeze=True)
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- xlim, ylim, sharex, sharey arguments are added
- kwds argument added which is passed to ax.scatter()
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"f... | 1 | 2012-09-23T18:40:23Z | 2014-05-29T05:45:15Z | 2014-05-29T05:45:15Z | CONTRIBUTOR | null | The following does not work:
```
import pandas as pd
import numpy as np
rng = pd.date_range('1/1/1994', periods=216, freq='M')
data = np.random.randint(100, high=350, size=216)
ts = pd.Series(data, index=rng)
from pandas.tseries.util import pivot_annual
ts_pivot = pivot_annual(ts)
```
Error:
```
ts_pivot = piv... | {
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"f... | 1 | 2012-09-23T18:56:36Z | 2013-09-16T03:51:38Z | 2013-09-16T03:51:38Z | CONTRIBUTOR | null | The data info
```
ts_monthly.dtype
Out[41]: dtype('float64')
ts_monthly.shape
Out[43]: (216,)
ts_monthly.index
Out[44]:
<class 'pandas.tseries.index.DatetimeIndex'>
[1994-01-31 00:00:00, ..., 2011-12-31 00:00:00]
Length: 216, Freq: M, Timezone: None
```
The data returns when pivoted to annual null values:
```
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-23T18:58:30Z | 2013-03-21T13:44:25Z | 2013-03-21T13:44:25Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: pd.tseries.util.pivot_annual
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
AttributeError: 'module' object has no attribute 'util'
```
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] | closed | false | null | [] | null | 2 | 2012-09-23T19:00:53Z | 2012-09-23T20:54:50Z | 2012-09-23T20:54:50Z | CONTRIBUTOR | null | The logic in the module
_tseries/util.py_
Currently, there's only one function that eases the reshaping of a (time) Series into a dataframe:
pivot_annual.
More functions in that range shall help processing of the series.
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http://pandas.pydata.org/developers.html#roadmap-to-1-0
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``` python
pandas/src/generated.c:237:31: fatal error: numpy/arrayobject... | {
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```
Shuffle word. Rearranges the top of the datastack as indicated in the stack effect pattern.
```
Not very helpful to new-comers who don't know what datastack means, or what exactly a stack effect pattern is.
At the very least it could link to help articles, better would be to have "shuffle-specifi... | {
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#1798
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This is cleaner, and the test actually works, which is always nice.
The mocking helper is primitive, but at least no new dependency is added.
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Possible and/or desirable to add an inplace keyword to dropna? Maybe an out keyword, so you can avoid typing the assignment? [**Edit:** I guess out doesn't really make sense like it does for numpy ufuncs.] Don't really mind if there's a copy even I just get tired of always doing assignm... | {
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"f... | 2 | 2012-09-25T07:55:25Z | 2014-05-25T23:40:35Z | 2014-05-25T23:40:35Z | CONTRIBUTOR | null | Add more details on selecting / indexing of time series based on dates or intervals.
to
https://github.com/pydata/pandas/blob/master/doc/source/indexing.rst
and
https://github.com/pydata/pandas/blob/master/doc/source/timeseries.rst
But I could not find any part showing how to E.g. getting all data where year =
2004 ... | {
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I think I have encountered a bug (0.8).
When using
DateOffset(second = 10) the seconds in the time series are not added or substracted to the datetime object, the seconds in the datetime are setto 10 instead of calculating the offset.
Thanks,
Luc
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] | closed | false | null | [] | null | 2 | 2012-09-25T14:46:39Z | 2013-12-04T00:57:52Z | 2012-10-02T18:03:00Z | NONE | null | I timeit, "groupby index" is almost 10 time slower than 'groupby column' , the index content is same as column for index is created by 'dataframe.set_index(col_name, drop=False)'. Can "dataframe.groupby(level=index)" be improved ?
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1. series & dataframe
series is a one-column dataframe, why not use dataframe's function to cover series ?
2. index & columns
can dataframe inde... | {
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```
from StringIO import StringIO
import pand... | {
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"f... | 5 | 2012-09-25T21:18:25Z | 2015-05-05T10:40:55Z | 2015-05-05T10:40:55Z | CONTRIBUTOR | null | in the docs, numpy is often imported with
```
from numpy import np
```
http://pandas.pydata.org/pandas-docs/dev/reshaping.html?highlight=curious
for an easier reuse, what about adopting
```
import pandas as pd
```
for the docs?
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``` python
df = pandas.DataFrame([u"\xe9"])
df[0].unique()
<snip>
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/pyth... | {
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"f... | 1 | 2012-09-25T22:30:30Z | 2014-05-29T03:51:41Z | 2014-05-29T03:51:41Z | NONE | null | Calling `np.delete` on a `pandas.DataFrame` fails like so:
``` python
>>> df = pandas.DataFrame([[1,2,3],[4,5,6],[7,8,9]])
>>> np.delete(df, [1], axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/numpy-1.6.2-py2.7-macosx-10.8-intel.egg/numpy/lib... | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-26T02:17:22Z | 2012-09-26T21:04:39Z | 2012-09-26T21:04:39Z | CONTRIBUTOR | null | In [33]: cols2 = pd.MultiIndex.from_tuples(list(itertools.product(['A', 'B', 'C'], ['X', 'Y', 'Z']))*2)
In [34]: df2 = DataFrame(np.random.randn(3, len(cols2)), columns=cols2)
In [35]: df2
Out[35]: ---------------------------------------------------------------------------
AttributeError Tr... | {
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```
df = DataFrame([
dict(a=1, p=0),
dict(a=2, m=10),
dict(a=3, m=11, p=20),
dict(a=4, m=12, p=21)
], columns=('a', 'm', 'p', 'x'))
```
```
a m p x
0 1 N... | {
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``` python
df = DataFrame([
dict(a=1, p=0),
dict(a=2, m=10),
dict(a=3, m=11, p=20),
dict(a=4, m=12, p=21)
], columns=('a', 'm', 'p', 'x'))
```
```
. a m p x
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``` python
import pandas
from StringIO import StringIO
fin = StringIO("Łaski, Jan;1")
df1 = pandas.read_table(fin, sep=";", encoding="utf-8", header=None)
fin.seek(0)
df2 = pandas.read_csv(fin, sep=";", encoding="utf-8", header=None)
type(df1["X.1... | {
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Back to using pandas after having to take a hiatus in MATLAB. One minor problem I ran into was the use of re.match instead of re.search (as is documented) in the DataFrame.filter method.
I assumed there was a good reason for the change, but for most of my use cases the re.search way was less ve... | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-26T21:32:15Z | 2012-09-26T23:08:28Z | 2012-09-26T23:08:28Z | NONE | null | pi = pandas.PeriodIndex(start='2012-01-01', periods=10, freq='W-MON')
ts = pandas.TimeSeries(range(len(pi)), index=pi)
dt1 = datetime.datetime(2011, 10, 2)
dt2 = datetime.datetime(2012, 1, 2)
dt3 = datetime.datetime(2012, 2, 10)
dt4 = datetime.datetime(2012, 4, 20)
ts[dt2: dt3]
ts[dt1: dt3]
ts[dt2: dt4]
The first sl... | {
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```
qcut in pandas 0.8.1 is failing for some quantile lists but not others (see below). Sorry if I'm missing something...
type(F.g)
Out[3]:
pandas.core.series.TimeSeries
chg = (F.g[20:]-F.g[20:].shift(1))
fac = qcut(chg, [0, .5, 1])
fac
Out[4]:
Categorical: g
array([nan, [-1005094.8... | {
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On advice I installed Python 3.2 on my Windows x64 system. All was going well until I got to Pandas (which seems like the answer to a couple of my prayers).
I can't find a version that fits. In case they worked I tried installing:
pandas-0.8.1.win-amd64-py3.1.exe
pandas-0.8.1.win32-py3.2.exe
but b... | {
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> idx.summary()
---
UnicodeEncodeError Traceback (most recent call last)
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1 idx=pd.Index([u'\u05d3\u05e4\u05e0\u05d4',u'\u05d3\u05e4\u05e0\u05d4'])
----> 2 idx.summary(... | {
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Had to hemstring another test so the fix will not break the tests, see
first patch in series I think it's the right thing to do.
There was another fix for a similar unicode problem in 703bbba7ff3bd,
there seems to be some Unicode whack-a-mole going on...
I'm willing to try my hand at fixing the unicod... | {
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A store created outside of pandas can't be loaded into pandas with the HFDStore() method.
An error occurs : AttributeError: Attribute 'pandas_type' does not exist in node: '/'
To reproduce, create a store using pytables, and try to load it in pandas. It will raise this error.
In my opinion, if pandas is supp... | {
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``` python
In [3]: series = pandas.Series(["car", "house", "tree", 1])
In [4]: series
Out[4]:
0 car
1 house
2 tree
3 1
In [5]: series.astype("int")
ValueError: invalid literal for long() with base 10: 'tree'
```
This doesn't:
``` python
In [7]: series = pandas.Series(["car", "house... | {
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Should be able to take another Panel or dict of DataFrame
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https://api.github.com/repos/pandas-dev/pandas/issues/1989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1989/events | https://github.com/pandas-dev/pandas/issues/1989 | 7,237,048 | MDU6SXNzdWU3MjM3MDQ4 | 1,989 | Using numpy median with groupby aggregrate | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-09-29T11:39:09Z | 2012-11-28T21:29:35Z | 2012-11-28T21:29:35Z | CONTRIBUTOR | null | Migrated from http://stackoverflow.com/questions/12651618/inconsisitency-in-results-of-aggregating-pandas-groupby-object-using-numpy-media
```
test = pd.DataFrame({'A' : [10,11,12,13,15,25,43,70],
'B' : [1,2,3,4,5,6,7,8],
'C' : [1,1,1,1,2,2,2,2]})
A B C
0 10 1 1
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https://api.github.com/repos/pandas-dev/pandas/issues/1990 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1990/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1990/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1990/events | https://github.com/pandas-dev/pandas/issues/1990 | 7,239,081 | MDU6SXNzdWU3MjM5MDgx | 1,990 | NaN comparison in merge | {
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] | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-29T17:31:42Z | 2012-09-29T21:04:45Z | 2012-09-29T21:03:14Z | CONTRIBUTOR | null | Parking this here so I don't forget about it. I assume that the NaNs here are not comparing equal, so we get "duplicate" rows on a merge, which I think is a bug.
```
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1991 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1991/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1991/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1991/events | https://github.com/pandas-dev/pandas/pull/1991 | 7,239,262 | MDExOlB1bGxSZXF1ZXN0MjQ5MzMxMA== | 1,991 | ENH: autocov function for Series | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2012-09-29T17:55:43Z | 2014-07-19T02:19:29Z | 2013-09-21T12:58:04Z | CONTRIBUTOR | null | Add an autocov function to Series.
Default is single lag w/ bias, but those are configurable.
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https://api.github.com/repos/pandas-dev/pandas/issues/1992 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1992/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1992/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1992/events | https://github.com/pandas-dev/pandas/issues/1992 | 7,239,362 | MDU6SXNzdWU3MjM5MzYy | 1,992 | Series vs. TimeSeries methods | {
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"f... | 2 | 2012-09-29T18:10:01Z | 2013-09-21T12:57:19Z | 2013-09-21T12:57:19Z | CONTRIBUTOR | null | Just looking at a few of these after PR #1991. Should autocorr and autocov be methods of TimeSeries rather than Series since not all Series are going to be TimeSeries? Maybe at least head in this direction?
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https://api.github.com/repos/pandas-dev/pandas/issues/1993 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1993/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1993/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1993/events | https://github.com/pandas-dev/pandas/issues/1993 | 7,244,635 | MDU6SXNzdWU3MjQ0NjM1 | 1,993 | date_range export to csv weird behavior | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-09-30T10:47:35Z | 2012-10-01T01:20:22Z | 2012-10-01T01:19:11Z | NONE | null | Hello,
Working with 0.8.1
date_range works nicely, I am using the technique to reduce sparse GPS measurements to a fixed time step.
procedure:
step 1: create date_range ("10S"), all datetime within a single day (input from gpx)
step 2: outer join with original data
step 3: interpolate the locations
Everything works... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1994 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1994/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1994/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1994/events | https://github.com/pandas-dev/pandas/pull/1994 | 7,244,836 | MDExOlB1bGxSZXF1ZXN0MjQ5NTE3NA== | 1,994 | More unicode fixes | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-30T11:32:27Z | 2014-07-17T06:08:21Z | 2012-10-01T01:44:27Z | NONE | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1995 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1995/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1995/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1995/events | https://github.com/pandas-dev/pandas/issues/1995 | 7,244,977 | MDU6SXNzdWU3MjQ0OTc3 | 1,995 | coding style - assert vs self.assert* vs self.assert_ in tests, which is it? | {
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<table>
<tr>
<th>grep regexp</th><th>count</th>
</tr>
<tr><td>self.assertSomething()</td><td>1617</td> </tr>
<tr><td>self.assert_() </td><td>2122</td> </tr>
<tr><td>assert()</td><td>407</td> </tr>
</table>
This might be a non-issue, but co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1996 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1996/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1996/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1996/events | https://github.com/pandas-dev/pandas/issues/1996 | 7,246,202 | MDU6SXNzdWU3MjQ2MjAy | 1,996 | PyTables enhancements for selection | {
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changes to pandas.io.pytables to support more natural selection (from tables):
1. rename column -> major, index -> minor ( to be more consistent with panel nomenclature)
2. provide parsable string selection methodology - pretty easy to do - and can be backwards compatible
`store.select('mypanel', where = [ 'maj... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1997 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1997/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1997/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1997/events | https://github.com/pandas-dev/pandas/pull/1997 | 7,246,394 | MDExOlB1bGxSZXF1ZXN0MjQ5NTYyOQ== | 1,997 | ENH: Allow DataFrame.update to work on non DataFrame object if coercible | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-30T15:47:02Z | 2014-06-17T00:40:43Z | 2012-10-05T00:36:27Z | CONTRIBUTOR | null | On issue #1988, a new Panel.update function should accept a dict of DataFrames. Right now, DataFrame.update can't accept a dict of Series (or a dict of arrays for example). To me, it wouldn't make much sense for Panel.update to have the functionality while a DataFrame does not.
Currently:
```
>>> df.update(s) #s is... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1998 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1998/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1998/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1998/events | https://github.com/pandas-dev/pandas/issues/1998 | 7,249,065 | MDU6SXNzdWU3MjQ5MDY1 | 1,998 | TimeSeries.resample how='last' & 'first' broken? | {
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pd.**version**
Out[79]: '0.8.1'
rng8h = pd.date_range('1/1/12', periods=10, freq='8H')
rng8h
Out[70]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-04 00:00:00]
Length: 10, Freq: 8H, Timezone: None
ts8h = S... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1999 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1999/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1999/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1999/events | https://github.com/pandas-dev/pandas/pull/1999 | 7,250,003 | MDExOlB1bGxSZXF1ZXN0MjQ5NjcwMQ== | 1,999 | ENH: Add method Panel.update | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-09-30T23:15:42Z | 2014-06-20T22:05:44Z | 2012-10-05T00:25:57Z | CONTRIBUTOR | null | ```
panel_one.update(panel_two)
```
Addresses Issue #1988.
Delegates most the work to DataFrame.update. Aligns on items whereas DataFrame.update aligns on indices.
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https://api.github.com/repos/pandas-dev/pandas/issues/2000 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2000/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2000/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2000/events | https://github.com/pandas-dev/pandas/issues/2000 | 7,250,457 | MDU6SXNzdWU3MjUwNDU3 | 2,000 | Change default column names in read_* functions when header=False | {
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"followers_url": "https://api.github.com/users/wesm/... | 7 | 2012-10-01T00:14:26Z | 2012-10-01T18:59:29Z | 2012-10-01T18:59:29Z | MEMBER | null | In retrospect I think using R's convention of `X.1`, ..., `X.N` was a bad move. Maybe `range(N)` would be better? Will cause API breakage but not the end of the world
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