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} | 11 | "2012-03-11T20:55:39Z" | "2020-12-18T23:25:01Z" | "2015-01-28T11:05:37Z" | CONTRIBUTOR | null | Can the scikits.timeseries based interval code be extended to handle multiples of base durations?
Is it worth considering dropping the existing implementation and use, eg, start + end points (either two arrays, or one structured array), also have for fuzzier indexing some sort of
http://en.wikipedia.org/wiki/Interval_tree
"Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point"
ie log(n) operations for querying range intersections
Another possibility is an interval skip list, probabilistic log(n) behavior.
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} | 1 | "2012-03-11T21:56:35Z" | "2012-04-23T00:53:23Z" | "2012-04-23T00:53:23Z" | CONTRIBUTOR | null | Analogously to lubridate or joda date/time, this would be a period of time (say a month or a year) without reference to particular time endpoints. In contrast to a duration, which is an absolute length of time in seconds, a period is relative (e.g, a month has 28 to 31 days)
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} | 1 | "2012-03-12T05:14:41Z" | "2014-06-12T16:38:17Z" | "2013-02-27T18:41:23Z" | NONE | null | My attempt at a weighted rolling average. Doesn't force the weights to sum to 1. If the min period is less than the length of the arrays, the nan value is skipped along with weighted value. Should the weights be re-normed somehow?
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/2"
} | 4 | "2012-03-12T10:22:53Z" | "2012-06-01T19:20:48Z" | "2012-06-01T19:20:43Z" | CONTRIBUTOR | null | https://launchpadlibrarian.net/96431877/buildlog_ubuntu-precise-amd64.pandas_0.7.2-ubuntu0~1987~precise1_FAILEDTOBUILD.txt.gz
```
pandas.tests.test_tseries.test_pad_backfill_object_segfault ... ok
pandas.tests.test_tseries.test_arrmap ... ok
pandas.tests.test_tseries.test_series_grouper ... ok
======================================================================
ERROR: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 109, in test_boxplot
_check_plot_works(df.boxplot)
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_hist (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 120, in test_hist
_check_plot_works(df.hist)
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_plot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 67, in test_plot
_check_plot_works(df.plot, grid=False)
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_plot_bar (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 89, in test_plot_bar
_check_plot_works(df.plot, kind='bar')
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_plot_int_columns (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 126, in test_plot_int_columns
_check_plot_works(df.plot, legend=True)
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_hist (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 46, in test_hist
_check_plot_works(self.ts.hist)
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
======================================================================
ERROR: test_plot (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 36, in test_plot
_check_plot_works(self.ts.plot, label='foo')
File "/build/buildd/pandas-0.7.2/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 136, in _check_plot_works
fig = plt.gcf()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 369, in gcf
return figure()
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 343, in figure
**kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 80, in new_figure_manager
window = Tk.Tk()
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1688, in __init__
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
TclError: no display name and no $DISPLAY environment variable
----------------------------------------------------------------------
Ran 1475 tests in 31.196s
```
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} | 9 | "2012-03-12T10:35:19Z" | "2014-01-20T22:48:31Z" | "2014-01-20T22:48:31Z" | CONTRIBUTOR | null | Since pandas is developing very quickly, daily packages can help users to keep track of changes while using their usual software install mechanisms.
Launchpad offers inidvidual package repositories (PPA) and automatic build procedures based on the [bazaar repository](https://code.launchpad.net/+branch/~pythonxy/numpy/pandas), a mirror of the github repository.
As part of the [PythonXY for Ubuntu initiative](https://launchpad.net/~pythonxy), a bzr builder [recipe has been set up](https://code.launchpad.net/~timmie/+recipe/pandas-daily-current) to automatically create updated packages after repository changes.
The packages will be placed in the [Packaged Upstream Development Snapshots](https://code.launchpad.net/~pythonxy/+archive/individual-devel).
This bug shall track problems encountered during the builds.
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https://api.github.com/repos/pandas-dev/pandas/issues/907 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/907/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/907/comments | https://api.github.com/repos/pandas-dev/pandas/issues/907/events | https://github.com/pandas-dev/pandas/pull/907 | 3,611,695 | MDExOlB1bGxSZXF1ZXN0OTY3MDI2 | 907 | Patched _safe_fetch to ensure a list of tuples is returned. | {
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} | [] | closed | false | null | [] | null | 2 | "2012-03-12T14:17:04Z" | "2014-06-12T05:13:10Z" | "2012-03-16T19:35:46Z" | CONTRIBUTOR | null | When MSSQL is used via pyodbc, Cursor.fetchall() returns a list of pyodbc.Row instances. This just casts the rows as tuples so they don't give a TypeError in DataFrame.from_records.
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https://api.github.com/repos/pandas-dev/pandas/issues/908 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/908/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/908/comments | https://api.github.com/repos/pandas-dev/pandas/issues/908/events | https://github.com/pandas-dev/pandas/issues/908 | 3,612,619 | MDU6SXNzdWUzNjEyNjE5 | 908 | Test Failed: pandas.io.tests.test_parsers.TestParsers | {
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} | [] | closed | false | null | [] | null | 1 | "2012-03-12T15:06:48Z" | "2012-03-12T19:45:02Z" | "2012-03-12T19:45:02Z" | CONTRIBUTOR | null | Following error when I ran nosetests in pandas\io\tests:
Traceback (most recent call last):
File "C:\src\pandas\pandas\io\tests\test_parsers.py", line 219, in test_read_c
sv_no_index_name
self.assert_(df.ix[:, ['A', 'B', 'C', 'D']].values.dtype == np.float64)
File "C:\src\pandas\pandas\core\indexing.py", line 33, in **getitem**
return self._getitem_tuple(key)
File "C:\src\pandas\pandas\core\indexing.py", line 115, in _getitem_tuple
retval = retval.ix._getitem_axis(key, axis=i)
File "C:\src\pandas\pandas\core\indexing.py", line 174, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\src\pandas\pandas\core\indexing.py", line 215, in _getitem_iterable
return self.obj.reindex(**{axis_name: keyarr})
File "C:\src\pandas\pandas\core\frame.py", line 1851, in reindex
frame = frame._reindex_columns(columns, copy, level, fill_value)
File "C:\src\pandas\pandas\core\frame.py", line 1914, in _reindex_columns
copy, fill_value)
File "C:\src\pandas\pandas\core\frame.py", line 1929, in _reindex_with_indexer
s
fill_value=fill_value)
File "C:\src\pandas\pandas\core\internals.py", line 737, in reindex_indexer
return self._reindex_indexer_items(new_axis, indexer, fill_value)
File "C:\src\pandas\pandas\core\internals.py", line 757, in _reindex_indexer_i
tems
item_order = com.take_1d(self.items.values, indexer)
File "C:\src\pandas\pandas\core\common.py", line 188, in take_1d
take_f(arr, indexer, out=out, fill_value=fill_value)
File "generated.pyx", line 951, in pandas._tseries.take_1d_object (pandas\src\
tseries.c:40483)
fv = fill_value
TypeError: take_1d_object() got an unexpected keyword argument 'fill_value'
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https://api.github.com/repos/pandas-dev/pandas/issues/909 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/909/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/909/comments | https://api.github.com/repos/pandas-dev/pandas/issues/909/events | https://github.com/pandas-dev/pandas/issues/909 | 3,619,805 | MDU6SXNzdWUzNjE5ODA1 | 909 | Columns off one cell in to_html with MultiIndex | {
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} | 3 | "2012-03-13T21:49:05Z" | "2012-03-15T01:10:22Z" | "2012-03-15T01:09:59Z" | CONTRIBUTOR | null | Sample code to reproduce the error:
import numpy as np
import pandas
X = np.random.rand(10,10)
Y = np.ones((10,1),dtype=int)
df1 = pandas.DataFrame(X)
df1 = df1.join(pandas.DataFrame(Y),lsuffix='.X')
print df1-df1.mean()
---
Traceback (most recent call last):
File "pls_model.py", line 383, in <module>
run(df)
File "pls_model.py", line 305, in run
report(df.copy(),n_comp,priors=priors,plot=False)
File "pls_model.py", line 83, in report
plsgnb.fit(X,Y,priors=priors)
File "C:\src\multivariate\src\multivariate\PLS.py", line 477, in fit
self.model.fit(X,Y)
File "C:\src\multivariate\src\multivariate\PLS.py", line 145, in fit
print x - self.Xmean
File "C:\src\pandas\pandas\core\frame.py", line 177, in f
return self._combine_series(other, func, fill_value, axis, level)
File "C:\src\pandas\pandas\core\frame.py", line 2526, in _combine_series
return self._combine_series_infer(other, func, fill_value)
File "C:\src\pandas\pandas\core\frame.py", line 2541, in _combine_series_infer
return self._combine_match_columns(other, func, fill_value)
File "C:\src\pandas\pandas\core\frame.py", line 2552, in _combine_match_columns
left, right = self.align(other, join='outer', axis=1, copy=False)
File "C:\src\pandas\pandas\core\frame.py", line 1735, in align
method=method)
File "C:\src\pandas\pandas\core\frame.py", line 1804, in _align_series
return (left_result.fillna(fill_value, method=method),
File "C:\src\pandas\pandas\core\frame.py", line 2426, in fillna
new_data = self._data.fillna(value)
File "C:\src\pandas\pandas\core\internals.py", line 938, in fillna
new_blocks = [b.fillna(value) for b in self.blocks]
File "C:\src\pandas\pandas\core\internals.py", line 211, in fillna
new_values.flat[mask] = value
ValueError: cannot convert float NaN to integer
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} | 2 | "2012-03-14T08:15:11Z" | "2017-12-14T09:44:09Z" | "2012-03-14T10:08:53Z" | CONTRIBUTOR | null | Accedentically swapping the index and column label in using `DataFrame.set_value()` gave an unexpected result.
All values, except the one to be set, in the _DataFrame_ became _NaN_.
``` python
In [4]: df
Out[4]:
A B C
0 0.463981 0.163885 0.644793
1 0.979047 0.347373 0.736267
2 0.103155 0.304439 0.911314
In [5]: df.set_value('C', 2, 1.0)
Out[5]:
A B C 2
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
C NaN NaN NaN 1
In [6]: pandas.__version__
Out[6]: '0.7.2.dev-79decd7'
```
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In [20]: df1 = pandas.DataFrame(np.random.rand(3,3), index=range(3), columns=list('ABC'))
In [21]: df1
Out[21]:
A B C
0 0.061189 0.035471 0.104002
1 0.894004 0.545950 0.852395
2 0.794040 0.448952 0.338134
In [22]: df2 = pandas.DataFrame(np.random.rand(3,3), index=range(3,6), columns=list('DEF'))
In [23]: df2
Out[23]:
D E F
3 0.558891 0.097873 0.643581
4 0.572424 0.856442 0.484147
5 0.865900 0.968363 0.793452
In [24]: df1 - df2
Out[24]:
A B C D E F
0 NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN
```
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} | 1 | "2012-03-14T16:05:22Z" | "2016-01-18T00:39:36Z" | "2016-01-18T00:39:36Z" | NONE | null | The open source Orange project (http://orange.biolab.si/) is a component-based data mining and machine learning suite that can be controlled through the visual Orange Canvas interface or (for experts) through the Python scripting interface. It has many advanced data mining algorithms, visualizations and is probably suited to be integrated with Pandas.
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There's general stuff about boolean indexing but this actually is not a pandas feature but rather a NumPy feature (or-ing arrays with |). Could definitely use a section in the docs about this
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pred.dat
which has only two lines of content
0.001
0.051
(this being the actual test case where this happens).
if I do
``````
labchunks = read_csv(fromvw, sep=" ",header=None,chunksize = 100000) #
predchunks= read_table( preddat ,header=None, names=["prediction"],chunksize = 100000 )
print labchunks
print predchunks
for labels,preds in izip(labchunks,preddat):
# print labels
# print predd
# currentrange = min( len(labels.ix[:,0]), len(predd.ix[:,0]))
# print preds
for lbl,prd in zip( labels.ix[:,"X.1"] ,preds.prediction):
fl.write(str(lbl) + "," +str(logistic(prd))+ "\n" )
fl.flush()
fl.close()```
``i get "Traceback (most recent call last):
File "./fixedpear.py", line 29, in <module>
for lbl,prd in zip( labels.ix[:,"X.1"] ,preds.prediction):
AttributeError: 'str' object has no attribute 'prediction'"``
which I don't understand, because it looks like somehow preds is actually the sequence of characters in the filename "pred.dat"
rather than a chunk of the contents!
``````
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} | 0 | "2012-03-15T02:05:48Z" | "2012-03-15T02:28:16Z" | "2012-03-15T02:26:50Z" | MEMBER | null | When excluding nuisance columns, block ref_items are not updated
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```
s = Series([], index=[])
s[-1]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/918 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/918/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/918/comments | https://api.github.com/repos/pandas-dev/pandas/issues/918/events | https://github.com/pandas-dev/pandas/pull/918 | 3,661,215 | MDExOlB1bGxSZXF1ZXN0OTg3NjI0 | 918 | Add sort_columns parameter to plot to allow unsorted plots | {
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I felt like the ordering I had defined in the DataFrame was a pretty decent proxy for how I wanted the subplots to be ordered, so I think this should be an option so I can over-ride the sorting and have the plot go in the direction specified.
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} | 7 | "2012-03-15T15:13:56Z" | "2013-06-06T21:07:08Z" | "2013-06-06T21:06:26Z" | MEMBER | null | Can be accomplished in a hackish way using apply, but a more structured approach would be nice
7/12/2012: Not sure what I was intending with this one
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https://api.github.com/repos/pandas-dev/pandas/issues/921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/921/events | https://github.com/pandas-dev/pandas/issues/921 | 3,670,621 | MDU6SXNzdWUzNjcwNjIx | 921 | ENH: specify header=['list', 'of', 'headers'] in DataFrame.write_csv() | {
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} | 1 | "2012-03-15T18:10:31Z" | "2012-06-25T16:24:48Z" | "2012-06-25T16:24:48Z" | NONE | null | As with R's write.table col.names which can take a list of column headers to output instead of the
headers.
here's a start, though I'm unsure about hwo it interacts with the index label stuff.
``` Patch
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 248a4e9..8c93488 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -13,7 +13,7 @@ labeling information
# pylint: disable=E1101,E1103
# pylint: disable=W0212,W0231,W0703,W0622
-
+import collections
from itertools import izip
from StringIO import StringIO
import csv
@@ -889,7 +889,9 @@ class DataFrame(NDFrame):
cols = self.columns
series = self._series
- if header:
+ if isinstance(header, collections.Iterable):
+ writer.writerow([h or '' for h in header])
+ elif header:
if index:
# should write something for index label
if index_label is None:
```
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} | 4 | "2012-03-15T18:44:05Z" | "2016-10-12T23:02:51Z" | "2014-02-18T20:26:57Z" | MEMBER | null | ```
In [35]: A
Out[35]:
Aattr3 Aattr1 Aattr2
Aidx1
37 Delta East hard
59 Epsilon East hard
96 Zeta East hard
21 Gamma West medium hard
2 Alpha West soft
10 Beta West soft
In [36]: B
Out[36]:
Bname Battr
Bidx1 Bidx2
37 20579 Alan 45.20
59 20693 Betty 4.40
20706 Charlie 9.95
37 20742 Dougal 16.60
96 20861 Emma 7.40
37 21167 Freddy 20.60
59 20947 Gareth 21.00
96 20536 Helen 9.65
37 21002 Ivan 43.80
21 21491 Jenny 6.90
20655 Keith 42.40
2 21083 Leonard 23.60
21391 Mandy 30.40
21381 Nicola 11.60
10 21416 Oswald 30.30
2 21392 Peter 44.40
21386 Quentin 5.40
In [15]: A.index.intersection(B.index)
Out[15]: Index([], dtype=object)
In [9]: pandas.merge(A, B, left_index=True, right_on=['Bidx1'])
...
KeyError: 'no item named Bidx1'
In [10]: pandas.merge(A, B, left_index=True, right_on=['Bidx1',
'Bidx2'])
...
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_be66c27-py2.7-
linux-x86_64.egg/pandas/tools/merge.pyc in
_validate_specification(self)
278 n = len(self.right_on)
279 if self.left_index:
--> 280 assert(len(self.right_on) ==
self.left.index.nlevels)
281 self.left_on = [None] * n
282 assert(len(self.right_on) == len(self.left_on))
AssertionError:
In [18]: pandas.merge(A, B)
...
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_be66c27-py2.7-
linux-x86_64.egg/pandas/core/groupby.pyc in
get_group_index(label_list, shape)
1494 return label_list[0]
1495
-> 1496 n = len(label_list[0])
1497 group_index = np.zeros(n, dtype=np.int64)
1498 mask = np.zeros(n, dtype=bool)
IndexError: list index out of range
```
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https://api.github.com/repos/pandas-dev/pandas/issues/923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/923/events | https://github.com/pandas-dev/pandas/issues/923 | 3,676,296 | MDU6SXNzdWUzNjc2Mjk2 | 923 | dropna able to accept a list/tuple for axis argument | {
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I sometimes work with square matricies that potentially could have a column (and row) of all na's
I find myself doing:
df.dropna(how='all',axis=0).dropna(how='all',axis=1)
so
df.dropna(how='all',axis=[0,1])
e.g.
_dropna = DataFrame.dropna
def revised_dropna(self, axis = None, **kwargs):
if isinstance(axis,(list,tuple)):
return _dropna(_dropna(self,axis=axis[0],**kwargs),axis=axis[1],**kwargs)
return _dropna(self,axis=axis,**kwargs)
DataFrame.dropna = revised_dropna
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} | 1 | "2012-03-16T01:17:54Z" | "2012-09-18T16:34:43Z" | "2012-09-18T16:34:30Z" | CONTRIBUTOR | null | rationale:
I sometimes work with square matricies that potentially could have a column (and row) of all na's
I find myself doing:
`df.dropna(how='all',axis=0).dropna(how='all',axis=1)`
so
`df.dropna(how='all',axis=[0,1])`
e.g.
```
_dropna = DataFrame.dropna
def revised_dropna(self, axis = None, **kwargs):
if isinstance(axis,(list,tuple)):
return _dropna(_dropna(self,axis=axis[0],**kwargs),axis=axis[1],**kwargs)
return _dropna(self,axis=axis,**kwargs)
DataFrame.dropna = revised_dropna
```
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} | 0 | "2012-03-16T14:22:36Z" | "2012-03-16T16:11:29Z" | "2012-03-16T16:11:29Z" | MEMBER | null | ```
test=PD.Series(index=PD.DateRange(DT.datetime(2012,3,15), DT.datetime(2012,3,16)), data=NP.ones(2))
test == test.shift(1)
```
vs
```
test2=PD.Series(index=PD.DateRange(DT.datetime(2012,3,15), DT.datetime(2012,3,16)), data=['a','a'])
test2 == test2.shift(1)
```
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} | 0 | "2012-03-16T15:10:35Z" | "2012-11-28T05:47:34Z" | "2012-11-28T05:47:34Z" | MEMBER | null | per
http://stackoverflow.com/questions/9695668/how-to-specify-dtype-when-using-pandas-read-csv-to-load-data-from-csv-files
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https://api.github.com/repos/pandas-dev/pandas/issues/927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/927/events | https://github.com/pandas-dev/pandas/issues/927 | 3,686,430 | MDU6SXNzdWUzNjg2NDMw | 927 | pip install borked... | {
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```
$ virtualenv /tmp/pandatest
$ source /tmp/pandatest/bin/activate
$ pip install pandas
$ python
Python 2.6.6 (r266:84292, Sep 15 2010, 15:52:39)
[GCC 4.4.5] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/tmp/pandatest/lib/python2.6/site-packages/pandas/__init__.py", line 10, in <module>
import pandas._tseries as lib
ImportError: /tmp/pandatest/lib/python2.6/site-packages/pandas/_tseries.so: undefined symbol: PyArray_IsIntegerScalar
```
It would be really cool if this worked :)
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} | 7 | "2012-03-16T21:29:14Z" | "2012-11-05T07:07:31Z" | "2012-11-01T15:17:40Z" | NONE | null | The `sort` method on DataFrame supports sorting by multiple columns, yet only supports sorting in one direction (ascending or descending). It would be great to specify this. Perhaps something like this: `df.sort(columns=['Name|descending', 'Age']` which would sort name first in descending order and then where names match use ascending order.
Also I see no mention of whether the sort is stable. If it is, this can be replicated by multiple sort calls.
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} | 0 | "2012-03-16T22:33:10Z" | "2016-10-12T23:02:51Z" | "2012-05-15T20:31:48Z" | MEMBER | null | More generalized version of fillna. Want to be able to do stuff like:
```
s.replace([np.nan, ''], 'Missing')
```
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} | 0 | "2012-03-16T22:55:39Z" | "2012-04-02T03:51:35Z" | "2012-04-02T03:51:35Z" | CONTRIBUTOR | null | Is there one already or is there any chance of getting a version.short_version like numpy/scipy or a version number for 'anal retentives' (see distutils.version.StrictVersion?). This obviously isn't high priority but it's nice.
<snip>
ValueError: invalid version number '0.7.2.dev-81dcf10'
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https://api.github.com/repos/pandas-dev/pandas/issues/931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/931/events | https://github.com/pandas-dev/pandas/issues/931 | 3,692,403 | MDU6SXNzdWUzNjkyNDAz | 931 | Series.kurt and DataFrame.kurt | {
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} | [] | closed | false | null | [] | null | 0 | "2012-03-17T02:42:47Z" | "2012-03-24T17:47:19Z" | "2012-03-24T17:47:19Z" | CONTRIBUTOR | null | like the Series.skew and DataFrame.skew methods, have Series.kurt and DataFrame.kurt
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https://api.github.com/repos/pandas-dev/pandas/issues/932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/932/events | https://github.com/pandas-dev/pandas/pull/932 | 3,692,438 | MDExOlB1bGxSZXF1ZXN0MTAwMDc4OA== | 932 | #931 Series.kurt and DataFrame.kurt | {
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} | [] | closed | false | null | [] | null | 0 | "2012-03-17T02:46:00Z" | "2014-07-17T09:32:14Z" | "2012-03-24T17:05:17Z" | CONTRIBUTOR | null | Series.kurt calls rolling_kurt and DataFrame.kurt aplies Series.kurt on only numeric data (slightly different from the nanops functional flow)
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https://api.github.com/repos/pandas-dev/pandas/issues/933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/933/events | https://github.com/pandas-dev/pandas/issues/933 | 3,693,175 | MDU6SXNzdWUzNjkzMTc1 | 933 | (series == x) doesn't always produce a boolean series: bug? | {
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} | 2 | "2012-03-17T07:08:37Z" | "2012-04-09T20:05:54Z" | "2012-04-09T20:05:54Z" | NONE | null | In [2]: a = pandas.Series(['a', nan, 'b'])
In [3]: a == 'a'
Out[3]:
0 True
1 NaN
2 False
# There is a NaN in Out[3]. This can't be the desired result because of the following error:
In [4]: b = pandas.Series(['a', 'b', 'c'])
In [5]: (a == 'a') & (b == 'a')
ValueError Traceback (most recent call last)
/home/dong/<ipython-input-5-89f377c08b91> in <module>()
----> 1 (a == 'a') & (b == 'a')
/home/dong/.virtualenv/src/pandas/pandas/core/series.pyc in wrapper(self, other)
143 if isinstance(other, Series):
144 name = _maybe_match_name(self, other)
--> 145 return Series(na_op(self.values, other.values),
146 index=self.index, name=name)
147 elif isinstance(other, DataFrame):
/home/dong/.virtualenv/src/pandas/pandas/core/series.pyc in na_op(x, y)
132
133 if isinstance(y, np.ndarray):
--> 134 result = lib.vec_binop(x, y, op)
135 else:
136 result = lib.scalar_binop(x, y, op)
/home/dong/.virtualenv/src/pandas/pandas/_tseries.so in pandas._tseries.vec_binop (pandas/src/tseries.c:9302)()
ValueError: Does not understand character buffer dtype format string ('?')
# BTW for series of dtype float there is no such problem:
In [6]: c = pandas.Series([1.0, nan, 3.0])
In [7]: c == 1
Out[7]:
0 True
1 False
2 False
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https://api.github.com/repos/pandas-dev/pandas/issues/934 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/934/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/934/comments | https://api.github.com/repos/pandas-dev/pandas/issues/934/events | https://github.com/pandas-dev/pandas/issues/934 | 3,697,802 | MDU6SXNzdWUzNjk3ODAy | 934 | ddof no longer passed for Series.std and Series.var | {
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} | 1 | "2012-03-18T02:01:11Z" | "2012-04-02T19:16:24Z" | "2012-04-02T19:15:29Z" | CONTRIBUTOR | null | As part of the following commit:
https://github.com/pydata/pandas/commit/fbb110247ea81e980bf9265cc1b0b26c8eb549be#L5L799
Series.std and Series.var no longer pass along ddof to the nanops.nanvar function.
Also since ddof defaults to 1 in pandas and 0 in numpy.
``` python
arr = np.arange(10)
s = Series(arr)
# False
np.std(s) == np.std(arr)
# True
np.std(s) == np.std(arr, ddof=1)
```
Which I don't know if it's a bug but it's non-intuitive.
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https://api.github.com/repos/pandas-dev/pandas/issues/935 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/935/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/935/comments | https://api.github.com/repos/pandas-dev/pandas/issues/935/events | https://github.com/pandas-dev/pandas/pull/935 | 3,698,757 | MDExOlB1bGxSZXF1ZXN0MTAwMjY3OQ== | 935 | scatter plot matrix | {
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} | 1 | "2012-03-18T07:32:32Z" | "2014-06-15T09:28:23Z" | "2012-04-10T04:10:58Z" | CONTRIBUTOR | null | I wanted an easy way to create a scatter plot matrix from a data frame. It doesn't have the labels along the diagonals like R's ipairs does, but I don't think that would be too much work if you guys preferred that. I saw there was a scatter_matrix function in tools/plotting.py, but I wasn't sure what that was for.
I made sure it displayed data with NA values correctly, but that's about as much testing I could think of.
Update: I didn't notice issue #857, I guess if this is already in the works then please ignore this.
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https://api.github.com/repos/pandas-dev/pandas/issues/936 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/936/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/936/comments | https://api.github.com/repos/pandas-dev/pandas/issues/936/events | https://github.com/pandas-dev/pandas/issues/936 | 3,702,724 | MDU6SXNzdWUzNzAyNzI0 | 936 | Add time-length windowing capability to moving statistics | {
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https://api.github.com/repos/pandas-dev/pandas/issues/940 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/940/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/940/comments | https://api.github.com/repos/pandas-dev/pandas/issues/940/events | https://github.com/pandas-dev/pandas/issues/940 | 3,707,028 | MDU6SXNzdWUzNzA3MDI4 | 940 | DataFrame.to_excel() fails for boolean values. | {
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--> 796 for val in row])
seems to be the problem here (see below).
Maybe there should be something like (bool(val) if instance(val, np.bool_) ?
In [14]: df = DataFrame([(1,2,3),(4,5,6)],columns = ['a','b','c'])
In [16]: df['d'] = df.b>df.c
## In [18]: df.to_excel('test.xlsx')
AttributeError Traceback (most recent call last)
/home/arthur/<i
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_12d511f-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in to_excel(self, excel_writer, sheet_name, na_rep, cols, header, index, index_label)
1032 self._helper_csvexcel(excel_writer, na_rep=na_rep, cols=cols,
1033 header=header, index=index,
-> 1034 index_label=index_label)
1035 if need_save:
1036 excel_writer.save()
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_12d511f-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _helper_csvexcel(self, writer, na_rep, cols, header, index, index_label)
933 row_fields.append(val)
934
--> 935 writer.writerow(row_fields)
936
937 def to_csv(self, path_or_buf, sep=",", na_rep='', cols=None,
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_12d511f-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in writerow(self, row, sheet_name)
759 'cur_sheet property')
760 if self.use_xlsx:
--> 761 self._writerow_xlsx(row, sheet_name)
762 else:
763 self._writerow_xls(row, sheet_name)
/usr/local/lib/python2.7/dist-packages/pandas-0.7.2.dev_12d511f-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _writerow_xlsx(self, row, sheet_name)
794
795 sheet.append([int(val) if isinstance(val, np.int64) else val
--> 796 for val in row])
797 row_idx += 1
798 self.sheets[sheet_name] = (sheet, row_idx)
/usr/local/lib/python2.7/dist-packages/openpyxl/writer/dump_worksheet.pyc in append(self, row)
265 else:
266 dtype = 'string'
--> 267 cell = self._string_builder.add(cell)
268
269 attributes['t'] = STYLES[dtype]['type']
/usr/local/lib/python2.7/dist-packages/openpyxl/writer/strings.pyc in add(self, key)
74 def add(self, key):
75
---> 76 key = key.strip()
77 try:
78 return self.dct[key]
AttributeError: 'numpy.bool_' object has no attribute 'strip'
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https://api.github.com/repos/pandas-dev/pandas/issues/941 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/941/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/941/comments | https://api.github.com/repos/pandas-dev/pandas/issues/941/events | https://github.com/pandas-dev/pandas/issues/941 | 3,714,134 | MDU6SXNzdWUzNzE0MTM0 | 941 | Pass more than column names to rows/cols args in pivot_table | {
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} | 0 | "2012-03-19T17:18:14Z" | "2012-03-19T17:43:44Z" | "2012-03-19T17:43:44Z" | MEMBER | null | Should be same argument types as groupby
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} | 1 | "2012-03-20T01:36:35Z" | "2012-03-20T02:27:28Z" | "2012-03-20T02:27:28Z" | NONE | null | For any DataFrame with numeric values, saying something like `df == 'cmp'`results in `PandasError: DataFrame constructor not properly called!`, which is pretty unintuitive. I'm not sure what behavior would be correct, but I do know that this error message is not helpful.
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Perhaps I'm just using read_csv incorrectly but I can't reproduce the issue using Std lib csv or by directly reading the file and parsing myself.
example:
In [31]: lehman = pd.read_csv('/Users/rroberts/fed_data/Lehman_Brothers_Holdings_Inc_1.csv', sep=',', skiprows=range(13), index_col=0, na_values='', names=['date', 'debt', 'mkt_cap', 'percent_mkt_cap', 'ST_OMO', 'TSLF', 'PDCF', 'DW'], parse_dates=True)
In [32]: lehman.DW
Out[32]:
date
Ticker NaN
Identified in Fed Documents as: NaN
Capital Raised From Home Governments NaN
Programs NaN
Country NaN
Industry NaN
Average Daily Balance
From 8/1/2007 to 4/30/2010 NaN
Peak Amount of Debt NaN
Peak Date NaN
Number of Days In Debt to the Fed DW
8/1/2007 $0.00
8/2/2007 $0.00
8/3/2007 $0.00
8/4/2007 $0.00
8/5/2007 $0.00
...
4/16/2010 $0.00
4/17/2010 $0.00
4/18/2010 $0.00
4/19/2010 $0.00
4/20/2010 $0.00
4/21/2010 $0.00
4/22/2010 $0.00
4/23/2010 $0.00
4/24/2010 $0.00
4/25/2010 $0.00
4/26/2010 $0.00
4/27/2010 $0.00
4/28/2010 $0.00
4/29/2010 $0.00
4/30/2010 $0.00
Name: DW, Length: 1014
######
# using file reading
######
In [33]: f = open('/Users/rroberts/fed_data/Lehman_Brothers_Holdings_Inc_1.csv', 'r')
In [34]: lines = f.readl
f.readline f.readlines
In [34]: lines = f.readlines()
In [35]: lines[:13]
Out[35]:
['Lehman Brothers Holdings Inc.,Lehman Brothers Holdings Inc.*,,,,,,\r\n',
'Ticker,LEHMQ US Equity,,,,,,\r\n',
'Includes Loans to:,"Lehman Brothers Holdings Inc., Lehman Brothers Holdings Inc., Lehman Brothers Holdings Inc. and Lehman Brothers Holdings Inc.",,,,,,\r\n',
'Identified in Fed Documents as:,"Lehman Brothers Inc., Lehman Brothers Inc., Lehman Brothers Inc. and LEHMAN BROTHERS BK FSB",,,,,,\r\n',
'Capital Raised From Home Governments,$0.00 ,,,,,,\r\n',
'Programs,"PDCF, TSLF, ST OMO, DW",,,,,,\r\n',
'Country,United States,,,,,,\r\n',
'Industry,Diversified Financial Services,,,,,,\r\n',
'"Average Daily Balance \n',
'From 8/1/2007 to 4/30/2010","$3,121.20 ",,,,,,\r\n',
'Peak Amount of Debt,"$46,023.00 ",,,,,,\r\n',
'Peak Date,9/15/2008,,,,,,\r\n',
'Number of Days In Debt to the Fed,195,Market Cap,Percent of Market Cap,ST OMO,TSLF,PDCF,DW\r\n']
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} | 4 | "2012-03-20T19:44:23Z" | "2012-04-02T03:54:49Z" | "2012-04-02T03:54:49Z" | NONE | null | I'm not sure if this is intended behaviour that I don't yet understand, or a bug.
I have a DataFrame df
A B C
0 -1.191583 1.290778 -0.445384
1 -1.275654 -0.716870 1.068261
2 -0.179524 -1.783951 2.539275
3 -0.149163 0.621901 -0.326219
4 0.055287 -0.057617 1.015610
5 -0.816133 -0.505380 -0.480527
6 0.531102 0.292489 0.362619
7 -0.467907 0.179168 -0.892605
I want to set some values by generating the correct slice, and then passing an array:
df[:4]['B'] = range(4)
but df remains unchanged. However, if I change the order of the slicing, it works:
df['B'][:4] = range(4)
df
A B C
0 -1.191583 0.000000 -0.445384
1 -1.275654 1.000000 1.068261
2 -0.179524 2.000000 2.539275
3 -0.149163 3.000000 -0.326219
4 0.055287 -0.057617 1.015610
5 -0.816133 -0.505380 -0.480527
6 0.531102 0.292489 0.362619
7 -0.467907 0.179168 -0.892605
Is this normal?
Thanks,
Roel
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} | 0 | "2012-03-20T20:58:22Z" | "2012-04-09T15:31:27Z" | "2012-04-09T15:31:27Z" | MEMBER | null | E.g. `df[date1:date2]` vs. `df.ix[date1:date2]`
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https://api.github.com/repos/pandas-dev/pandas/issues/947 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/947/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/947/comments | https://api.github.com/repos/pandas-dev/pandas/issues/947/events | https://github.com/pandas-dev/pandas/issues/947 | 3,750,322 | MDU6SXNzdWUzNzUwMzIy | 947 | Timeseries branch won't build with Python 3. | {
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} | [] | closed | false | null | [] | null | 1 | "2012-03-21T18:00:09Z" | "2012-03-21T18:12:10Z" | "2012-03-21T18:12:10Z" | NONE | null | I got the following error when trying to build the timeseries branch with python-3.2:
cythoning pandas/src/tseries.pyx to pandas/src/tseries.c
## Error compiling Cython file:
...
to use start ('S') or end ('E') of interval.
"""
cdef:
long retval
```
if not isinstance(relation, basestring) or len(relation) != 1:
^
```
---
pandas/src/datetime.pyx:961:42: undeclared name not builtin: basestring
building 'pandas._tseries' extension
gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/usr/local/lib/python3.2/site-packages/numpy/core/include -I/usr/local/include/python3.2m -c pandas/src/tseries.c -o build/temp.linux-x86_64-3.2/pandas/src/tseries.o
pandas/src/tseries.c:1:2: error: #error Do not use this file, it is the result of a failed Cython compilation.
Replacing all uses of "basestring" in tseries.pyx with "str" allows it to build. (I'm new to python, so I'm not sure whether this solution is appropriate.)
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} | 0 | "2012-03-21T20:45:38Z" | "2012-03-21T21:45:02Z" | "2012-03-21T21:45:02Z" | CONTRIBUTOR | null | In [4]: i = Index([1,2,3,4])
In [5]: i
Out[5]: Int64Index([1, 2, 3, 4])
In [6]: i == i
Out[6]: Int64Index([ 1, 1, 1, 1])
In [7]: i == 0
Out[7]: array([False, False, False, False], dtype=bool)
In [8]: i == 1
Out[8]: array([ True, False, False, False], dtype=bool)
In [9]: i == np.array([1,2,3,4])
Out[9]: array([ True, True, True, True], dtype=bool)
In [10]: i == Index([1,2,3,4])
Out[10]: Int64Index([ 1, 1, 1, 1])
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} | [] | closed | false | null | [] | null | 7 | "2012-03-21T21:52:54Z" | "2012-03-22T19:50:36Z" | "2012-03-22T19:50:36Z" | NONE | null | Thanks to Adam's quick fix, I am now able to build the timeseries branch with python-3.2, but when I try to load it, I get
ImportError: /usr/local/lib/python3.2/site-packages/pandas-0.7.2.dev-py3.2-linux-x86_64.egg/pandas/_tseries.cpython-32m.so: undefined symbol: PyString_FromString
I couldn't figure out why it's trying to use that symbol on Python 3.
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https://api.github.com/repos/pandas-dev/pandas/issues/950 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/950/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/950/comments | https://api.github.com/repos/pandas-dev/pandas/issues/950/events | https://github.com/pandas-dev/pandas/issues/950 | 3,762,687 | MDU6SXNzdWUzNzYyNjg3 | 950 | pip install does not check for openpyxl | {
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} | 5 | "2012-03-22T13:32:51Z" | "2012-10-11T23:49:54Z" | "2012-10-11T23:49:54Z" | CONTRIBUTOR | null | Installed the newest pandas (live on the edge! ;) via pip.
When trying to read an xlsx file I got the known import error.
openpyxl was just not installed.
This should be added as a dependy to setup.py, I guess?
Regards,
Michael
System: Mac OSX 10.7.3, pandas version 0.7.0 installed into EPD 32-bit via pip
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```
>>> excel = pandas.io.parsers.ExcelFile("BELA FM Screning Test data (2nd Batch)_KMA.xlsx")
```
This works
```
>>> bw = excel.parse("BW_clean")
```
but this fails
```
>>> bw = excel.parse("BW_clean",index_col=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/maye/Dropbox/DDocuments/BELA/apd/<ipython-input-20-ee78800ab5e1> in <module>()
----> 1 bw = excel.parse("BW_clean",index_col=0)
/Users/maye/Library/Python/2.7/site-packages/pandas/io/parsers.pyc in parse(self, sheetname, header, skiprows, index_col, parse_dates, date_parser, na_values, chunksize)
609 parse_dates=parse_dates,
610 date_parser=date_parser,
--> 611 na_values=na_values, chunksize=chunksize)
612 else:
613 return self._parse_xls(sheetname, header=header, skiprows=skiprows,
/Users/maye/Library/Python/2.7/site-packages/pandas/io/parsers.pyc in _parse_xlsx(self, sheetname, header, skiprows, index_col, parse_dates, date_parser, na_values, chunksize)
635 date_parser=date_parser,
636 skiprows=skiprows,
--> 637 chunksize=chunksize)
638
639 return parser.get_chunk()
/Users/maye/Library/Python/2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, delimiter, names, header, index_col, na_values, parse_dates, date_parser, chunksize, skiprows, skip_footer, converters, verbose, encoding)
249 self.data = f
250 self.columns = self._infer_columns()
--> 251 self.index_name = self._get_index_name()
252 self._first_chunk = True
253
/Users/maye/Library/Python/2.7/site-packages/pandas/io/parsers.pyc in _get_index_name(self)
394 elif np.isscalar(self.index_col):
395 index_name = columns.pop(self.index_col)
--> 396 if 'Unnamed' in index_name:
397 index_name = None
398 elif self.index_col is not None:
TypeError: argument of type 'NoneType' is not iterable
```
I then checked if maybe the excel file is broken by saving it as xls, but THEN it works:
```
>>> excel = pandas.io.parsers.ExcelFile("BELA FM Screning Test data (2nd Batch)_KMA.xls")
>>> bw = excel.parse("BW_clean",index_col=0)
Out[25]: Index([25.0, 15.0, 10.0, 5.0, 0.0], dtype=object)
```
Therefore I induce that it's a problem with how openpyxl is used.
Regards,
Michael
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} | [] | closed | false | null | [] | null | 5 | "2012-03-22T15:10:38Z" | "2014-06-16T10:09:49Z" | "2012-04-02T16:27:52Z" | CONTRIBUTOR | null | I added a simple function like R's read_fwf().
It also made sense to refactor read_csv() and read_table() to redirect to a new function _read() which takes the parser's class as input. I needed this for the FixedWidthFieldParser which needs to override the TextParser in order to provide a custom reader for the fields. Please review; the tests pass.
Also, I made the column spec ("widths")
- zero-based, and
- inclusive on both the from and to fields (e.g., a colspec of (1,2) has two characters)
I'm not sure that's the best; I think in practice a lot of column format specs start counting at one, but I haven't done a survey or anything, feel free to change it. Maybe we should support both.
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} | 2 | "2012-03-22T16:11:16Z" | "2012-04-09T20:06:23Z" | "2012-04-09T20:06:23Z" | NONE | null | this didn't fail before:
In [30]: a = DataFrame([[1,2,3],['b',None,'a'],[1,'b',9]])
In [31]: a
Out[31]:
0 1 2
0 1 2 3
1 b None a
2 1 b 9
## In [32]: a[a[1]=='a']
ValueError Traceback (most recent call last)
/Users/jorge/<ipython-input-32-3a0cb4ca7415> in <module>()
----> 1 a[a[1]=='a']
/Users/jorge/.python_unstable/lib/python2.7/site-packages/pandas-0.7.2-py2.7-macosx-10.7-intel.egg/pandas/core/frame.pyc in **getitem**(self, key)
1422
1423 # also raises Exception if object array with NA values
-> 1424 if com._is_bool_indexer(key):
1425 key = np.asarray(key, dtype=bool)
1426 return self._getitem_array(key)
/Users/jorge/.python_unstable/lib/python2.7/site-packages/pandas-0.7.2-py2.7-macosx-10.7-intel.egg/pandas/core/common.pyc in _is_bool_indexer(key)
321 if not lib.is_bool_array(key):
322 if isnull(key).any():
--> 323 raise ValueError('cannot index with vector containing '
324 'NA / NaN values')
325 return False
ValueError: cannot index with vector containing NA / NaN values
Note: With integer values the exception is not raised:
In [34]: a = DataFrame([[1,2,3],['b',None,'a'],[1,2,9]])
In [35]: a
Out[35]:
0 1 2
0 1 2 3
1 b NaN a
2 1 2 9
In [36]: a[a[1]=='a']
Out[36]:
Empty DataFrame
Columns: array([0, 1, 2])
Index: array([], dtype=int64)
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} | [] | closed | false | null | [] | null | 0 | "2012-03-22T18:24:34Z" | "2012-03-27T09:10:37Z" | "2012-03-27T09:10:36Z" | CONTRIBUTOR | null | DataFrame.plot() does not treat float index as numeric.
This is becasue `DataFrame.index.is_numeric()` returns `False` when the index holds floats.
`DataFrame.index.is_numeric()` checks on the dtype of the index to know if it holds numeric data. Apparently dtype of a float index is `object`.
example from the [mailing list](https://groups.google.com/forum/?fromgroups#!topic/pystatsmodels/M77RuKGvC6c)
``` python
In [89]: df = pd.DataFrame(np.random.randn(4,3), columns=['A', 'B',
'C'])
In [90]: df
Out[90]:
A B C
0 -1.209472 -2.016404 0.748993
1 0.568799 1.332006 1.228565
2 -1.105552 0.247344 0.379427
3 0.518771 1.155601 0.441952
In [91]: df.set_index('A')
Out[91]:
B C
A
-1.209472 -2.016404 0.748993
0.568799 1.332006 1.228565
-1.105552 0.247344 0.379427
0.518771 1.155601 0.441952
In [93]: df2 = df.set_index('A')
In [98]: df2.plot()
Out[98]: <matplotlib.axes.AxesSubplot object at 0x5a942d0>
In [100]: xlim()
Out[100]: (0.0, 3.0)
```
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} | 11 | "2012-03-22T18:28:21Z" | "2017-04-17T01:17:03Z" | "2013-09-28T19:09:48Z" | MEMBER | null | there are already a few things floating around but having something more structured / more options + in the pandas namespace would be nice
from an e-mail on the statsmodels mailing list
```
Here's a quick hack at it (not too dissimilar to Aman's code it looks
like)-- I should find a place in the library to put this:
def make_dummies(data, cat_variables):
result = data.drop(cat_variables, axis=1)
for variable in cat_variables:
dummies = _get_dummy_frame(data, variable)
result = result.join(dummies)
return result
def _get_dummy_frame(data, column):
from pandas import Factor
factor = Factor(data[column])
dummy_mat = np.eye(len(factor.levels)).take(factor.labels, axis=0)
dummy_cols = ['%s.%s' % (column, v) for v in factor.levels]
dummies = DataFrame(dummy_mat, index=data.index,
columns=dummy_cols)
return dummies
In [29]: df
Out[29]:
gender hand color height age
0 male right green 5.75 23
1 female right brown 5.42 27
2 female left green 5.58 31
3 male right brown 5.92 39
4 male right blue 5.83 33
In [30]: make_dummies(df, ['gender', 'hand', 'color']).T
Out[30]:
0 1 2 3 4
height 5.75 5.42 5.58 5.92 5.83
age 23 27 31 39 33
gender.female 0 1 1 0 0
gender.male 1 0 0 1 1
hand.left 0 0 1 0 0
hand.right 1 1 0 1 1
color.blue 0 0 0 0 1
color.brown 0 1 0 1 0
color.green 1 0 1 0 0
(BTW I read in that data using df = read_clipboard(sep=','))
```
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} | 1 | "2012-03-23T09:58:51Z" | "2012-04-10T04:55:58Z" | "2012-04-10T04:55:58Z" | CONTRIBUTOR | null | This plots black bars, as expected:
Series(randn(10)).plot(kind='bar',color='black')
While this plots bars in default blue color, ignoring color keyword:
DataFrame(randn(10,3)).plot(kind='bar',subplots=True,color='black')
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} | 1 | "2012-03-23T17:29:12Z" | "2014-05-29T05:47:58Z" | "2014-05-29T05:47:58Z" | CONTRIBUTOR | null | HDF5 is allows random access to large array data, enabling one to cherrypick fragments from enormous arrays that don't fit in memory. It would be good to have this supported in here https://github.com/wesm/pandas/blob/master/pandas/io/pytables.py.
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``` python
df = pandas.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : randn(8), 'D' : randn(8)})
df
grp = df.groupby('A')
tstgrp = grp.get_group('foo')
```
....
Now typing
tstgrp.reindex(
crashes the IPython kernel. I am actually guessing this is an IPython bug, but I know that Pandas does hook in to IPython so I figured I'd start here.
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} | 2 | "2012-03-23T21:40:45Z" | "2012-05-07T18:58:38Z" | "2012-05-07T18:57:51Z" | MEMBER | null | http://stackoverflow.com/questions/9794697/best-way-to-construct-a-pandas-dataframe-composed-of-different-chunks
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} | [] | closed | false | null | [] | null | 2 | "2012-03-26T14:48:51Z" | "2012-03-30T15:33:29Z" | "2012-03-30T15:33:29Z" | NONE | null | import numpy as np
from pandas import Series
a = [np.nan, 1]
b = [np.inf, 1]
print Series(a).sum() #1
print Series(b).sum() #2
print np.sum(a) #3
print np.sum(b) #4
I understand that pandas has made the design decision that sums with nans will drop nans (#1), but why is infinity dropped in the same way? I don't see why np.inf + 5 could reasonably be 5. numpy returns np.inf in this case.
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} | 1 | "2012-03-26T16:34:55Z" | "2012-05-29T00:20:09Z" | "2012-05-29T00:20:09Z" | NONE | null | Hello
I believe there is a problem when using DateRange.tz_normalize(tz) when the tz is pytz.utc (but not other pytz timezones):
I create a DateRange using a DateOffset that is Week(weekday=1), and then use the built-in DateRange methods tz_localize and tz_normalize to account for time zones. However, I get something weird if the time zone I use is pytz.UTC:
> > > dr = pandas.DateRange(start=start, end=end, offset=pandas.core.datetools.Week(weekday=1))
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 Week: kwds={'weekday': 1}, weekday=1>, tzinfo: None
> > > [2010-04-13 00:00:00, ..., 2012-03-06 00:00:00]
> > > length: 100
Now localize:
> > > drl = dr.tz_localize(pytz.timezone('US/Eastern'))
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 Week: kwds={'weekday': 1}, weekday=1>, tzinfo: US/Eastern
> > > [2010-04-13 00:00:00-04:00, ..., 2012-03-06 00:00:00-05:00]
> > > length: 100
And normalize:
> > > drl.tz_normalize(pytz.timezone('UTC'))
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 Week: kwds={'weekday': 1}, weekday=1>, tzinfo: UTC
> > > [2010-04-13 00:00:00+00:00, ..., 2012-03-06 00:00:00+00:00]
> > > length: 100
Bzzzt. The time zone info has changed, but the times have remained the same!
But if I use any other timezone, it works:
> > > drl.tz_normalize(pytz.timezone('Europe/London'))
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 Week: kwds={'weekday': 1}, weekday=1>, tzinfo: Europe/London
> > > [2010-04-13 05:00:00+01:00, ..., 2012-03-06 05:00:00+00:00]
> > > length: 100
Or even with 'Etc/UTC':
> > > drl.tz_normalize(pytz.timezone('Etc/UTC'))
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 Week: kwds={'weekday': 1}, weekday=1>, tzinfo: Etc/UTC
> > > [2010-04-13 04:00:00+00:00, ..., 2012-03-06 05:00:00+00:00]
> > > length: 100
Why does the DateRange.tz_localize() method fail with 'UTC' but work with other timezones (including 'Etc/UTC')? I looked at the code for DateRange.tz_localize(), and it's a very simple invocation of pytz tz.normalize().
But the pytz documentation (which isn't easy to parse, IMO) suggests that when using UTC normalize() alone is insufficient. For example:
> > > dt = datetime.datetime(2012, 3, 26, 12, 0)
> > > dtl = pytz.timezone('US/Eastern').localize(dt)
> > > datetime.datetime(2012, 3, 26, 12, 0, tzinfo=<DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>)
> > > print pytz.timezone('Etc/UTC').normalize(dtl) <- as expected
> > > 2012-03-26 16:00:00+00:00
> > > print pytz.timezone('UTC').normalize(dtl) <- bzzzt
> > > 2012-03-26 12:00:00+00:00
> > > print dtl.astimezone(pytz.timezone('UTC')) <- as expected
> > > 2012-03-26 16:00:00+00:00
> > > print pytz.timezone('UTC').normalize(dtl.astimezone(pytz.timezone('UTC'))) <- as expected
> > > 2012-03-26 16:00:00+00:00
Why this behavior of normalize should be limited to the canonical pytz UTC timezone (versus e.g. 'Etc/UTC'), I don't know, but it appears to be due to the fact that pytz.utc is a specialized singleton class that has different character than the other TZ classes.
I would suggest that DateRange.tz_normalize() should use either dt.astimezone(pytz.utc) or pytz.utc.normalize(dt.astimezone(pytz.utc)) at line 417.
Iain
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https://api.github.com/repos/pandas-dev/pandas/issues/970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/970/events | https://github.com/pandas-dev/pandas/pull/970 | 3,813,964 | MDExOlB1bGxSZXF1ZXN0MTA0OTE5MQ== | 970 | Allow read_csv to take URLs | {
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} | [] | closed | false | null | [] | null | 16 | "2012-03-26T18:32:52Z" | "2014-06-15T07:33:09Z" | "2012-04-02T20:04:19Z" | CONTRIBUTOR | null | This allows read_csv to take URLs. The tests are going to need to be modified after it's merged to point the the new repo URL or if you want to host a test file somewhere else. I also have no idea what the file:// path should be on non-posix systems, so this path might need some adjustment. Not sure. There's no test case for ftp, but I don't see why it wouldn't work the same as http.
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https://api.github.com/repos/pandas-dev/pandas/issues/971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/971/events | https://github.com/pandas-dev/pandas/pull/971 | 3,817,042 | MDExOlB1bGxSZXF1ZXN0MTA1MDUxNw== | 971 | Add an attach method to DataFrame | {
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} | [] | closed | false | null | [] | null | 14 | "2012-03-26T21:22:19Z" | "2014-06-15T10:46:45Z" | "2012-03-27T16:54:02Z" | CONTRIBUTOR | null | This is a method that injects the columns of a DataFrame into the calling namespace. As far as I can tell it works, even if it is hackish. However, the nosetests do not like the trickery and throw errors and failures. But, if you do
nosetests test_frame.py --pdb --pdb-failure
You can indeed see that the tests will pass from inside the debugger. I'm not really sure why. Does anyone have any ideas?
Am I missing anything about injecting variables into the calling local variables of the calling frame? I'm sure this is bad form, but it's functionality I've missed in Python.
Am I safe in assuming that column names are valid python variable names? If not, I may need to use a name validator.
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https://api.github.com/repos/pandas-dev/pandas/issues/972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/972/events | https://github.com/pandas-dev/pandas/issues/972 | 3,826,735 | MDU6SXNzdWUzODI2NzM1 | 972 | scoreateprecentile return wrong value | {
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```
In [1341]: a = np.random.rand(100)
In [1342]: b = pd.Series(a)
In [1343]: a[:10]
Out[1343]:
array([ 0.6131142 , 0.65266141, 0.24583156, 0.70179786, 0.33361506,
0.65042728, 0.70192276, 0.02727854, 0.65948894, 0.44326182])
In [1348]: scoreatpercentile(a,1)
Out[1348]: 0.010388922650144839 #correct value
In [1349]: scoreatpercentile(b,1)
Out[1349]: 0.65226593993834392 #incorrect value
In [1350]: scoreatpercentile(a,2)
Out[1350]: 0.011971896338709577 #correct value
In [1351]: scoreatpercentile(b,2)
Out[1351]: 0.25396815348880808 #incorrect value
```
I'm not sure if this is a pandas issue or scipy issue, and I am aware of the quantile method, but I still wonder if it is possible to fix that.
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} | 3 | "2012-03-27T13:37:39Z" | "2012-05-28T23:01:04Z" | "2012-05-28T23:01:04Z" | NONE | null | I have DataFrame with DateRange index, and i store it with HDFStore to h5.
But when I retreive my DataFrame, there is a problem with the indices cause I get this error:
Exception: Index values are not unique
Here are the commands that produce the exception, I didn't test every possible case so I keep the commands in between that I executed:
dffc=pandas.DataFrame(vpp.forecast.data, index=dr_year_900[:-1])
dffc[start_dt:stop_dt]
dffc.ix[start_dt:stop_dt]
dffc.drop(['Counter'], axis=1)
dffc=dffc.drop(['Counter'], axis=1)
dffc.ix[start_dt:stop_dt]
dffc['EFor'] = dffc['Power'] - dffc['Forecast']
dffc.ix[start_dt:stop_dt]
dffc = dffc.rename(columns={'Power':'PRef', 'Forecast':'PFor'})
dffc
dffc.ix[start_dt:stop_dt]
dfs = pandas.HDFStore('test.h5', 'w')
dfs.put('dffc', dffc)
dfs.close()
del(dffc)
dfs = pandas.HDFStore('test.h5', 'r')
dffc
dffc = dfs['dffc']
dffc
dffc.ix[start_dt:stop_dt]
==> here is the output:
In [49]: dffc.ix[start_dt:stop_dt]
Out[49]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 961 entries, 2010-01-01 00:00:00 to 2010-01-11 00:00:00
offset: <900 Seconds>
Data columns:
Counter 961 non-null values
Forecast 961 non-null values
Imbalance 961 non-null values
NegImbPrice 961 non-null values
PosImbPrice 961 non-null values
Power 961 non-null values
Price 961 non-null values
dtypes: float64(6), object(1)
In [50]: dffc.drop(['Counter'], axis=1)
Out[50]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 35040 entries, 2010-01-01 00:00:00 to 2010-12-31 23:45:00
offset: <900 Seconds>
Data columns:
Forecast 35040 non-null values
Imbalance 35040 non-null values
NegImbPrice 35040 non-null values
PosImbPrice 35040 non-null values
Power 35040 non-null values
Price 35040 non-null values
dtypes: float64(6)
In [51]: dffc=dffc.drop(['Counter'], axis=1)
In [52]: dffc.ix[start_dt:stop_dt]
Out[52]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 961 entries, 2010-01-01 00:00:00 to 2010-01-11 00:00:00
offset: <900 Seconds>
Data columns:
Forecast 961 non-null values
Imbalance 961 non-null values
NegImbPrice 961 non-null values
PosImbPrice 961 non-null values
Power 961 non-null values
Price 961 non-null values
dtypes: float64(6)
In [53]: dffc['EFor'] = dffc['Power'] - dffc['Forecast']
In [54]: dffc.ix[start_dt:stop_dt]
Out[54]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 961 entries, 2010-01-01 00:00:00 to 2010-01-11 00:00:00
offset: <900 Seconds>
Data columns:
Forecast 961 non-null values
Imbalance 961 non-null values
NegImbPrice 961 non-null values
PosImbPrice 961 non-null values
Power 961 non-null values
Price 961 non-null values
EFor 961 non-null values
dtypes: float64(7)
In [55]: dffc = dffc.rename(columns={'Power':'PRef', 'Forecast':'PFor'})
In [56]: dffc
Out[56]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 35040 entries, 2010-01-01 00:00:00 to 2010-12-31 23:45:00
offset: <900 Seconds>
Data columns:
PFor 35040 non-null values
Imbalance 35040 non-null values
NegImbPrice 35040 non-null values
PosImbPrice 35040 non-null values
PRef 35040 non-null values
Price 35040 non-null values
EFor 35040 non-null values
dtypes: float64(7)
In [57]: dffc.ix[start_dt:stop_dt]
Out[57]:
<class 'pandas.core.frame.DataFrame'>
DateRange: 961 entries, 2010-01-01 00:00:00 to 2010-01-11 00:00:00
offset: <900 Seconds>
Data columns:
PFor 961 non-null values
Imbalance 961 non-null values
NegImbPrice 961 non-null values
PosImbPrice 961 non-null values
PRef 961 non-null values
Price 961 non-null values
EFor 961 non-null values
dtypes: float64(7)
In [58]: dfs = pandas.HDFStore('test.h5', 'w')
In [59]: dfs.put('dffc', dffc)
In [60]: dfs.close()
In [61]: del(dffc)
In [62]: dfs = pandas.HDFStore('test.h5', 'r')
## In [63]: dffc
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
NameError: name 'dffc' is not defined
In [64]: dffc = dfs['dffc']
In [65]: dffc
Out[65]:
<class 'pandas.core.frame.DataFrame'>
Index: 35040 entries, 2010-01-01 00:00:00 to 2010-12-31 23:45:00
Data columns:
PFor 35040 non-null values
Imbalance 35040 non-null values
NegImbPrice 35040 non-null values
PosImbPrice 35040 non-null values
PRef 35040 non-null values
Price 35040 non-null values
EFor 35040 non-null values
dtypes: float64(7)
## In [66]: dffc.ix[start_dt:stop_dt]
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas-0.7.1-py2.7-win32.egg\pandas\core\indexing.py", line 35, in **getitem**
return self._getitem_axis(key, axis=0)
File "C:\Python27\lib\site-packages\pandas-0.7.1-py2.7-win32.egg\pandas\core\indexing.py", line 167, in _getitem_axis
return self._get_slice_axis(key, axis=axis)
File "C:\Python27\lib\site-packages\pandas-0.7.1-py2.7-win32.egg\pandas\core\indexing.py", line 345, in _get_slice_axis
i, j = labels.slice_locs(start, stop)
File "C:\Python27\lib\site-packages\pandas-0.7.1-py2.7-win32.egg\pandas\core\index.py", line 819, in slice_locs
beg_slice = self.get_loc(start)
File "C:\Python27\lib\site-packages\pandas-0.7.1-py2.7-win32.egg\pandas\core\index.py", line 499, in get_loc
return self._engine.get_loc(key)
File "engines.pyx", line 101, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2498)
File "engines.pyx", line 107, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2447)
Exception: Index values are not unique
How can I avoid this problem?
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https://api.github.com/repos/pandas-dev/pandas/issues/974 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/974/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/974/comments | https://api.github.com/repos/pandas-dev/pandas/issues/974/events | https://github.com/pandas-dev/pandas/pull/974 | 3,832,869 | MDExOlB1bGxSZXF1ZXN0MTA1NjkwNQ== | 974 | ENH: assure elements of list are of base dict class while converting list of dicts to a DataFrame | {
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} | [] | closed | false | null | [] | null | 1 | "2012-03-27T18:16:31Z" | "2014-07-16T08:04:49Z" | "2012-04-02T18:57:38Z" | CONTRIBUTOR | null | There might be a better and more generic solution for this -- I just didn't know it and didn't want just to file yet another bug report ;)
Cheers
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https://api.github.com/repos/pandas-dev/pandas/issues/975 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/975/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/975/comments | https://api.github.com/repos/pandas-dev/pandas/issues/975/events | https://github.com/pandas-dev/pandas/pull/975 | 3,841,289 | MDExOlB1bGxSZXF1ZXN0MTA1ODcyOQ== | 975 | BUG: Don't mutate DataFrame.from_records columns | {
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} | 1 | "2012-03-27T22:13:16Z" | "2014-06-25T23:44:11Z" | "2012-04-02T03:53:13Z" | CONTRIBUTOR | null | This also lets you use non-list types for the columns.
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} | 1 | "2012-03-28T14:49:20Z" | "2014-01-26T23:49:43Z" | "2014-01-26T23:49:43Z" | NONE | null | Would be nice to have the possibility to specify the date/number formatting when exporting to excel files, basically for internationalization pourposes (openpxl already incorporates it).
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} | 1 | "2012-03-29T03:21:03Z" | "2012-07-13T02:13:51Z" | "2012-07-13T02:13:51Z" | MEMBER | null | from mailing list:
```
To follow up:
I used timeit.Timer() to time this two different ways:
a) return a slice of a Panel using panel.ix[foo, bar, qux] (where foo,
bar, and qux are arrays)
b) i) iterate through the items of the panel that are in foo
ii) do a slice on each DataFrame with data.ix[bar, qux]
iii) save the slice
iv) reassemble the slices into a new, smaller Panel
v) return the new Panel
For the data I was using (10 items x 768432 major x 9 minor), the
results I got were:
t = timeit.Timer(a, setup=setup)
print min(t.repeat(repeat=3, number=10))
2.52914
t = timeit.Timer(b, setup=setup)
print min(t.repeat(repeat=3, number=10))
0.54757
This seems odd to me. Approach b is not only faster than a, but almost
5-fold faster. It appears that Panel.ix[foo, bar, qux] is doing
something even slower than the dumb approach embodied in B - either
that or I'm doing something horribly wrong! :-)
Iain
```
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} | 0 | "2012-03-29T17:58:19Z" | "2012-04-02T03:46:58Z" | "2012-04-02T03:46:58Z" | MEMBER | null | from mailing list
```
import pandas
import numpy as np
import pandas
print pandas.__version__
0.7.2.dev-12d511f
df1 = pandas.DataFrame(data=None, columns=['A','B','C'])
df1 = df1.set_index(['A'])
df2 = pandas.DataFrame(data=[[1,4,7],[2,5,8],[3,6,9]],
columns=['A','B','C'])
df2 = df2.set_index(['A'])
print df1.append(df2).index.names
[None]
```
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```
$ pip install pandas
...
...
running build_ext
building 'pandas._tseries' extension
creating build\temp.win-amd64-2.7
creating build\temp.win-amd64-2.7\Release
creating build\temp.win-amd64-2.7\Release\pandas
creating build\temp.win-amd64-2.7\Release\pandas\src
C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\BIN\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC:\Python27\lib\site-packages\numpy\core\include -IC:\Python27\include -IC:\Python27\PC /Tcpandas\src\tseries.c /Fobuild\temp.win-amd64-2.7\Release\pandas\src\tseries.obj
tseries.c
c:\python27\include\pyconfig.h(68) : fatal error C1083: Cannot open include file: 'io.h': No such file or directory
error: command '"C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\BIN\cl.exe"' failed with exit status 2
```
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} | 4 | "2012-03-30T12:10:59Z" | "2012-04-09T20:04:35Z" | "2012-04-09T20:04:35Z" | CONTRIBUTOR | null | Case in point:
``` python
In [14]: example = pandas.Series(["Activated", "Inhibited", np.nan], dtype="object")
In [15]: example == "Inhibited"
Out[15]:
0 False
1 True
2 NaN
```
This breaks boolean indexing:
``` python
example[example == "Inihibted"]
/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
392 # special handling of boolean data with NAs stored in object
393 # arrays. Since we can't represent NA with dtype=bool
--> 394 if _is_bool_indexer(key):
395 key = self._check_bool_indexer(key)
396 key = np.asarray(key, dtype=bool)
/usr/lib64/python2.7/site-packages/pandas/core/common.pyc in _is_bool_indexer(key)
321 if not lib.is_bool_array(key):
322 if isnull(key).any():
--> 323 raise ValueError('cannot index with vector containing '
324 'NA / NaN values')
325 return False
ValueError: cannot index with vector containing NA / NaN value
```
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} | 6 | "2012-03-30T14:40:52Z" | "2012-04-11T09:54:09Z" | "2012-04-10T17:28:45Z" | CONTRIBUTOR | null | On both axes, the generated Series on which the function has to work lack the "name" attribute (either the row name or th column name).
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} | 6 | "2012-04-01T21:41:08Z" | "2014-05-10T23:44:48Z" | "2012-04-02T03:23:34Z" | CONTRIBUTOR | null | I saw that @wesm was doing a bit of a plotting refactor. One thing that might be nice is to optionally return the Boxplot dict from matplotlib for df.boxplot. It makes it easier to do ex post styling. See: http://matplotlib.sourceforge.net/examples/pylab_examples/boxplot_demo2.html
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 3 | "2012-04-01T22:31:41Z" | "2013-09-20T17:13:16Z" | "2013-09-20T17:13:16Z" | MEMBER | null | Would be useful to me in my book writing, might get around to doing it
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} | 1 | "2012-04-02T13:46:19Z" | "2012-04-10T04:48:35Z" | "2012-04-10T04:48:34Z" | NONE | null | Hi Wes,
Just a quick question on NaN equality, I haven't specifically looked it up, but upgrading to 0.7.2 broke my code and it seems it's down to how testing for equality with NaN behaves:
```
a = pandas.Series(['abc', nan])
```
a == 'cba' outputs:
```
0 False
1 NaN
```
rather than all False. This seems to most definitely be a bug. Especially as
pandas.DataFrame(['abc', nan]) == 'cba' outputs the correct version:
```
0
0 False
1 False
```
Many thanks,
Marius
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} | 4 | "2012-04-03T13:08:29Z" | "2012-04-09T15:12:20Z" | "2012-04-09T15:11:55Z" | CONTRIBUTOR | null | From pystatsmodels ML "[pandas] slicing a series returns an array?"
Got it. It only happens if the series is longer than 200
``` python
x = pandas.Series(np.random.random(201), name='x')
x.reshape((-1,1))
<snip>
```
This is part of what I was talking about that Series aren't quite
array-like. Something like this
np.dot(x, np.random.random((1,10))
doesn't work, and I can't coerce x to be 2d. However, if X is less
than length 201, I sure can coerce it to be 2d, which is why I
couldn't reproduce
``` python
x = pandas.Series(np.random.random(199), name='x')
x = x.reshape((-1,1))
```
But I still can't dot
``` python
np.dot(x,np.random.random((1,10)))
<snip>
ValueError: The truth value of an array with more than one element is
ambiguous. Use a.any() or a.all()
```
I realize that trying to 'dot' might not make sense with a Series or
there's no intuitive interpretation to the output in terms of the
Series that I can think of offhand. Just an example of the 1d vs 2d
issue. Mainly, I guess I'd expect a series to be able to pass through
an atleast_2d check.
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Handle lower case time rules.
Parsing tuples e.g., ('Min', 5) as freq
Interval supports aliasing for frequency names
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} | 1 | "2012-04-03T22:09:18Z" | "2012-04-07T21:20:32Z" | "2012-04-07T21:20:32Z" | NONE | null | In the example below, column "name" of groupby apply method has the same content as column "year". A similar column with a copy of the "name" values will remain untouched (as expected).
```
import pandas
import StringIO
csv = """"year","name","percent","sex"
1880,"John",0.081541,"boy"
1880,"William",0.080511,"boy"
1880,"James",0.050057,"boy"
1881,"Charles",0.045167,"boy"
1881,"George",0.043292,"boy"
"""
names = pandas.read_csv(StringIO.StringIO(csv))
names['name2'] = names['name']
grouped = names.groupby('year')
print 'Pandas version', pandas.__version__
print names
print grouped.apply(lambda x:x)
```
OUTPUT:
```
Pandas version 0.7.2
year name percent sex name2
0 1880 John 0.081541 boy John
1 1880 William 0.080511 boy William
2 1880 James 0.050057 boy James
3 1881 Charles 0.045167 boy Charles
4 1881 George 0.043292 boy George
year name percent sex name2
0 1880 1880 0.081541 boy John
1 1880 1880 0.080511 boy William
2 1880 1880 0.050057 boy James
3 1881 1881 0.045167 boy Charles
4 1881 1881 0.043292 boy George
```
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} | 1 | "2012-04-04T15:18:13Z" | "2012-04-10T19:23:24Z" | "2012-04-10T19:23:24Z" | CONTRIBUTOR | null | I expected pandas to know that `None` should be ignored as a missing value in the following simple example:
```
import pandas
import numpy
# works fine
df = pandas.DataFrame({"col1":[2,5.0,123,numpy.nan], "col2":[1,2,3,4]})
print df.sub(3)
# fails -- tries to do the subtraction with None
df = pandas.DataFrame({"col1":[2,5.0,123,None], "col2":[1,2,3,4]})
print df.sub(3)
```
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```
In [7]: Series(date_range('1/1/2000', '12/30/2000 23:00:00', freq='BM'))
Out[7]:
0 2000-01-31 00:00:00
1 2000-02-29 00:00:00
2 2000-03-31 00:00:00
3 2000-04-28 00:00:00
4 2000-05-31 00:00:00
5 2000-06-30 00:00:00
6 2000-07-31 00:00:00
7 2000-08-31 00:00:00
8 2000-09-29 00:00:00
9 2000-10-31 00:00:00
10 2000-11-30 00:00:00
11 2000-12-29 00:00:00
In [8]: Series(date_range('1/1/2000', '12/30/2000 23:00:00', freq='BM')).dtype
Out[8]: dtype('datetime64[us]')
In [9]: Series(date_range('1/1/2000', '12/30/2000 23:00:00', freq='BM')).reindex(range(20)
...: )
Out[9]:
0 9.492768e+14
1 9.517824e+14
2 9.544608e+14
3 9.568800e+14
4 9.597312e+14
5 9.623232e+14
6 9.650016e+14
7 9.676800e+14
8 9.701856e+14
9 9.729504e+14
10 9.755424e+14
11 9.780480e+14
12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
17 NaN
18 NaN
19 NaN
```
similarly:
```
In [11]: DataFrame({'A' : rng})
Out[11]:
A
0 949276800000000
1 951782400000000
2 954460800000000
3 956880000000000
4 959731200000000
5 962323200000000
6 965001600000000
7 967680000000000
8 970185600000000
9 972950400000000
10 975542400000000
11 978048000000000
In [12]: DataFrame({'A' : rng}).dtypes
Out[12]: A int64
```
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} | 5 | "2012-04-05T01:12:41Z" | "2012-04-13T01:36:14Z" | "2012-04-13T01:36:14Z" | CONTRIBUTOR | null | It would be really nice to have time rules that can handle intraday data (http://pandas.sourceforge.net/timeseries.html#time-rules). This is slightly complicated, because you want it to be flexible enough to handle both raw tick data (which might be stamped to the millisecond or lower) and sampled times (e.g. 1-minute to 1-hour).
At a minimum, I would suggesting having N@SECONDS, where N can be any number of specified seconds. Another thing to consider is having a start time parameter for the sampling (e.g. every 60 seconds starting at 0:00, so that it doesn't simply anchor to the first observation).
Another really useful intraday time rule would take a list of times (with time zones) and return sampled data at those times (e.g. market open and close, or specific news announcement times).
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} | 1 | "2012-04-05T04:27:22Z" | "2012-04-13T00:20:45Z" | "2012-04-13T00:20:45Z" | MEMBER | null | May have to wait for NumPy 1.7?
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} | 2 | "2012-04-05T18:32:58Z" | "2019-10-09T07:01:35Z" | "2012-11-29T20:32:13Z" | NONE | null | The col_space parameter in the to_html and to_string functions for dataframe doesn't seem to affect the output. Here's an example using to_html(). Note that the HTML code is identical across the two function calls.
Thanks,
Tej
output.to_html()
<table border="1">
<thead>
<tr>
<th></th>
<th>nearby = 1 </th>
<th>nearby = 2 </th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>CM_CL</strong></td>
<td> K2</td>
<td> M2</td>
</tr>
<tr>
<td><strong>FI_TY</strong></td>
<td> M2</td>
<td> NaN</td>
</tr>
</tbody>
</table>
output.to_html(col_space = 2000)
<table border="1">
<thead>
<tr>
<th></th>
<th>nearby = 1 </th>
<th>nearby = 2 </th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>CM_CL</strong></td>
<td> K2</td>
<td> M2</td>
</tr>
<tr>
<td><strong>FI_TY</strong></td>
<td> M2</td>
<td> NaN</td>
</tr>
</tbody>
</table>
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