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https://api.github.com/repos/pandas-dev/pandas/issues/1702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1702/events | https://github.com/pandas-dev/pandas/issues/1702 | 5,909,978 | MDU6SXNzdWU1OTA5OTc4 | 1,702 | concat DataFrame along timeseries indexes | {
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} | 2 | "2012-07-30T03:01:24Z" | "2012-08-10T16:03:00Z" | "2012-08-10T16:03:00Z" | NONE | null | Please see: http://stackoverflow.com/questions/11714768/concat-pandas-dataframe-along-timeseries-indexes
NumPy v1.6.3
Pandas v0.8.1
I have two largish (snippets provided) pandas DateFrames with unequal dates as indexes that I wish to concat into one:
```
NAB.AX CBA.AX
Close Volume Close Volume
Date Date
2009-06-05 36.51 4962900 2009-06-08 21.95 0
2009-06-04 36.79 5528800 2009-06-05 21.95 8917000
2009-06-03 36.80 5116500 2009-06-04 22.21 18723600
2009-06-02 36.33 5303700 2009-06-03 23.11 11643800
2009-06-01 36.16 5625500 2009-06-02 22.80 14249900
2009-05-29 35.14 13038600 --AND-- 2009-06-01 22.52 11687200
2009-05-28 33.95 7917600 2009-05-29 22.02 22350700
2009-05-27 35.13 4701100 2009-05-28 21.63 9679800
2009-05-26 35.45 4572700 2009-05-27 21.74 9338200
2009-05-25 34.80 3652500 2009-05-26 21.64 8502900
```
Problem is, if I run this:
```
keys = ['CBA.AX','NAB.AX']
mv = pandas.concat([data['CBA.AX'][650:660],data['NAB.AX'][650:660]], axis=1, keys=stocks,)
```
the following DateFrame is produced:
```
CBA.AX NAB.AX
Close Volume Close Volume
Date
2200-08-16 04:24:21.460041 NaN NaN NaN NaN
2203-05-13 04:24:21.460041 NaN NaN NaN NaN
2206-02-06 04:24:21.460041 NaN NaN NaN NaN
2208-11-02 04:24:21.460041 NaN NaN NaN NaN
2211-07-30 04:24:21.460041 NaN NaN NaN NaN
2219-10-16 04:24:21.460041 NaN NaN NaN NaN
2222-07-12 04:24:21.460041 NaN NaN NaN NaN
2225-04-07 04:24:21.460041 NaN NaN NaN NaN
2228-01-02 04:24:21.460041 NaN NaN NaN NaN
2230-09-28 04:24:21.460041 NaN NaN NaN NaN
2238-12-15 04:24:21.460041 NaN NaN NaN NaN
```
Does anybody have any idea why this might be the case?
On another point: is there any python libraries around that pull data from yahoo and normalise it?
```
For reference:
data = {
'CBA.AX': <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2313 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
Data columns:
Close 2313 non-null values
Volume 2313 non-null values
dtypes: float64(1), int64(1),
'NAB.AX': <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2329 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
Data columns:
Close 2329 non-null values
Volume 2329 non-null values
dtypes: float64(1), int64(1)
}
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1703/events | https://github.com/pandas-dev/pandas/issues/1703 | 5,914,440 | MDU6SXNzdWU1OTE0NDQw | 1,703 | Incorrect result when using to_datetime() on PeriodIndex | {
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} | 0 | "2012-07-30T10:15:27Z" | "2012-08-12T20:53:07Z" | "2012-08-12T20:53:07Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
index = pd.period_range('1/1/2012',periods=4,freq='D')
index.to_datetime()
```
returns incorrectly:
```
<class 'pandas.tseries.index.DatetimeIndex'>
[1970-01-01 00:00:00.000015340, ..., 1970-01-01 00:00:00.000015343]
Length: 4, Freq: None, Timezone: None
```
The correct result is obtained though with to_timestamp() instead of to_datetime():
```
index.to_timestamp()
```
returns correctly:
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-04 00:00:00]
Length: 4, Freq: D, Timezone: None
```
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```
import pandas as pd
import datetime as dt
tuples = [(dt.datetime(2012,1,1),1),(dt.datetime(2012,1,1),2),(dt.datetime(2012,1,2),1),(dt.datetime(2012,1,2),2)]
index = pd.MultiIndex.from_tuples(tuples)
index.get_level_values(level=0)
```
incorrectly returns:
```
array([1970-01-16 224:00:00, 1970-01-16 224:00:00, 1970-01-16 248:00:00,
1970-01-16 248:00:00], dtype=datetime64[ns])
```
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} | 0 | "2012-07-30T10:29:53Z" | "2012-08-12T20:18:39Z" | "2012-08-12T20:18:39Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
index = pd.date_range('1/1/2012',periods=4,freq='12H')
index_as_arrays = [index.to_period(freq='D'), index.hour]
pd.Series([0, 1, 2, 3], index_as_arrays)
```
incorrectly returns:
```
15340 0 0
12 1
15341 0 2
12 3
```
The correct result is obtained though when explicitly building the MultiIndex:
```
index_as_multi_index = pd.MultiIndex.from_tuples(zip(*index_as_arrays))
pd.Series([0, 1, 2, 3], index_as_multi_index)
```
correctly returns:
```
01-Jan-2012 0 0
12 1
02-Jan-2012 0 2
12 3
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1706/events | https://github.com/pandas-dev/pandas/issues/1706 | 5,916,760 | MDU6SXNzdWU1OTE2NzYw | 1,706 | encode/decode method for string module (like in np.char.encode ) | {
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} | 0 | "2012-07-30T12:49:57Z" | "2012-08-12T01:48:40Z" | "2012-08-12T01:48:40Z" | NONE | null | This is already in included in numpy but for convenience it would be nice to have this in pandas.
```
series.str.decode('utf-8')
```
is nicer than for example
```
series = Series(['a','b','ä'])
Series(np.char.decode(series.values.astype('|S255'), 'utf-8'))
```
I find myself having to decode and encode a lot depending on what other libraries accept/return.
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} | 1 | "2012-07-30T19:55:06Z" | "2012-08-12T01:31:59Z" | "2012-08-12T01:31:39Z" | NONE | null | With pandas 0.8.1 (and pytables 2.3.1), trying to save an empty Series or DataFrame when using HDFStore results in an exception after some (but not all) data has been written to the hdf5 file.
``` python
from pandas import DataFrame, Series, HDFStore
# These are all empty
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
store = HDFStore('myfile.h5')
# These all fail
try:
store['s0'] = s0
except ValueError:
print 'Failed to write s0'
try:
store['s1'] = s1
except ValueError:
print 'Failed to write s1'
try:
store['df0'] = df0
except ValueError:
print 'Failed to write df0'
try:
store['df1'] = df1
except ValueError:
print 'Failed to write df1'
try:
store['df2'] = df2
except ValueError:
print 'Failed to write df2'
```
Here is the traceback:
``` python
ValueError Traceback (most recent call last)
/usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where)
176 else:
177 filename = fname
--> 178 __builtin__.execfile(filename, *where)
/home/erikw/pandas_hdf5_fail.py in <module>()
30
31 try:
---> 32 store['df2'] = df2
33 except ValueError:
34 print 'Failed to write df2'
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in __setitem__(self, key, value)
184
185 def __setitem__(self, key, value):
--> 186 self.put(key, value)
187
188 def __contains__(self, key):
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in put(self, key, value, table, append, compression)
341 self._write_to_group(key, value, table=table, append=append,
--> 342 comp=compression)
343
344 def _get_handler(self, op, kind):
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in _write_to_group(self, key, value, table, append, comp)
408 wrapper = lambda value: handler(group, value)
409
--> 410 wrapper(value)
411 group._v_attrs.pandas_type = kind
412
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in <lambda>(value)
406
407 handler = self._get_handler(op='write', kind=kind)
--> 408 wrapper = lambda value: handler(group, value)
409
410 wrapper(value)
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in _write_frame(self, group, df)
489
490 def _write_frame(self, group, df):
--> 491 self._write_block_manager(group, df._data)
492
493 def _read_frame(self, group, where=None):
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in _write_block_manager(self, group, data)
500 group._v_attrs.ndim = data.ndim
501 for i, ax in enumerate(data.axes):
--> 502 self._write_index(group, 'axis%d' % i, ax)
503
504 # Supporting mixed-type DataFrame objects...nontrivial
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in _write_index(self, group, key, index)
571 else:
572 if len(index) == 0:
--> 573 raise ValueError('Can not write empty structure, '
574 'axis length was 0')
575
ValueError: Can not write empty structure, axis length was 0
```
And here I show an issue that arises from only some of the data being written:
``` python
In [6]: store.keys()
Out[6]: ['s1', 's0', 'df1', 'df0', 'df2']
In [7]: store['df0']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-b5d10da56de7> in <module>()
----> 1 store['df0']
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in __getitem__(self, key)
181
182 def __getitem__(self, key):
--> 183 return self.get(key)
184
185 def __setitem__(self, key, value):
/usr/lib/pymodules/python2.7/pandas/io/pytables.pyc in get(self, key)
283 return self._read_group(group)
284 except (exc_type, AttributeError):
--> 285 raise KeyError('No object named %s in the file' % key)
286
287 def select(self, key, where=None):
KeyError: 'No object named df0 in the file'
```
Oh, and just for the record, all tests in "io/tests/test_pytables.py" succeed for me.
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https://api.github.com/repos/pandas-dev/pandas/issues/1708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1708/events | https://github.com/pandas-dev/pandas/issues/1708 | 5,928,761 | MDU6SXNzdWU1OTI4NzYx | 1,708 | Unexpected result with union, intersection and diff on Index objects | {
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} | 1 | "2012-07-30T20:33:54Z" | "2012-08-10T16:55:17Z" | "2012-08-10T16:55:00Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
import datetime as dt
index_1 = pd.DatetimeIndex([dt.datetime(2012,1,1,0), dt.datetime(2012,1,1,12),
dt.datetime(2012,1,2,0), dt.datetime(2012,1,2,12)])
index_2 = index_1 + pd.DateOffset(hours=1)
index_1 & index_2
```
correctly returns:
```
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: None, Timezone: None
```
But when building the same Index objects by specifying their frequency:
```
index_1 = pd.date_range('1/1/2012', periods=4, freq='12H')
index_2 = index_1 + pd.DateOffset(hours=1)
index_1 & index_2
```
unexpectedly results in:
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 12:00:00, ..., 2012-01-02 12:00:00]
Length: 3, Freq: 12H, Timezone: None
```
The same issue occurs when directly calling the intersection() method and similar unexpected results occur for union (|, +) and diff (-) operators.
For information, combining append() and order() methods as an alternative for the union operators does give the correct result, independently how index_1 and index_2 are built:
```
index_1 = pd.date_range('1/1/2012', periods=4, freq='12H')
index_2 = index_1 + pd.DateOffset(hours=1)
index_1.append(index_2).order()
```
correctly results in:
```
[2012-01-01 00:00:00, ..., 2012-01-02 13:00:00]
Length: 8, Freq: None, Timezone: None
```
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} | 2 | "2012-07-30T21:07:09Z" | "2012-10-21T18:08:42Z" | "2012-10-21T18:08:42Z" | CONTRIBUTOR | null | I just noticed the very nice radviz graphing feature added to pandas 0.8.1 (http://pandas.pydata.org/pandas-docs/dev/visualization.html#scatter-plot-matrix). Most of the graphs in the pandas documentation use randomly generated graphs, however the radviz examples uses the iris data set. The iris dataset, however, is not included as part of the pandas package to a new user cannot immediately reproduce, and play with, the example graph.
Would it be possible to include the iris dataset as part of the pandas package? alternatively, could the the example use a dataset that is hosted somewhere online?
best,
zach cp
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} | [] | closed | false | null | [] | null | 1 | "2012-07-30T21:32:01Z" | "2012-08-19T13:49:15Z" | "2012-08-19T13:49:15Z" | CONTRIBUTOR | null | This is a followup question to issue 1527 (https://github.com/pydata/pandas/issues/1527) which dealt with the ability to plot two column values against one another - which was added to pandas 0.8.1
Is there a way that each x-y position can be represented as points rather than as a line? For example the following will generate a squiggly line where points would be more useful:
from pandas import DataFrame
df = DataFrame(np.random.randn(100, 2), columns=['a', 'b']
df.plot(x='a',y='b')
thanks,
zach cp
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} | 3 | "2012-07-31T12:32:20Z" | "2012-08-12T18:48:09Z" | "2012-08-12T18:48:09Z" | NONE | null | E.g.:
``` python
pandas.Series(randn(10)).plot(color='red')
```
Whose output is:
This used to work correctly in pandas 0.7.2
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s.get('f')
nan
however, when 'f' is not a key of a Series, the get function returns None, as in:
s.get('f') is None
True
A workaround is to use get(..., NaN), as in:
s.get('f', NaN)
nan
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} | 15 | "2012-07-31T16:34:30Z" | "2017-05-04T13:35:39Z" | "2017-05-04T13:35:39Z" | NONE | null | When trying to insert/append a subclass (or composition) of a pandas Series into a DataFrame, any and all of the 'extra' functions that come with my subclass (or composition) are stripped and a Series is created:
``` python
In [7]: df = read_csv('some/data/from/file.csv')
In [8]: sp = SpatialSeries(df.the_geom) # SpatialSeries is subclass, the_geom is spatial location (WKT)
In [9]: type(sp)
Out[9]: spseries.SpatialSeries
In [10]: type(df)
Out[10]: pandas.core.frame.DataFrame
In [11]: df['geoms'] = sp
In [12]: type(df['geoms'])
Out[12]: pandas.core.series.Series
```
I suspect that for the most part, this kind of behaviour is useful, however, I need the extra functions and classes associated with SpatialSeries, and I'd rather not have to subclass DataFrame to create a special DataFrame that allows this. It looks like the culprit is here in frame.py at lines 1761-1772:
``` python
def _set_item(self, key, value):
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrame's index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrame's index to
ensure homogeneity.
"""
value = self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
```
I particular, I'm looking at `value = self._sanitize_column(key, value)`, which appears to use `np.asarray(value)` before it returns the input array (even if the input column is a Series). Is there any way to avoid this behaviour? Or alternatively, a better way to implement this so that useful subclasses can be used within a DataFrame? I hope I'm not missing something simple/vital here?
FYI:
``` python
In [13]: pandas.__version__
Out[13]: '0.8.0b1'
```
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} | 5 | "2012-07-31T18:38:20Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:16:29Z" | CONTRIBUTOR | null | From [stackoverflow](http://stackoverflow.com/questions/11707586/python-pandas-widen-output-display)
```
Python/IDLE 2.7.3
Pandas 0.8.1
Notepad++ 6.1.4 (UNICODE)
Windows Vista SP2
```
``` python
pandas.util.terminal.get_terminal_size()
```
Fails to detect the real IDLE display size and returns default (80, 25)
workaround is to use `pandas.set_printoptions(max_rows=200, max_columns=10)`
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} | 2 | "2012-07-31T23:46:26Z" | "2013-08-07T16:37:04Z" | "2013-08-07T16:37:04Z" | CONTRIBUTOR | null | Hi,
I've encountered some (seemingly) inconsistent behavior in assigning values to Panels. Here are some examples
``` python
In [2]: aapl = DataReader('AAPL','yahoo')
p = Panel({'stock':aapl})
print p['stock'][:5]
Out[2]: Open High Low Close Volume Adj Close
Date
2011-08-01 00:00:00 397.78 399.50 392.37 396.75 21887000 396.75
2011-08-02 00:00:00 397.65 397.90 388.35 388.91 22840700 388.91
2011-08-03 00:00:00 390.98 393.55 382.24 392.57 26161000 392.57
2011-08-04 00:00:00 389.41 391.32 377.35 377.37 31121700 377.37
2011-08-05 00:00:00 380.44 383.50 362.57 373.62 43021100 373.62
```
The above is exactly what we expect.
However, assigning a new DataFrame to the Panel drops any new columns:
``` python
In [3]: goog = DataReader('GOOG','yahoo')
goog['test'] = 1
print goog[:5]
Out[3]: Open High Low Close Volume Adj Close test
Date
2011-08-01 00:00:00 611.22 615.50 599.18 606.77 3966100 606.77 1
2011-08-02 00:00:00 606.00 609.67 591.57 592.40 3200600 592.40 1
2011-08-03 00:00:00 594.27 603.00 583.63 601.17 3825700 601.17 1
2011-08-04 00:00:00 594.50 598.85 577.47 577.52 4914600 577.52 1
2011-08-05 00:00:00 582.54 590.00 562.00 579.04 5929100 579.04 1
In [4]: p['stock'] = goog
print p['stock'][:5]
Out[4]: Open High Low Close Volume Adj Close
Date
2011-08-01 00:00:00 611.22 615.50 599.18 606.77 3966100 606.77
2011-08-02 00:00:00 606.00 609.67 591.57 592.40 3200600 592.40
2011-08-03 00:00:00 594.27 603.00 583.63 601.17 3825700 601.17
2011-08-04 00:00:00 594.50 598.85 577.47 577.52 4914600 577.52
2011-08-05 00:00:00 582.54 590.00 562.00 579.04 5929100 579.04
```
Direct assignment of a column works:
``` python
In [5]: for i in p.items:
p[i]['test'] = 1
print p['stock'][:5]
Out[5]: Open High Low Close Volume Adj Close test
Date
2011-08-01 00:00:00 611.22 615.50 599.18 606.77 3966100 606.77 1
2011-08-02 00:00:00 606.00 609.67 591.57 592.40 3200600 592.40 1
2011-08-03 00:00:00 594.27 603.00 583.63 601.17 3825700 601.17 1
2011-08-04 00:00:00 594.50 598.85 577.47 577.52 4914600 577.52 1
2011-08-05 00:00:00 582.54 590.00 562.00 579.04 5929100 579.04 1
```
But we lose the new column again if we try to assign by item:
``` python
In [6]: for i in p.items:
p[i]['test'] = 1
for i in p.items:
p[i] = goog
print p['stock'][:5]
Out[6]: Open High Low Close Volume Adj Close
Date
2011-08-01 00:00:00 611.22 615.50 599.18 606.77 3966100 606.77
2011-08-02 00:00:00 606.00 609.67 591.57 592.40 3200600 592.40
2011-08-03 00:00:00 594.27 603.00 583.63 601.17 3825700 601.17
2011-08-04 00:00:00 594.50 598.85 577.47 577.52 4914600 577.52
2011-08-05 00:00:00 582.54 590.00 562.00 579.04 5929100 579.04
```
The above behavior seems to stem from `Panel.__setitem__`'s reindexing of the input value by the Panel's major and minor axes, while `Panel.set_value` extends the axes of the underlying DataFrames, but doesn't update the Panel's axes.
This occurs on pandas-0.8.1-py2.7-linux-x86_64
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} | 1 | "2012-08-01T07:26:04Z" | "2014-06-23T18:10:26Z" | "2012-08-12T19:14:11Z" | NONE | null | ```
In [1]: import pandas
In [2]: from pandas.tools.plotting import scatter_plot
In [7]: df = pandas.DataFrame(np.random.randn(5,2), columns=['a','b'])
In [8]: df
Out[8]:
a b
0 0.032454 0.057020
1 -0.292223 -0.265303
2 -0.118943 1.315302
3 -2.754867 0.827289
4 0.497206 -1.360892
In [9]: df['c'] = [True, True, False, True, False]
In [10]: df
Out[10]:
a b c
0 0.032454 0.057020 True
1 -0.292223 -0.265303 True
2 -0.118943 1.315302 False
3 -2.754867 0.827289 True
4 0.497206 -1.360892 False
In [11]: scatter_plot(df, "a", "b", by="c")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/johnnybrown/Desktop/src/pandas/pandas/<ipython-input-11-95d97e8283db> in <module>()
----> 1 scatter_plot(df, "a", "b", by="c")
/Users/johnnybrown/Desktop/src/pandas/pandas/tools/plotting.py in scatter_plot(data, x, y, by, ax, figsize, grid)
1419
1420 if by is not None:
-> 1421 fig = _grouped_plot(plot_group, data, by=by, figsize=figsize, ax=ax)
1422 else:
1423 if ax is None:
/Users/johnnybrown/Desktop/src/pandas/pandas/tools/plotting.py in _grouped_plot(plotf, data, column, by, numeric_only, figsize, sharex, sharey, layout, rot, ax)
1634 ravel_axes = []
1635 for row in axes:
-> 1636 ravel_axes.extend(row)
1637
1638 for i, (key, group) in enumerate(grouped):
TypeError: 'AxesSubplot' object is not iterable
```
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} | 2 | "2012-08-01T10:52:31Z" | "2012-08-09T03:14:33Z" | "2012-08-09T03:11:50Z" | NONE | null | numpy 1.7 dev and pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64
```
import pandas as pd
import numpy as np
df = pd.DataFrame([(3,np.datetime64('2012-07-03')),(3,np.datetime64('2012-07-04'))], columns = ['a', 'date'])
df.groupby('a').first()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-56fc44df2d9e> in <module>()
2 import numpy as np
3 df = pd.DataFrame([(3,np.datetime64('2012-07-03')),(3,np.datetime64('2012-07-04'))], columns = ['a', 'date'])
----> 4 df.groupby('a').first()
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in f(self)
25 return self._cython_agg_general(alias)
26 except Exception:
---> 27 return self.aggregate(lambda x: npfunc(x, axis=self.axis))
28
29 f.__doc__ = "Compute %s of group values" % name
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
1501 return self._python_agg_general(arg, *args, **kwargs)
1502 else:
-> 1503 result = self._aggregate_generic(arg, *args, **kwargs)
1504
1505 if not self.as_index:
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _aggregate_generic(self, func, *args, **kwargs)
1564 result[name] = data.apply(wrapper, axis=axis)
1565
-> 1566 return self._wrap_generic_output(result, obj)
1567
1568 def _wrap_aggregated_output(self, output, names=None):
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _wrap_generic_output(self, result, obj)
1763 if self.axis == 0:
1764 result = DataFrame(result, index=obj.columns,
-> 1765 columns=result_index).T
1766 else:
1767 result = DataFrame(result, index=obj.index,
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
371 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
372 elif isinstance(data, dict):
--> 373 mgr = self._init_dict(data, index, columns, dtype=dtype)
374 elif isinstance(data, ma.MaskedArray):
375 mask = ma.getmaskarray(data)
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
459
460 # don't force copy because getting jammed in an ndarray anyway
--> 461 homogenized = _homogenize(data, index, columns, dtype)
462
463 # from BlockManager perspective
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _homogenize(data, index, columns, dtype)
4879
4880 v = _sanitize_array(v, index, dtype=dtype, copy=False,
-> 4881 raise_cast_failure=False)
4882
4883 homogenized[k] = v
/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_f5a74d4-py2.7-linux-x86_64.egg/pandas/core/series.pyc in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
2724 else:
2725 subarr = np.empty(len(index), dtype=dtype)
-> 2726 subarr.fill(value)
2727 else:
2728 return subarr.item()
ValueError: Cannot convert from specific units to generic units in NumPy datetimes or timedeltas
```
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} | 9 | "2012-08-02T01:02:18Z" | "2020-10-07T09:53:17Z" | "2014-11-30T15:20:32Z" | MEMBER | null | Be nice to be able to go from say 5-min OHLC to 1-day OHLC easily
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} | 2 | "2012-08-02T01:25:10Z" | "2012-08-09T02:26:52Z" | "2012-08-09T02:26:52Z" | MEMBER | null | http://stackoverflow.com/questions/11761884/pandas-concatouter-not-doing-union
```
It looks pandas.concat is doing 'left outer' join instead of just union the indexes. Seems a bug to me but maybe I'm missing something obvious.
import pandas
import pandas.util.testing as put
ts1 = put.makeTimeSeries()
ts2 = put.makeTimeSeries()[::2]
ts3 = put.makeTimeSeries()[::3]
ts4 = put.makeTimeSeries()[::4]
## to join with union
## these two are of different length!
pandas.concat([ts1,ts2], join='outer', axis = 1)
pandas.concat([ts2,ts1], join='outer', axis = 1)
Any idea how can I get the full union (as they do claim by using join='outer' on the pandas document)
Thanks.
```
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} | 0 | "2012-08-02T01:27:47Z" | "2012-08-12T21:57:29Z" | "2012-08-12T21:57:29Z" | MEMBER | null | http://stackoverflow.com/questions/11754334/dataframe-to-records-gives-an-error-when-typeindex-is-datetimeindex
```
In [20]: df = read_clipboard(sep='\s+', parse_dates=True)
In [21]: df
Out[21]:
a b c
2012-08-01 2.355928 -2.465061 0.240094
2012-08-02 -0.952323 0.746623 -0.384021
2012-08-03 1.460156 0.292560 -0.494793
2012-08-04 -0.989584 -1.630384 1.373587
2012-08-05 0.014760 -0.789603 -0.622780
In [22]: df.to_records()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-22-6d3142e97d2d> in <module>()
----> 1 df.to_records()
/Users/wesm/code/pandas/pandas/core/frame.pyc in to_records(self, index)
889 names = list(map(str, self.columns))
890
--> 891 return np.rec.fromarrays(arrays, names=names)
892
893 @classmethod
/Library/Frameworks/EPD64.framework/Versions/7.1/lib/python2.7/site-packages/numpy/core/records.pyc in fromarrays(arrayList, dtype, shape, formats, names, titles, aligned, byteorder)
546 # Determine shape from data-type.
547 if len(descr) != len(arrayList):
--> 548 raise ValueError, "mismatch between the number of fields "\
549 "and the number of arrays"
550
ValueError: mismatch between the number of fields and the number of arrays
```
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} | 1 | "2012-08-02T09:00:24Z" | "2012-08-09T02:24:57Z" | "2012-08-09T02:24:57Z" | NONE | null | ```
import numpy as np
import pandas
x = pandas.TimeSeries(data = np.array([-0.54548407, 2.81105692, np.nan, -1.61954275, 1.3670269, -0.73211055, -0.22832786, 0.06384124, -1.7113508, -2.42429978]), index = np.arange(0,10))
```
`print np.fix(x)`
crashes with:
```
File "C:\Python27\lib\site-packages\numpy\lib\ufunclike.py", line 48, in fix
y[...] = nx.where(x >= 0, y1, y2)
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 540, in __setitem__
raise KeyError('%s not in this series!' % str(key))
KeyError: 'Ellipsis not in this series!'
```
I assume this should work?
Thomas
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https://api.github.com/repos/pandas-dev/pandas/issues/1722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1722/events | https://github.com/pandas-dev/pandas/issues/1722 | 5,989,983 | MDU6SXNzdWU1OTg5OTgz | 1,722 | Confusing docstring | {
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} | 0 | "2012-08-02T11:26:00Z" | "2012-08-09T01:05:08Z" | "2012-08-09T01:05:08Z" | NONE | null | I just came across this one while browsing the code.
Maybe this should be Index.intersection in the docstring ?
```
def intersection(self, other):
"""
Specialized intersection for DatetimeIndex objects. May be much faster
than Index.union
Parameters
----------
other : DatetimeIndex or array-like
Returns
-------
y : Index or DatetimeIndex
"""``
```
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} | 1 | "2012-08-02T12:22:31Z" | "2012-08-09T09:02:11Z" | "2012-08-09T01:09:31Z" | CONTRIBUTOR | null | PeriodIndex has a day_of_year method, but DatetimeIndex doesn't. This makes it hard to write generic code which can take either.
Given they both have dayofyear attributes it may be best to remove the day_of_year method so that there is only one obvious way to do it.
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https://api.github.com/repos/pandas-dev/pandas/issues/1724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1724/events | https://github.com/pandas-dev/pandas/issues/1724 | 6,004,415 | MDU6SXNzdWU2MDA0NDE1 | 1,724 | More flexibility in to_period | {
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] | closed | false | null | [] | null | 1 | "2012-08-02T22:57:31Z" | "2012-08-20T05:46:45Z" | "2012-08-20T05:46:45Z" | MEMBER | null | from mailing list
```
Also noticed that `to_period` doesn't work for frequency QS:
In [110]: q = pd.date_range('01-Jan-2012', periods=8, freq='QS-Jan')
In [111]: p = q.to_period()
---------------------------------------------------------------------------
KeyError Traceback (most recent call
last)
<ipython-input-111-869731392d28> in <module>()
----> 1 p = q.to_period()
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\index.pyc in
to_period(self, freq)
582 freq = self.freqstr
583
--> 584 return PeriodIndex(self.values, freq=freq)
585
586 def order(self, return_indexer=False, ascending=True):
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\period.pyc in
__new__(cls, data, ordinal, freq, start, end, periods, copy, name,
year, month, quarter, day, hour, minute, second)
510 hour=None, minute=None, second=None):
511
--> 512 freq = _freq_mod.get_standard_freq(freq)
513
514 if periods is not None:
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\frequencies.pyc
in get_standard_freq(freq)
501 return get_offset_name(freq)
502
--> 503 code, stride = get_freq_code(freq)
504 return _get_freq_str(code, stride)
505
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\frequencies.pyc
in get_freq_code(freqstr)
64
65 base, stride = _base_and_stride(freqstr)
---> 66 code = _period_str_to_code(base)
67
68 return code, stride
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\frequencies.pyc
in _period_str_to_code(freqstr)
687 return _period_code_map[freqstr]
688 except:
--> 689 alias = _period_alias_dict[freqstr]
690 return _period_code_map[alias]
691
KeyError: 'QS-JAN'
In [112]: pd.__version__
Out[112]: '0.8.1'
Although this can be worked around by providing freq='Q' as an
argument I think it would be sensible if the `to_period` method would
automatically recognise QS as a quarterly frequency.
Regards,
Dave
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1725/events | https://github.com/pandas-dev/pandas/issues/1725 | 6,004,435 | MDU6SXNzdWU2MDA0NDM1 | 1,725 | QS frequency alias | {
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] | closed | false | null | [] | null | 0 | "2012-08-02T22:58:41Z" | "2016-10-12T23:03:07Z" | "2012-08-20T05:38:12Z" | MEMBER | null | The table at http://pandas.pydata.org/pandas-docs/dev/timeseries.html#offset-aliases
appears to suggest there is a QS frequency
but when I try it I get the following traceback:
```
In [84]: pd.date_range('01-Jan-2012', periods=8, freq='QS')
---------------------------------------------------------------------------
ValueError Traceback (most recent call
last)
<ipython-input-84-26002af2c5f5> in <module>()
----> 1 pd.date_range('01-Jan-2012', periods=8, freq='QS')
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\index.pyc in
date_range(start, end, periods, freq, tz, normalize, name)
1315 """
1316 return DatetimeIndex(start=start, end=end,
periods=periods,
-> 1317 freq=freq, tz=tz,
normalize=normalize, name=name)
1318
1319
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\index.pyc in
__new__(cls, data, freq, start, end, periods, copy, name, tz,
verify_integrity, normalize, **kwds)
148 if not isinstance(freq, DateOffset):
149 if freq != 'infer':
--> 150 freq = to_offset(freq)
151 else:
152 freq_infer = True
C:\dev\bin\Python27\lib\site-packages\pandas\tseries\frequencies.pyc
in to_offset(freqstr)
390 delta = delta + offset
391 except Exception:
--> 392 raise ValueError("Could not evaluate %s" %
freqstr)
393
394 if delta is None:
ValueError: Could not evaluate QS
In [85]: pd.date_range('01-Jan-2012', periods=8, freq='QS-JAN')
Out[85]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2013-10-01 00:00:00]
Length: 8, Freq: QS-JAN, Timezone: None
In [86]:
QS-JAN seems to work but I'd expect QS to be an alias for QS-JAN in
the same way Q is an alias for Q-DEC:
In [87]: pd.date_range('01-Jan-2012', periods=8, freq='Q-DEC')
Out[87]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-03-31 00:00:00, ..., 2013-12-31 00:00:00]
Length: 8, Freq: Q-DEC, Timezone: None
In [88]: pd.date_range('01-Jan-2012', periods=8, freq='Q')
Out[88]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-03-31 00:00:00, ..., 2013-12-31 00:00:00]
Length: 8, Freq: Q-DEC, Timezone: None
```
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} | 1 | "2012-08-03T03:49:52Z" | "2013-11-24T13:44:10Z" | "2012-08-13T01:08:30Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
#8/6/12 is a Monday
ind = pd.DatetimeIndex(start="8/6/2012", end="8/26/2012", freq="D")
n = len(ind)
data = [[x] * 5 for x in range(n)]
df = pd.DataFrame(data, columns=['open', 'high', 'low', 'close', 'vol'], index=ind)
df.resample('W-MON', how='first', closed='left', label='left')
```
Will result in an error. I tracked it down to
``` python
def _adjust_bin_edges(self, binner, ax_values):
# Some hacks for > daily data, see #1471, #1458, #1483
bin_edges = binner.asi8
if self.freq != 'D' and is_superperiod(self.freq, 'D'):
day_nanos = _delta_to_nanoseconds(timedelta(1))
if self.closed == 'right':
bin_edges = bin_edges + day_nanos - 1
else:
bin_edges = bin_edges + day_nanos
```
I'm not sure why the day is added when closed='left'. Even on datasets that don't start on a Monday, and thus don't error, it'll cause the intervals to start on Tuesday.
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} | 1 | "2012-08-03T10:13:51Z" | "2012-08-12T21:35:09Z" | "2012-08-12T21:34:54Z" | NONE | null | I am trying to build a DataFrame from a dict of Series objects which have (not necessarily exactly matching) MultiIndex indices, sometimes I don't get any results for some particular data point and so I create an empty Series object and add that to the dict, as I need there to be a column present even without any data (later I convert all NAs to 0). This seems to work sometimes, but other times I get an error message that implies all the Series need hierarchical indices, it seems to depend on the order in which the Series are added to the dict. See example session below, the first time I create the DataFrame it behaves just as I'd like, giving me a df index that is the union of all the indices in the populated Series and supplying NaNs wherever there is no data, but the second time it blows up. Perhaps I shouldn't be relying on this behaviour but it seems that the results should at least be consistent.
Any tips on how to solve this in a cleaner way also very welcome. Thanks.
``` python
In [292]: pandas.__version__
Out[292]: '0.8.1'
In [293]: s1 = Series([1,2,3,4], index=MultiIndex.from_tuples([(1,2),(1,3),(2,2),(2,4)]))
In [294]: s2 = Series([1,2,3,4], index=MultiIndex.from_tuples([(1,2),(1,3),(3,2),(3,4)]))
In [295]: s3 = Series()
In [296]: df = DataFrame.from_dict({'foo':s1, 'bar':s2, 'baz':s3})
In [297]: df
Out[297]:
bar baz foo
1 2 1 NaN 1
3 2 NaN 2
2 2 NaN NaN 3
4 NaN NaN 4
3 2 3 NaN NaN
4 4 NaN NaN
In [298]: df = DataFrame.from_dict({'foo':s1, 'baz':s3, 'bar':s2})
... stacktrace
TypeError: can only call with other hierarchical index objects
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1728/events | https://github.com/pandas-dev/pandas/issues/1728 | 6,016,411 | MDU6SXNzdWU2MDE2NDEx | 1,728 | to_html issues | {
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] | closed | false | null | [] | null | 2 | "2012-08-03T14:54:44Z" | "2016-10-12T23:03:07Z" | "2012-08-05T19:45:44Z" | CONTRIBUTOR | null | - doc string mentions a "justify" parameter that apparently does not exist and provokes an errore if used
- setting index=False produces no visible effect
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https://api.github.com/repos/pandas-dev/pandas/issues/1729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1729/events | https://github.com/pandas-dev/pandas/issues/1729 | 6,033,100 | MDU6SXNzdWU2MDMzMTAw | 1,729 | Unexpected result when slicing time series intra day | {
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} | [] | closed | false | null | [] | null | 1 | "2012-08-04T21:56:05Z" | "2012-08-12T23:28:20Z" | "2012-08-12T23:28:20Z" | CONTRIBUTOR | null | Time series slicing does not work intra day (Pandas 0.8.1):
```
import pandas as pd
import datetime as dt
rng = pd.date_range('1/1/2012', periods=4, freq='12H')
a = pd.Series(index=rng)
a[dt.datetime(2011,1,1,3):]
```
incorrectly results:
```
2012-01-01 00:00:00 NaN
2012-01-01 12:00:00 NaN
2012-01-02 00:00:00 NaN
2012-01-02 12:00:00 NaN
```
The same incorrect result is returned with:
```
a.ix[dt.datetime(2011,1,1,3):]
```
The correct result is obtained though with the truncate() method:
```
a.truncate(before=dt.datetime(2012,1,1,3))
```
correctly returns:
```
2012-01-01 12:00:00 NaN
2012-01-02 00:00:00 NaN
2012-01-02 12:00:00 NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1730/events | https://github.com/pandas-dev/pandas/issues/1730 | 6,033,230 | MDU6SXNzdWU2MDMzMjMw | 1,730 | Unexpected result when combining Series into a DataFrame | {
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} | [] | closed | false | null | [] | null | 1 | "2012-08-04T22:16:02Z" | "2012-08-09T01:25:57Z" | "2012-08-09T01:25:34Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
rng_a = pd.date_range('1/1/2012',periods=4,freq='3H')
rng_b = pd.date_range('1/1/2012',periods=4,freq='4H')
a = pd.Series([0, 1, 2, 3], rng_a)
b = pd.Series([0, 1, 2, 3], rng_b)
pd.DataFrame({'a':a, 'b': b})
```
unexpectedly returns:
```
a b
1/1/2012 00:00:00 0 0
1/1/2012 03:00:00 1 NaN
1/1/2012 06:00:00 2 NaN
1/1/2012 09:00:00 3 NaN
1/1/2012 12:00:00 NaN 3
```
The correct result though is obtained with:
```
a = pd.Series([0, 1, 2, 3], ['1/1/2012 00:00:00', '1/1/2012 03:00:00', '1/1/2012 06:00:00', '1/1/2012 09:00:00'])
b = pd.Series([0, 1, 2, 3], ['1/1/2012 00:00:00', '1/1/2012 04:00:00', '1/1/2012 08:00:00', '1/1/2012 12:00:00'])
pd.DataFrame({'a':a, 'b': b})
```
correctly returns:
```
a b
1/1/2012 00:00:00 0 0
1/1/2012 03:00:00 1 NaN
1/1/2012 04:00:00 NaN 1
1/1/2012 06:00:00 2 NaN
1/1/2012 08:00:00 NaN 2
1/1/2012 09:00:00 3 NaN
1/1/2012 12:00:00 NaN 3
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1731/events | https://github.com/pandas-dev/pandas/pull/1731 | 6,033,239 | MDExOlB1bGxSZXF1ZXN0MTk3NTE3MQ== | 1,731 | DOC: skipna not an argument of drop_duplicates | {
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} | 1 | "2012-08-04T22:18:09Z" | "2014-06-18T10:03:18Z" | "2012-09-08T21:25:09Z" | CONTRIBUTOR | null | From what I can tell, `skipna` keyword was both added and subsequently
removed in #1244. However, the docstring for DataFrame.drop_duplicates
still lists it as a parameter.
I remove this from the docs, so they accurately represent the code.
An alternative to this would be to put back the skipna functionality.
pinging @changhiskhan, since he was the one that worked on this.
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} | 2 | "2012-08-04T22:42:38Z" | "2016-10-12T23:03:07Z" | "2012-08-20T05:36:24Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
import matplotlib.pyplot as plt
rng_a = pd.date_range('1/1/2012', periods=4, freq='2H'')
rng_b = pd.date_range('1/1/2012', periods=4, freq='H')
rng_c = pd.date_range('1/1/2012', periods=4, freq='30T')
a = pd.Series([0, 1, 3, 6], rng_a)
b = pd.Series([0, 1, 3, 6], rng_b)
c = pd.Series([0, 1, 3, 6], rng_c)
a.plot()
b.plot() # works fine
c.plot() # ValueError: Incompatible frequency conversion
plt.show() # only a and b are plotted
```
b.plot() works fine, but c.plot() doesn't and returns the following error:
```
ValueError: Incompatible frequency conversion
```
When building the timeseries without specifying their frequency, no error is reported when trying to plot them, but there seems to be another issue occuring:
```
a = pd.Series([0, 1, 2, 3], ['1/1/2012 00:00:00', '1/1/2012 02:00:00', '1/1/2012 04:00:00', '1/1/2012 06:00:00'])
b = pd.Series([0, 1, 2, 3], ['1/1/2012 00:00:00', '1/1/2012 01:00:00', '1/1/2012 02:00:00', '1/1/2012 03:00:00'])
c = pd.Series([0, 1, 2, 3], ['1/1/2012 00:00:00', '1/1/2012 00:30:00', '1/1/2012 01:00:00', '1/1/2012 01:30:00'])
a.plot()
b.plot() # no error
c.plot() # no error
plt.show() # only c is plotted
```
No errors are reported, but only series c is plotted.
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https://api.github.com/repos/pandas-dev/pandas/issues/1733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1733/events | https://github.com/pandas-dev/pandas/pull/1733 | 6,033,440 | MDExOlB1bGxSZXF1ZXN0MTk3NTI0MQ== | 1,733 | DOC: fixed some minor typos | {
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} | 1 | "2012-08-04T22:57:14Z" | "2012-08-12T21:44:19Z" | "2012-08-12T21:44:19Z" | CONTRIBUTOR | null | pandas is great, thank you!
I've only been using it for a couple of months now, and when I found one typo, I couldn't stop myself from pitching-in and going through all the relevant `.py` files and fixing the typos that popped out.
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https://api.github.com/repos/pandas-dev/pandas/issues/1734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1734/events | https://github.com/pandas-dev/pandas/issues/1734 | 6,033,453 | MDU6SXNzdWU2MDMzNDUz | 1,734 | Fix statsmodels import in pandas.stats.var or remove module | {
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} | 1 | "2012-08-04T23:00:11Z" | "2012-08-10T16:49:40Z" | "2012-08-10T16:49:40Z" | MEMBER | null | cc @blais
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https://api.github.com/repos/pandas-dev/pandas/issues/1735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1735/events | https://github.com/pandas-dev/pandas/pull/1735 | 6,033,768 | MDExOlB1bGxSZXF1ZXN0MTk3NTM2Ng== | 1,735 | Support for HTML class'es on DataFrame.to_html(). | {
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} | 2 | "2012-08-05T00:03:28Z" | "2014-06-19T18:08:10Z" | "2012-08-13T04:05:24Z" | CONTRIBUTOR | null | I really like to use sorttable.js and dragtable.js.
I need to be able to customize the HTML classes.
I also added a default class, so that people can use css on all Pandas generated tables by default.
Default class is "dataframe"
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} | 0 | "2012-08-06T20:07:04Z" | "2012-08-12T20:37:34Z" | "2012-08-12T20:37:34Z" | CONTRIBUTOR | null | ``` python
In [34]: tuples = [t for t in product(range(2), range(2))]
In [35]: index = pandas.MultiIndex.from_tuples(tuples, names=[u'\u0394', 'i1'])
In [36]: df = pandas.DataFrame(np.random.randn(4,4), index=index)
In [37]: s = pandas.Series(range(4), index=index)
In[38]: df
...
UnicodeEncodeError: 'ascii' codec can't encode character u'\u0394' in position 0: ordinal not in range(128)
In[38]: s
...
UnicodeEncodeError: 'ascii' codec can't encode character u'\u0394' in position 0: ordinal not in range(128)
```
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} | 5 | "2012-08-07T15:23:26Z" | "2016-10-12T23:03:07Z" | "2012-08-13T08:02:16Z" | NONE | null | The example here fails with 0.8.1 (worked with 0.8.0)
http://pandas.pydata.org/pandas-docs/dev/computation.html?highlight=outlier#moving-rolling-statistics-moments
In [1]: from pandas import *
In [2]: from numpy.random import randn
In [3]: ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
In [4]: ts = ts.cumsum()
In [5]: ts.plot(style='k--')
/usr/lib64/python2.6/site-packages/matplotlib/backends/backend_gtk.py:621: DeprecationWarning: Use the new widget gtk.Tooltip
## self.tooltips = gtk.Tooltips()
AttributeError Traceback (most recent call last)
/home/jgarcke/code/python_fx_tools/<ipython console> in <module>()
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, logy, secondary_y, *_kwds)
1292 secondary_y=secondary_y, *_kwds)
1293
-> 1294 plot_obj.generate()
1295 plot_obj.draw()
1296
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tools/plotting.pyc in generate(self)
573 self._compute_plot_data()
574 self._setup_subplots()
--> 575 self._make_plot()
576 self._post_plot_logic()
577 self._adorn_subplots()
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tools/plotting.pyc in _make_plot(self)
858 if self.use_index and self._use_dynamic_x():
859 data = self._maybe_convert_index(self.data)
--> 860 self._make_ts_plot(data, **self.kwds)
861 else:
862 import matplotlib.pyplot as plt
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tools/plotting.pyc in _make_ts_plot(self, data, *_kwargs)
918
919 newlines = tsplot(data, plotf, ax=ax, label=label, style=self.style,
--> 920 *_kwargs)
921 ax.grid(self.grid)
922 lines.append(newlines[0])
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tseries/plotting.pyc in tsplot(series, plotf, **kwargs)
80 # set date formatter, locators and rescale limits
```
81 format_dateaxis(ax, ax.freq)
```
---> 82 left, right = _get_xlim(ax.get_lines())
83 ax.set_xlim(left, right)
84
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.1-py2.6-linux-x86_64.egg/pandas/tseries/plotting.pyc in _get_xlim(lines)
210 for l in lines:
211 x = l.get_xdata()
--> 212 left = min(x[0].ordinal, left)
213 right = max(x[-1].ordinal, right)
214 return left, right
AttributeError: 'numpy.int64' object has no attribute 'ordinal'
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} | 2 | "2012-08-07T21:16:32Z" | "2016-10-12T23:03:08Z" | "2013-03-23T01:53:06Z" | MEMBER | null | from the mailing list cc @lodagro
```
That is indeed odd.
There also seems to be a dependency on the dtype. Below an example for all ints and all floats.
In [121]: df
Out[121]:
A B
a 7 0
b 7 -2
In [122]: df.apply(foo, 1)
Out[122]:
A B
a NaN NaN
b NaN NaN
All NaN???
I would expect to see this:
In [123]: s = pandas.Series([foo(row[1]) for row in df.iterrows()], df.index)
In [124]: s
Out[124]:
a {'properties': {'A': 7, 'B': 0}}
b {'properties': {'A': 7, 'B': -2}}
Seems to work fine for all floats.
In [125]: df = pandas.DataFrame(np.random.randn(2, 2), columns=list('AB'), index=list('ab'))
In [126]: df
Out[126]:
A B
a -0.407883 0.018206
b -1.081038 0.492944
In [127]: df.apply(foo, 1)
Out[127]:
a {'properties': {'A': -0.407882576359619, 'B': 0.0
b {'properties': {'A': -1.081038117264707, 'B': 0.4
```
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} | 4 | "2012-08-07T21:24:44Z" | "2012-09-08T22:03:08Z" | "2012-08-09T20:51:50Z" | CONTRIBUTOR | null | Add functionality to pd.io.DataReader that collects options data from yahoo finance for a given stock.
Would rely on BeatifulSoup to parse html data.
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} | 1 | "2012-08-07T21:25:30Z" | "2014-05-29T00:31:21Z" | "2014-05-29T00:31:21Z" | MEMBER | null | from mailing list
```
Hi,
I'm using pandas to read in a set of tab delimited files into DataFrames. Each file has only 3-5 columns and roughly 20,000 rows. The files have a column that I'd like to index by, but some of the entries are duplicates. I find that it takes roughly 5 seconds to read a set of three such files into memory, drop duplicates, and index using "read_table". I.e. to do:
dict_of_dfs = {}
for filename in three_filenames:
df = pandas.read_table(filename, sep="\t")
# drop duplicates: use "entry_id" field
df = df.drop_duplicates("entry_id")
df = df.set_index("entry_id")
dict_of_dfs[filename] = df
So to do the above loop body three times takes 5 seconds, which seems slow to me, unless I am missing something. I'm wondering if this is the expected performance for dataframes of this size, or if I'm misusing something. It seems like IO should not be a bottleneck with files that are this small (keep in mind the max number of entries is less than 30k).
Any advice on how to optimize this would be greatly appreciated! Thanks.
```
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} | 3 | "2012-08-07T21:29:23Z" | "2012-09-08T21:11:23Z" | "2012-09-08T21:11:23Z" | CONTRIBUTOR | null | I'm using pandas in a Django application running on Ubuntu 12.04 and after upgrading from pandas 0.6.1 to 0.8.1 I'm getting some errors that completely prevent the application from running.
If I run Django's development server (manage.py runserver), everything works fine, but if instead I run uWSGI I see the following 2 tracebacks in my uwsgi log:
```
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/django/core/handlers/base.py", line 101, in get_response
request.path_info)
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 298, in resolve
for pattern in self.url_patterns:
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 328, in url_patterns
patterns = getattr(self.urlconf_module, "urlpatterns", self.urlconf_module)
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 323, in urlconf_module
self._urlconf_module = import_module(self.urlconf_name)
File "/usr/local/lib/python2.7/dist-packages/django/utils/importlib.py", line 35, in import_module
__import__(name)
File "/sites/ycharts/urls.py", line 5, in <module>
from apps.companies.sitemaps import CompanySitemap
File "/sites/ycharts/apps/companies/sitemaps.py", line 6, in <module>
from apps.companies.models import Company
File "/sites/ycharts/apps/companies/models.py", line 19, in <module>
from apps.main.utils import googlesearch, wikipedia, date_utils, data_utils, \
File "/sites/ycharts/apps/main/utils/data_utils.py", line 3, in <module>
import pandas
File "/usr/local/lib/python2.7/dist-packages/pandas/__init__.py", line 14, in <module>
import pandas.lib as lib
AttributeError: 'module' object has no attribute 'lib'
```
and
```
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/django/core/handlers/base.py", line 101, in get_response
request.path_info)
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 298, in resolve
for pattern in self.url_patterns:
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 328, in url_patterns
patterns = getattr(self.urlconf_module, "urlpatterns", self.urlconf_module)
File "/usr/local/lib/python2.7/dist-packages/django/core/urlresolvers.py", line 323, in urlconf_module
self._urlconf_module = import_module(self.urlconf_name)
File "/usr/local/lib/python2.7/dist-packages/django/utils/importlib.py", line 35, in import_module
__import__(name)
File "/sites/ycharts/urls.py", line 5, in <module>
from apps.companies.sitemaps import CompanySitemap
File "/sites/ycharts/apps/companies/sitemaps.py", line 6, in <module>
from apps.companies.models import Company
File "/sites/ycharts/apps/companies/models.py", line 19, in <module>
from apps.main.utils import googlesearch, wikipedia, date_utils, data_utils, \
File "/sites/ycharts/apps/main/utils/data_utils.py", line 3, in <module>
import pandas
File "/usr/local/lib/python2.7/dist-packages/pandas/__init__.py", line 28, in <module>
from pandas.core.api import *
File "/usr/local/lib/python2.7/dist-packages/pandas/core/api.py", line 10, in <module>
from pandas.core.format import (set_printoptions, reset_printoptions,
File "/usr/local/lib/python2.7/dist-packages/pandas/core/format.py", line 147, in <module>
class DataFrameFormatter(object):
File "/usr/local/lib/python2.7/dist-packages/pandas/core/format.py", line 156, in DataFrameFormatter
__doc__ += docstring_to_string
TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str
```
After reading in #284, I thought it may be a path issue so I tried overwriting `sys.path` in my uwsgi configuration file so that it's the exact same path that the dev server sets up for me, but that didn't help.
Any tips / debugging ideas / help on the above would be greatly appreciated.
Thank you!
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https://api.github.com/repos/pandas-dev/pandas/issues/1742 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1742/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1742/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1742/events | https://github.com/pandas-dev/pandas/issues/1742 | 6,092,176 | MDU6SXNzdWU2MDkyMTc2 | 1,742 | qcut not working properly with groupby | {
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} | [] | closed | false | null | [] | null | 1 | "2012-08-07T23:55:26Z" | "2012-08-07T23:57:24Z" | "2012-08-07T23:56:42Z" | NONE | null | For some reason, the quantile bands aren't what you would expect when using a groupby...
---
print pandas.**version**
0.8.1
frame = DataFrame({'data1': np.random.randn(1000), 'data2': np.random.randn(1000)})
factor = qcut(frame.data1, 4)
frame.data2.groupby(factor)
Out[43]: <pandas.core.groupby.SeriesGroupBy at 0x5a61e90>
frame.data2.groupby(factor).describe()
Out[44]:
data1
(-0.00303, 0.649] count 250.000000
mean 0.007775
std 0.999344
min -2.502555
25% -0.738085
50% -0.010441
75% 0.658221
max 3.101737
(-0.748, -0.00303] count 250.000000
mean 0.043912
std 1.000687
min -3.025111
25% -0.585176
50% 0.087992
75% 0.670224
max 3.043880
(0.649, 3.0124] count 250.000000
mean -0.068339
std 1.000569
min -2.767514
25% -0.842156
50% -0.048075
75% 0.623365
max 3.043415
[-3.127, -0.748] count 250.000000
mean -0.063501
std 1.090136
min -2.706057
25% -0.776094
50% -0.056513
75% 0.729134
max 3.377690
---
The "mean" and "median" functions seem to be behaving properly:
frame.data2.groupby(factor).mean()
Out[45]:
data1
[-3.127, -0.748] -0.063501
(-0.748, -0.00303] 0.043912
(-0.00303, 0.649] 0.007775
(0.649, 3.0124] -0.068339
frame.data2.groupby(factor).median()
Out[46]:
data1
[-3.127, -0.748] -0.056513
(-0.748, -0.00303] 0.087992
(-0.00303, 0.649] -0.010441
(0.649, 3.0124] -0.048075
BUT _count_ is broken. this makes the median and mean functions inconsistent with the describe function:
frame.data2.groupby(factor).count()
Out[47]:
data1
(-0.00303, 0.649] 250
(-0.748, -0.00303] 250
(0.649, 3.0124] 250
[-3.127, -0.748] 250
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} | 2 | "2012-08-08T10:30:03Z" | "2012-09-18T20:34:13Z" | "2012-09-18T20:33:40Z" | CONTRIBUTOR | null | Hey Wes,
Here's a use case that I think is not covered by Pandas.
It's a use case for creation of a DataFrame object from a
list of dicts.
I extract "documents" (dicts) from a MongoDB database.
From these dicts, one of the keys is meant to be used as
the index.
While I can do something like
df = DataFrame(documents, columns=['order_id', 'time', 'quantity'])
the index that is added is an arbitrary one.
I'd like to use 'order_id' as my index, in this example.
The only way for me to do that at the moment is to create
the index explicitly, e.g. like this:
df = DataFrame(documents, columns=['order_id', 'time', 'quantity'],
index=[o['order_id'] for o in documents])
This is a bit of a PIA. It would be nice if one could just
specify the index key/column name, e.g.
df = DataFrame(documents, columns=['order_id', 'time', 'quantity'], indexcol='order_id')
That's my use case almost everywhere.
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https://api.github.com/repos/pandas-dev/pandas/issues/1745 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1745/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1745/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1745/events | https://github.com/pandas-dev/pandas/issues/1745 | 6,100,949 | MDU6SXNzdWU2MTAwOTQ5 | 1,745 | Creating a DataFrame from Series with unsorted indeces gives completely bad data | {
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] | closed | false | null | [] | null | 5 | "2012-08-08T11:55:36Z" | "2012-08-10T16:06:24Z" | "2012-08-10T15:23:45Z" | NONE | null | Creating a DataFrame from Series with unsorted indeces gives completely bad data.
I've [written a module](https://gist.github.com/3294509/e777009080fbcbe3c6c91baeb3a71fbb1a092cdc) that reproduces the bug—unless this is by design. Give it a try.
(I'm running `pandas==0.8.1`, Python 2.7.2, and OS X Mountain Lion.)
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} | 3 | "2012-08-08T13:05:47Z" | "2012-09-18T00:22:47Z" | "2012-08-09T19:39:21Z" | NONE | null | I have a small data frame here (obtained by slicing a much larger data frame with a MultiIndex):
```
In [6]: x.shape
Out[6]: (4, 32)
```
Its repr() looks like this:
```
In [7]: print repr(x)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 4 entries, ('arquant9.crw', 10, 336640) to ('arquant9.crw', 10, 336652)
Data columns:
lle 4 non-null values
lhz 4 non-null values
MiPf 4 non-null values
LLPf 4 non-null values
RLPf 4 non-null values
LMPf 4 non-null values
RMPf 4 non-null values
LDFr 4 non-null values
RDFr 4 non-null values
LLFr 4 non-null values
RLFr 4 non-null values
LMFr 4 non-null values
RMFr 4 non-null values
LMCe 4 non-null values
RMCe 4 non-null values
MiCe 4 non-null values
MiPa 4 non-null values
LDCe 4 non-null values
RDCe 4 non-null values
LDPa 4 non-null values
RDPa 4 non-null values
LMOc 4 non-null values
RMOc 4 non-null values
LLTe 4 non-null values
RLTe 4 non-null values
LLOc 4 non-null values
RLOc 4 non-null values
MiOc 4 non-null values
A2 4 non-null values
HEOG 4 non-null values
rle 4 non-null values
rhz 4 non-null values
dtypes: float64(32)
```
Producing this repr takes a bizarrely long time, it's _very_ noticeable in interactive use:
```
In [8]: time repr(x)
CPU times: user 1.39 s, sys: 0.06 s, total: 1.45 s
Wall time: 1.36 s
```
The object is available here: http://vorpus.org/~njs/tmp/slow-repr-x.pickle
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} | [] | closed | false | null | [] | null | 1 | "2012-08-08T16:27:15Z" | "2012-11-12T06:08:57Z" | "2012-11-12T06:08:39Z" | CONTRIBUTOR | null | Extend the current OLS class (or create a new one) to perform regression using a more stable method. Some examples are LS_SVD (least squares using singular value decomposition), RLS (regularized least squares ), or RLS_SVD. Those two methods are more reliable when you have ill-conditioned problems.
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} | 3 | "2012-08-09T04:06:37Z" | "2014-06-16T23:44:42Z" | "2012-09-08T22:09:37Z" | CONTRIBUTOR | null | Added ability to fetch options data from yahoo! finance. With this there are a few 'helper' methods to get different things out of the data.
Answers issue #1739
Also cleaned up a bit of the /pandas/io/data.py file to comply better with PEP8.
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} | 0 | "2012-08-09T12:50:40Z" | "2012-08-12T22:16:08Z" | "2012-08-12T22:16:08Z" | CONTRIBUTOR | null | from [stackoverflow](http://stackoverflow.com/questions/11870058/pandas-stacking-dataframes-generated-by-apply)
``` python
In [67]: s = pandas.Series([pandas.DataFrame(np.random.randn(2,2)) for i in range(5)])
In [68]: s
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Exception ValueError: ValueError('Cannot call bool() on DataFrame.',) in 'util._checknull' ignored
Out[68]:
0 0 1
0 1.821894 -1.268104
1 0.
1 0 1
0 -1.361116 0.809527
1 -0.
2 0 1
0 0.603496 0.034081
1 -0.
3 0 1
0 -0.274753 -0.544456
1 0.
4 0 1
0 -0.699664 0.366191
1 -0.
```
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} | 1 | "2012-08-09T23:37:20Z" | "2012-08-13T02:45:11Z" | "2012-08-13T02:45:01Z" | NONE | null | Hi,
I think there might be a bug in the assignment to a DataFrame object through .ix when non-unique MultiIndexes are involved. The next sample code describes this issue:
```
In [1]: from pandas import DataFrame, MultiIndex
In [2]:
In [2]: df = DataFrame([[1, 1, "x", "X"], [1, 1, "y", "Y"], [1, 2, "z", "Z"]],
...: columns=list("ABCD"))
In [3]: df = df.set_index(list("AB"))
In [4]: df
Out[4]:
C D
A B
1 1 x X
1 y Y
2 z Z
In [5]:
In [5]: ix = MultiIndex.from_tuples([(1, 1)])
In [6]: df.ix[ix, "C"] # Shows both values with index (1, 1)
Out[6]:
A B
1 1 x
1 y
Name: C
In [7]:
In [7]: df.ix[ix, "C"] = "_" # Only sets one of the two values with index (1, 1): BUG?
In [8]: df
Out[8]:
C D
A B
1 1 x X
1 _ Y
2 z Z
```
Thanks and regards.
Manu.
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} | 2 | "2012-08-10T09:36:25Z" | "2014-02-18T18:57:38Z" | "2014-02-18T18:57:38Z" | NONE | null | In 8.1, the "adjust" argument was added to pandas.moments.ewma, but not to ewmcorr, ewmcov, ewmstd, ewmvar, ewmvol Their doc string says all of them have the option to pass adjust, but that's not the case. It's a trivial change & I'll try to submit a pull request to fix it if I find the time.
discussion
https://groups.google.com/forum/#!topic/pystatsmodels/9VvSpp_h8uo
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} | 17 | "2012-08-10T13:02:46Z" | "2014-11-02T18:16:51Z" | "2014-11-02T18:16:30Z" | NONE | null | This was on the list, but so it doesn't get forgotten: https://groups.google.com/d/msg/pydata/EaPRB4KNeTQ/Y-3kG5gW3xMJ
Summary: storing a large multi-index takes a _lot_ of memory, with current pandas master, once it has been indexed into once. ~200 megabytes for a 1 mega-entry index. (As compared to ~20 megabytes pickled.)
The offending dataframe is available here (this is different from the url in the email thread): http://vorpus.org/~njs/tmp/df-pandas-master.pickle.gz
Partly this is just a bug; there is a large object array of tuples that Wes says shouldn't exist, but seems to anyway.
I was going to also say that it would be nice to be able to just opt out of the hash table entirely, but code inspection suggests that this already happens for indexes with more than pandas/src/engines.pyx:_SIZE_CUTOFF = 1000000 entries. The array above is _just_ under this threshold. Perhaps this cutoff should be lowered for indices that have higher memory overhead, like multiindexes? Or even be user-controllable when the index is created? (The code I'm using to load in and set up these dataframes already takes elaborate measures to manage memory overhead, so this wouldn't be a hassle.)
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} | 1 | "2012-08-10T14:44:18Z" | "2012-08-10T15:31:47Z" | "2012-08-10T15:31:41Z" | CONTRIBUTOR | null | From [stackoverflow](http://stackoverflow.com/questions/11848578/pandas-messing-up-data-frame).
Constructing a DataFrame from multiple time series fails, all values from the resulting frame are NaN (which is not the case in the individual series).
Code to reproduce: http://pastebin.com/WTsCkKrL
I could reproduce it.
``` python
In [101]: np.__version__
Out[101]: '1.6.1'
In [102]: pandas.__version__
Out[102]: '0.8.2.dev-8cde377'
In [103]: df
Out[103]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 386 entries, 1992-06-05 15:50:11.527680 to 1774-08-13 02:00:15.237103
Data columns:
770000000006 0 non-null values
770000000009 0 non-null values
770000000010 0 non-null values
770000000011 0 non-null values
770000000012 0 non-null values
770000000013 0 non-null values
770000000018 0 non-null values
770000000020 0 non-null values
770000000021 0 non-null values
770000000022 0 non-null values
770000000024 0 non-null values
770000000029 0 non-null values
770000000030 0 non-null values
770000000032 0 non-null values
770000000034 0 non-null values
770000000049 0 non-null values
dtypes: float64(16)
```
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} | 1 | "2012-08-10T18:42:45Z" | "2012-08-10T22:10:22Z" | "2012-08-10T22:10:21Z" | CONTRIBUTOR | null | In numpy
``` python
>>> np.isnan([[False]])
array([[False]], dtype=bool)
```
In pandas
``` python
>>> pandas.isnull([[False]])
False
>>> pandas.isnull([[1],[2]])
False
```
I would expect these to return arrays for array-like input. Is there any reason not to?
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|
https://api.github.com/repos/pandas-dev/pandas/issues/1757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1757/events | https://github.com/pandas-dev/pandas/issues/1757 | 6,165,207 | MDU6SXNzdWU2MTY1MjA3 | 1,757 | MultiIndexes containing >=1000000 elements do not work | {
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} | 2 | "2012-08-10T22:23:29Z" | "2012-08-13T09:24:39Z" | "2012-08-13T03:20:13Z" | NONE | null | With current master, any attempt to index into a Series (or whatever) with a MultiIndex and >=1000000 (= _SIZE_CUTOFF) elements simply raises an error:
```
In [5]: n = 1000000 - 1; pandas.Series(np.arange(n), pandas.MultiIndex.from_arrays((["a"] * n, np.arange(n))))[("a", 5)]
Out[5]: 5
In [6]: n = 1000000; pandas.Series(np.arange(n), pandas.MultiIndex.from_arrays((["a"] * n, np.arange(n))))[("a", 5)]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-a83549b6c664> in <module>()
----> 1 n = 1000000; pandas.Series(np.arange(n), pandas.MultiIndex.from_arrays((["a"] * n, np.arange(n))))[("a", 5)]
/home/njs/src/pandas/pandas/core/series.pyc in __getitem__(self, key)
453 key = np.asarray(key, dtype=bool)
454
--> 455 return self._get_with(key)
456
457 def _get_with(self, key):
/home/njs/src/pandas/pandas/core/series.pyc in _get_with(self, key)
471 if isinstance(key, tuple):
472 try:
--> 473 return self._get_values_tuple(key)
474 except:
475 if len(key) == 1:
/home/njs/src/pandas/pandas/core/series.pyc in _get_values_tuple(self, key)
514
515 # If key is contained, would have returned by now
--> 516 indexer, new_index = self.index.get_loc_level(key)
517 return Series(self.values[indexer], index=new_index, name=self.name)
518
/home/njs/src/pandas/pandas/core/index.pyc in get_loc_level(self, key, level)
2069 if not any(isinstance(k, slice) for k in key):
2070 if len(key) == self.nlevels:
-> 2071 return self._engine.get_loc(key), None
2072 else:
2073 # partial selection
/home/njs/src/pandas/pandas/lib.so in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:107666)()
/home/njs/src/pandas/pandas/lib.so in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:107423)()
/home/njs/src/pandas/pandas/lib.so in util.get_value_at (pandas/src/tseries.c:114800)()
TypeError: only integer arrays with one element can be converted to an index
```
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} | 1 | "2012-08-12T09:21:02Z" | "2012-08-12T22:01:43Z" | "2012-08-12T22:01:28Z" | NONE | null | Hi,
as of pandas '0.8.2.dev-ee9321f', the merge operation is using False as default value for the optional arguments "left_index" and "right_index". However, its docstring states that the default value for both arguments is True instead.
Thanks and regards.
Manu.
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} | 0 | "2012-08-12T20:03:52Z" | "2012-09-18T20:22:29Z" | "2012-09-18T20:22:29Z" | MEMBER | null | e.g.
```
In [2]: mindex = MultiIndex.from_arrays([np.arange(5).repeat(5), np.tile(np.arange(5), 5)])
In [3]: pd.unique(mindex)
Out[3]: array([], dtype=object)
```
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} | 5 | "2012-08-13T09:34:02Z" | "2012-09-10T09:45:20Z" | "2012-09-10T03:27:18Z" | CONTRIBUTOR | null | Searching google for "pandas groupby" returns links to the 0.7.0 or older documentation on sourceforge.net in the first few places and only then the pydata links. It would be nice if sourceforge could be cleaned up, so that google returns links to the current docs :-)
Thanks!
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} | 0 | "2012-08-13T12:19:34Z" | "2012-08-13T21:50:47Z" | "2012-08-13T21:50:47Z" | CONTRIBUTOR | null | ```
df3 = DataFrame({"a":[1,2,3,4], "b":[1,2,3,4]})
df3
a b
0 1 1
1 2 2
2 3 3
3 4 4
df3.corr()
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "C:\portabel\Python27\lib\site-packages\pandas\core\frame.py", line 3867, in corr
correl = lib.nancorr(mat)
File "moments.pyx", line 284, in pandas.lib.nancorr (pandas\src\tseries.c:67792)
ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'long long'
```
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In [12]: s = pandas.Series(range(5), index=[0, 6, 20, 100, 134])
In [13]: s
Out[13]:
0 0
6 1
20 2
100 3
134 4
In [14]: s[1:3]
Out[14]:
6 1
20 2
```
Whereas `s.ix[]` works label based.
``` python
In [15]: s.ix[1:3]
Out[15]: Series([], dtype=int64)
```
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} | 0 | "2012-08-13T13:43:47Z" | "2012-08-17T18:37:41Z" | "2012-08-17T18:37:41Z" | CONTRIBUTOR | null | pandas version: 0.8.2.dev-742d7fb
``` python
import pandas
import pandas.util.testing as put
index = put.makeDateIndex(4)
index.isin(index) # returns array([False, False, False, False], dtype=bool)
```
It works fine with pandas 0.7.3, i.e. it returns an array of True instead. This works fine as well with a pandas.Int64Index. `index.isin(index)` just calls `pandas.lib.ismember(index._array_values(), set(index))` but I am afraid I lack the skills to understand what goes wrong in the cython code.
Just for some background, the original problem I bumped into:
``` python
import numpy as np
import pandas
ts = pandas.TimeSeries(data=[1,2,3], index=[ np.datetime64(x) for x in ['2000', '2000', '2002'] ])
ts.ix[ts.index] # returns an empty timeseries whereas ts[ts.index] works fine
```
I tracked it down to `ts.ix._get_item_iterable` which has a special case for when the index has duplicates which use pandas.TimeSeries.isin.
As an aside it looks like pandas.core.common._asarray_tuplesafe(index) is wrong when using numpy 1.6 so that ts.ix[ts.index] still would return an empty array if this bug was fixed, but I guess that's a numpy 1.6 datetime64 bug which I have seen mentioned in a few other places.
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} | 1 | "2012-08-14T11:58:32Z" | "2016-10-12T23:03:08Z" | "2012-11-04T21:21:59Z" | CONTRIBUTOR | null | As discussed in https://groups.google.com/group/pydata/browse_thread/thread/180249bd46ed1944 the following fails:
``` python
def test_to_timstamp_how():
months = pd.period_range('01-Aug-2012', periods=1, freq='M')
assert months[0].asfreq('S','S').to_timestamp() == months.to_timestamp(how='start')[0]
#
```
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``` python
p = pd.Panel(randint(0, 100, (4,5,2)),
pd.date_range('01-Jan-2012', periods=4, freq='MS').shift(15,'D'),
pd.date_range('01-Jan-2012', periods=5, freq='MS'))
```
``` python
def test_panel_slicing_axis2():
assert p.ix[:,:,0:1].shape == (4,5,1)
```
``` python
def test_panel_indexing_doesnt_transpose():
assert p.ix[:, 1, :].values == p.values[:, 1, :]
assert p.ix[:, :, 0].values == p.values[:, :, 0]
```
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} | 2 | "2012-08-14T17:08:18Z" | "2013-10-02T21:05:21Z" | "2013-09-21T13:28:37Z" | NONE | null | See http://stackoverflow.com/questions/11941492/selecting-rows-from-a-pandas-dataframe-with-a-compound-hierarchical-index
let's say I want to filter a DataFrame such that level[k] only contains the values x, y or z.
A vectorized possibility is:
```
df[np.in1d(df.index.labels[k], match([x, y, z], df.index.levels[k]))]
```
but I guess it would be nice to have some pandas method wrapping it or implementing it more efficiently.
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} | 2 | "2012-08-14T18:36:41Z" | "2014-06-30T23:58:04Z" | "2012-09-08T22:12:31Z" | CONTRIBUTOR | null | Minor docs correction for statsmodels and add statsmodels to intersphinx as per discussion in #1501.
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} | 0 | "2012-08-15T08:58:26Z" | "2012-09-08T21:11:58Z" | "2012-08-18T21:18:29Z" | NONE | null | converted to pull request #1782
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} | [] | closed | false | null | [] | null | 0 | "2012-08-15T13:45:05Z" | "2012-08-20T05:40:48Z" | "2012-08-20T05:40:48Z" | NONE | null | It would be really neat if the DatetimeIndex constructor took an argument "dayfirst" which would force it to interpret a date as DD-MM-YYYY or similar when presented with a sequence of strings.
What it should never do is assume different formats for different strings in the same sequence (the current behaviour):
- If the string can be interpreted as a "month first" date, i.e. <= 12 it's parsed this way
- Otherwise it's parse as a day first string
I.e. all strings in the sequence for which the day happens to be <= 12 are parsed as "month first" and the rest as "day first".
``` python
pandas.DatetimeIndex(['12/05/2007', '25/01/2008'])
<class 'pandas.tseries.index.DatetimeIndex'>
[2007-12-05 00:00:00, 2008-01-25 00:00:00]
Length: 2, Freq: None, Timezone: None
```
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} | 2 | "2012-08-15T14:25:07Z" | "2012-09-07T20:35:45Z" | "2012-08-16T09:58:09Z" | NONE | null | ```
print pandas.__version__
```
0.8.1
```
dtrange = pandas.date_range(start='1-1-2009', periods=31, freq='D')
raw = pandas.Series(np.arange(len(dtrange)), index=dtrange)
test1 = raw.resample('M', how='count', closed='left', label='left')
test2 = raw.resample('M', how='mean', closed='left', label='left')
print test1
```
2008-12-31 31
```
print test2
```
2008-12-31 15
2009-01-31 0
Freq: M
When closed on the right, the extra empty bin dissapears. Also note how it appears when resampling with a mean, but not when using count.
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} | 2 | "2012-08-15T14:35:02Z" | "2012-08-16T09:24:04Z" | "2012-08-15T19:44:38Z" | NONE | null | ```
print pandas.__version__
```
0.8.1
```
dtrange = pandas.date_range(start='1-1-2009', periods=21, freq='D')
raw = pandas.Series(np.arange(len(dtrange)), index=dtrange)
test1 = raw.resample('7D', how='count', closed='right')
print test1
```
2009-01-01 1
2009-01-08 7
2009-01-15 7
2009-01-22 6
```
test2 = raw.resample('7D', how='count', closed='left', label='left')
print test2
```
ValueError: Values falls before first bin
When trying to resample 21 days to three weeks i ran into the issue above. Closing to the right puts the 21 days in four bins, closing to the left (which i think does what i want) fails with the error above.
Changing the frequency from '7D' to 'W' makes the code run fine so it might have something to do with the 'custom' frequency length.
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} | 1 | "2012-08-16T03:01:34Z" | "2012-09-11T01:13:59Z" | "2012-09-11T01:11:58Z" | CONTRIBUTOR | null | Simple Example
``` python
import pandas as pd
start = datetime.datetime(1999, 3, 1, 5)
# end hour is less than start
end = datetime.datetime(2012, 7, 31, 4)
bad_ind = pd.date_range(start, end, freq="30min")
df = pd.DataFrame({'close':1}, index=bad_ind)
try:
df.resample('AS', 'sum')
except ValueError as e:
print e
```
Long example:
http://nbviewer.maxdrawdown.com/3344040/intraday%20binning%20error.ipynb
Tracking it down, it appears that the problem is that `_get_range_edges` carries the time over when downsampling intraday data. So when `generate_range` is called during the `DatetimeIndex` creation, the final bin doesn't pass the `while cur <= end` check.
Thinking about it, there are two issues.
1) `generate_range` should never output an index that doesn't include end. Maybe something
``` python
while True:
yield cur
# last
if cur >= end:
break
```
2) `_generate_range_edges` should generate a range that is perfectly divisible by the freq. For the downsampling, we'd have to change the time by adjusting the end time or just zeroing both out. I don't know how many rely on this behavior though.
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} | 1 | "2012-08-16T13:00:45Z" | "2013-12-04T00:57:58Z" | "2012-09-04T20:28:13Z" | NONE | null | `DataFrame.drop_duplicates()` does not properly handle array objects returned by `DataFrame.columns` (whether or not you use `DataFrame.columns.values` to get a NumPy array). If you compute
```
list(DataFrame.columns.values)
```
then it works, but this is needless overkill, especially when dealing with a large number of columns. Below is an example from IPython.
``` python
In [71]: dfrm = pandas.DataFrame({"A":[1,2,1,2,1,2], "B":[3,4,3,4,3,4], "C":[1,2,1,2,1,3]})
In [72]: dfrm
Out[72]:
A B C
0 1 3 1
1 2 4 2
2 1 3 1
3 2 4 2
4 1 3 1
5 2 4 3
In [73]: dfrm.drop_duplicates(dfrm.columns)
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (882, 0))
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (6442, 0))
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/home/espears/<ipython-input-73-bee9ee352073> in <module>()
----> 1 dfrm.drop_duplicates(dfrm.columns)
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/frame.pyc in drop_duplicates(self, cols, take_last)
2254 deduplicated : DataFrame
2255 """
-> 2256 duplicated = self.duplicated(cols, take_last=take_last)
2257 return self[-duplicated]
2258
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/frame.pyc in duplicated(self, cols, take_last)
2283
2284 duplicated = lib.duplicated(keys, take_last=take_last)
-> 2285 return Series(duplicated, index=self.index)
2286
2287 #----------------------------------------------------------------------
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/series.pyc in __new__(cls, data, index, dtype, name, copy)
286 else:
287 subarr = subarr.view(Series)
--> 288 subarr.index = index
289 subarr.name = name
290
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/_tseries.so in pandas._tseries.SeriesIndex.__set__ (pandas/src/tseries.c:73097)()
AssertionError: Index length did not match values
In [74]: dfrm.drop_duplicates(dfrm.columns.values)
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (882, 0))
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (6442, 0))
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/home/espears/<ipython-input-74-cb96df701a9b> in <module>()
----> 1 dfrm.drop_duplicates(dfrm.columns.values)
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/frame.pyc in drop_duplicates(self, cols, take_last)
2254 deduplicated : DataFrame
2255 """
-> 2256 duplicated = self.duplicated(cols, take_last=take_last)
2257 return self[-duplicated]
2258
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/frame.pyc in duplicated(self, cols, take_last)
2283
2284 duplicated = lib.duplicated(keys, take_last=take_last)
-> 2285 return Series(duplicated, index=self.index)
2286
2287 #----------------------------------------------------------------------
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/core/series.pyc in __new__(cls, data, index, dtype, name, copy)
286 else:
287 subarr = subarr.view(Series)
--> 288 subarr.index = index
289 subarr.name = name
290
/opt/epd/7.2-1/lib/python2.7/site-packages/pandas/_tseries.so in pandas._tseries.SeriesIndex.__set__ (pandas/src/tseries.c:73097)()
AssertionError: Index length did not match values
In [75]: dfrm.columns.values
Out[75]: array([A, B, C], dtype=object)
In [76]: list(dfrm.columns.values)
Out[76]: ['A', 'B', 'C']
In [77]: dfrm.drop_duplicates(list(dfrm.columns.values))
Out[77]:
A B C
0 1 3 1
1 2 4 2
5 2 4 3
```
FWIW:
```
In [91]: pandas.__version__
Out[91]: '0.7.3'
```
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} | 1 | "2012-08-17T09:15:45Z" | "2012-09-09T02:55:31Z" | "2012-09-09T02:55:31Z" | NONE | null | When compiling pandas 2 errors prevent the compiled installer from running:
In pandas.core.format (line 155) an unitintialised doc string is used:
**doc** += docstring_to_string
Easy solution:
if **doc**:
**doc** += docstring_to_string
else:
**doc** = docstring_to_string
In pandas.utils.decorators.py line 98:
docitems = [func.**doc** if func.**doc** else '', self.addendum]
func.**doc** = ''.join(docitems)
return func
becomes:
def **call**(self, func):
docitems = [func.**doc** if func.**doc** else '', self.addendum if self.addendum else '']
func.**doc** = ''.join(docitems)
return func
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} | [] | closed | false | null | [] | null | 2 | "2012-08-17T16:20:17Z" | "2012-08-17T17:08:10Z" | "2012-08-17T16:31:56Z" | CONTRIBUTOR | null | Wanting to convert my scripts from scikits.timeseries to pandas. Started out just exploring how pandas does stuff and stumbled on this problem.
In [163]: rng = date_range('01/01/2011', periods=72, freq='H')
In [164]: rng[:5]
Out[164]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2051-05-17 05:40:40.331386880, ..., 2051-10-30 21:40:40.331386880]
Length: 5, Freq: H, Timezone: None
Tried other start date formats...
In [165]: rng = date_range('Jan-01-2011', periods=72, freq='H')
In [166]: rng[:5]
Out[166]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2051-05-17 05:40:40.331386880, ..., 2051-10-30 21:40:40.331386880]
Length: 5, Freq: H, Timezone: None
Same problem. Tried a different start date:
In [167]: rng = date_range('Jan-01-2012', periods=72, freq='H')
In [168]: rng[:5]
Out[168]:
<class 'pandas.tseries.index.DatetimeIndex'>
[1881-08-08 06:31:32.912283648, ..., 1882-01-21 22:31:32.912283648]
Length: 5, Freq: H, Timezone: None
Version information:
pandas.**version**
Out[172]: '0.8.1'
np.**version**
Out[173]: '1.7.0.dev-ebe2fc5'
Kindest regards,
Tim
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} | 1 | "2012-08-17T17:05:32Z" | "2012-09-09T03:25:23Z" | "2012-09-09T03:25:23Z" | CONTRIBUTOR | null | As of pandas 0.8.1, the Period and PeriodIndex display uses the English abbreviations for the month. Suggest that ISO 8601, would be a better choice.
Annual Period is fine...
> z =PeriodIndex([Period('2012'), Period('2013')])
>
> z
> Out[182]:
> <class 'pandas.tseries.period.PeriodIndex'>
> freq: A-DEC
> [2012, ..., 2013]
> length: 2
Monthly though...
> In [185]: z =PeriodIndex([Period('2012-01'), Period('2012-02')])
>
> In [186]: z
> Out[186]:
> <class 'pandas.tseries.period.PeriodIndex'>
> freq: M
> [Jan-2012, ..., Feb-2012]
> length: 2
I think that 'z' instead should be:
> In [186]: z
> Out[186]:
> <class 'pandas.tseries.period.PeriodIndex'>
> freq: M
> [2012-01, ..., 2012-02]
> length: 2
Hourly currently is
> z =PeriodIndex([Period('2012-01-01 00', freq='H'), Period('2012-01-01 01', freq='H')])
>
> z
> Out[193]:
> <class 'pandas.tseries.period.PeriodIndex'>
> freq: H
> [01-Jan-2012 00:00, ..., 01-Jan-2012 01:00]
> length: 2
The ISO 8601 standard representation of hourly Periods would be:
> In [186]: z
> Out[186]:
> <class 'pandas.tseries.period.PeriodIndex'>
> freq: M
> [2012-01-01 00, ..., 2012-01-01 01]
> length: 2
Advantages:
Not English centric
Directly sortable chronologically without parsing
Kindest regards,
Tim
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} | 3 | "2012-08-17T17:39:32Z" | "2012-08-18T01:57:02Z" | "2012-08-18T01:56:03Z" | NONE | null | If you try to create a DatetimeIndex from a list of timezone aware datetimes, you get the exception: "Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True"
For example:
> > > import pandas, datetime, pytz
> > > d = [datetime.datetime(2012, 8, 19, tzinfo=pytz.timezone('US/Eastern'))]
> > > pandas.DatetimeIndex(d)
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} | 5 | "2012-08-17T17:41:15Z" | "2012-09-04T01:36:38Z" | "2012-09-04T01:36:38Z" | NONE | null | For example:
> > > dr = pandas.date_range('03/06/2012 00:00', periods=25, freq='W-FRI', tz='US/Eastern')
> > > print dr
> > > <class 'pandas.tseries.index.DatetimeIndex'>
> > > [2012-03-09 00:00:00, ..., 2012-08-24 01:00:00]
> > > Length: 25, Freq: W-FRI, Timezone: US/Eastern
This is midnight at the start, but 1am at the end. Shouldn't it be midnight all along, regardless of DST?
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https://api.github.com/repos/pandas-dev/pandas/issues/1779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1779/events | https://github.com/pandas-dev/pandas/issues/1779 | 6,294,796 | MDU6SXNzdWU2Mjk0Nzk2 | 1,779 | DatetimeIndex.values gives bad output | {
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> > > dr = pandas.date_range('03/06/2012 00:00', periods=25, freq='W-FRI', tz='US/Eastern')
> > > dr.values
> > > array([1970-01-16 67:00:00, 1970-01-16 234:00:00, 1970-01-16 145:00:00,
> > > 1970-01-16 63:00:00, 1970-01-16 229:00:00, 1970-01-16 142:00:00,
> > > 1970-01-16 57:00:00, 1970-01-16 217:00:00, 1970-01-16 136:00:00,
> > > 1970-01-16 43:00:00, 1970-01-16 217:00:00, 1970-01-16 122:00:00,
> > > 1970-01-16 33:00:00, 1970-01-16 203:00:00, 1970-01-16 123:00:00,
> > > 1970-01-16 32:00:00, 1970-01-16 198:00:00, 1970-01-16 109:00:00,
> > > 1970-01-16 21:00:00, 1970-01-16 187:00:00, 1970-01-16 104:00:00,
> > > 1970-01-16 12:00:00, 1970-01-16 177:00:00, 1970-01-16 91:00:00,
> > > 1970-01-16 03:00:00], dtype=datetime64[ns])
This happens whether the DatetimeIndex was timezone aware or naive. On the other hand, converting to a list works fine:
> > > list(dr)
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https://api.github.com/repos/pandas-dev/pandas/issues/1780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1780/events | https://github.com/pandas-dev/pandas/issues/1780 | 6,305,523 | MDU6SXNzdWU2MzA1NTIz | 1,780 | rolling_corr() doesn't handle nan well | {
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} | [] | closed | false | null | [] | null | 1 | "2012-08-18T14:00:08Z" | "2012-08-18T15:54:24Z" | "2012-08-18T15:54:24Z" | NONE | null | rolling_corr() produces a nan result for any window in which either input has a nan value. There is no dropna (or similar) parameter to rolling_corr() to alter this behavior. Contrast with corr(), which dops nans by default. Obviously the user could dropna() on the inputs before invoking rolling_corr(), but it would be convenient to be able to handle it within the method either by deafult (as in corr) or by parameter.
For example:
> > > import pandas, numpy
> > > df = pandas.DataFrame({'a': numpy.random.random(20), 'b': numpy.random.random(20)})
> > > df['a'][12] = numpy.nan
>>> df.a.corr(df.b)
0.067541528061417624
> > > pandas.rolling_corr(df.a, df.b, window=5)
> > > 0 NaN
> > > 1 NaN
> > > 2 NaN
> > > 3 NaN
> > > 4 -0.391790
> > > 5 -0.550723
> > > 6 -0.131031
> > > 7 0.292896
> > > 8 0.056880
> > > 9 0.440123
> > > 10 0.120214
> > > 11 -0.475006
> > > 12 NaN
> > > 13 NaN
> > > 14 NaN
> > > 15 NaN
> > > 16 NaN
> > > 17 0.476328
> > > 18 0.517736
> > > 19 0.886145
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https://api.github.com/repos/pandas-dev/pandas/issues/1781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1781/events | https://github.com/pandas-dev/pandas/issues/1781 | 6,308,037 | MDU6SXNzdWU2MzA4MDM3 | 1,781 | typo in the IO documentation | {
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} | [] | closed | false | null | [] | null | 1 | "2012-08-18T21:08:05Z" | "2012-08-18T23:43:55Z" | "2012-08-18T23:43:55Z" | CONTRIBUTOR | null | see [stackoverflow ](http://stackoverflow.com/questions/12021730/can-pandas-handle-variable-length-whitespace-as-column-delimeters/12022047#12022047) for more.
The [read_table documentation for white space](http://pandas.pydata.org/pandas-docs/dev/io.html#csv-text-files) should have a backslash in it.
wrong way:
data = read_table('sample.txt', sep = 's*')
right way:
data = read_table('sample.txt', sep = r'\s*')
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https://api.github.com/repos/pandas-dev/pandas/issues/1782 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1782/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1782/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1782/events | https://github.com/pandas-dev/pandas/pull/1782 | 6,308,082 | MDExOlB1bGxSZXF1ZXN0MjA5NTg3MQ== | 1,782 | console_encode() fails on unicode when using ipython-notebook | {
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} | 6 | "2012-08-18T21:14:54Z" | "2014-06-13T02:46:28Z" | "2012-09-09T01:15:28Z" | NONE | null | When running in ipnb (linux) I get:
> > > pandas.common.console_encode(u"\u05d0")
> > > '?'
I traced this back to sys.stdin.encoding being set to None rather then 'utf-8' like in the shell.
Attached is a fix for your consideration. the test is just a stub since mocking is required
to reproduce the bug when testing in the shell instead of inside ipnb.
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} | 3 | "2012-08-19T04:16:28Z" | "2012-09-18T17:25:12Z" | "2012-09-18T17:21:48Z" | CONTRIBUTOR | null | I ran into the following :
``` python
import pandas as pd
data = {}
columns = ['symbol', 'price']
df = pd.DataFrame(data, columns=columns)
df.symbol == 'AAPL'
# TypeError: Could not compare <type 'str'> type with Series
```
The problem being that an empty dataframe's dtype defaults to float. I've adjusted for it by checking the len of data and setting the dtype to object when at 0. But I feel like an empty DataFrame should act like an empty list, valid for all list operations. Which in lieu of #549, means casting the widest net possible with dtype=object.
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https://api.github.com/repos/pandas-dev/pandas/issues/1784 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1784/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1784/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1784/events | https://github.com/pandas-dev/pandas/pull/1784 | 6,313,701 | MDExOlB1bGxSZXF1ZXN0MjA5NzUzMA== | 1,784 | Str contains na | {
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} | 2 | "2012-08-19T17:32:42Z" | "2014-07-16T09:23:17Z" | "2012-09-09T00:41:37Z" | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1785 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1785/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1785/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1785/events | https://github.com/pandas-dev/pandas/pull/1785 | 6,316,354 | MDExOlB1bGxSZXF1ZXN0MjA5ODI4MQ== | 1,785 | Add expanding moment functions and related tests. | {
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} | 1 | "2012-08-20T00:03:45Z" | "2014-06-26T16:50:28Z" | "2012-09-08T21:06:53Z" | CONTRIBUTOR | null | Implement `expanding_` analogues to the `rolling_` moment functions available in `pandas/stats/moments.py` by calling the `rolling_` function with `window=len(arg)` and `min_periods` defaulting to the minimum number of data points to compute the statistic (`1` for most, `4` for kurtosis, etc.). Resolves Issue #849.
The only potentially surprising behavior is that some `expanding_` functions differ from their NumPy equivalents in whether they preserve `NaN`s. For example, `expanding_max(s)` is equivalent to `rolling_max(s, len(s), min_periods=1)`, but both produce different output from `s.cummax()`, as the latter always returns `NaN` for every `NaN` in `s`. I don't have a strong opinion on which output is correct, aside from thinking `rolling_` and `expanding_` should agree.
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} | 2 | "2012-08-20T03:34:54Z" | "2016-10-12T23:03:09Z" | "2012-09-18T20:13:17Z" | CONTRIBUTOR | null | I've been trying to get around an inconsistency in how pandas read_table() function handles two very similar tables. A few people have given some suggestions about how to handle this problem on [StackOverflow](http://stackoverflow.com/questions/12031532/possible-inconsistency-in-text-handling-of-pandas-read-table-function#comment16059573_12031532) and can replicate the error but nobody had a solution. So even though I don't have a detailed Idea of whats going on I wanted to post this here.
I have two files,[AY907538](https://dl.dropbox.com/u/1803062/AY907538.hmmdomtblout) and [AY942707](https://dl.dropbox.com/u/1803062/AY942707.hmmdomtblout) , that I try to load with read_table(). One works fine and the other gives an issue.
```
# filename:AY942707
# this will load with no problem
data = read_table('AY942707.hmmdomtblout', header=None, skiprows=3, sep=r'\s*')
## filename: AY907538
data = read_table('AY907538.hmmdomtblout', header=None, skiprows=3, sep=r'\s*')
```
The error report is here:
```
ValueError Traceback (most recent call last)
<ipython-input-17-e0dbd8eee3ac> in <module>()
2
3 #temp = get_dataset('AY907538.hmmdomtblout')
4 data = read_table('AY907538.hmmdomtblout', header=None, skiprows=3, sep=r'\s*')
5 #data = read_table('test.txt', header=None, skiprows=3, sep=r'\s*')
6 #data = read_table('test2.txt', header=None, skiprows=3, sep=r'\s*')
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in read_table(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze)
282 kwds['encoding'] = None
283
284 return _read(TextParser, filepath_or_buffer, kwds)
285
286 @Appender(_read_fwf_doc)
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(cls, filepath_or_buffer, kwds)
189 return parser
190
191 return parser.get_chunk()
192
193 @Appender(_read_csv_doc)
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.pyc in get_chunk(self, rows)
779 msg = ('Expecting %d columns, got %d in row %d' %
780 (col_len, zip_len, row_num))
781 raise ValueError(msg)
782
783 data = dict((k, v) for k, v in izip(self.columns, zipped_content))
ValueError: Expecting 26 columns, got 28 in row 6
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1787 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1787/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1787/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1787/events | https://github.com/pandas-dev/pandas/issues/1787 | 6,319,588 | MDU6SXNzdWU2MzE5NTg4 | 1,787 | import error with pyinstaller, related to pythoncom; pywin32 | {
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} | 2 | "2012-08-20T07:20:01Z" | "2014-07-24T12:52:30Z" | "2013-09-21T13:39:39Z" | NONE | null | I am using pywin32 217, python 2.7.2 and pandas developer version:
My script contains only one line:
import pandas as pd
When I run the compiled executable created using pyinstaller in debug mode, i get the following error:
_MEIPASS2 is NULL
archivename is C:\pyinstaller-devel\TestImport\dist\TestImport\TestImport.exe
No need to extract files to run; setting extractionpath to homepath
Already in the child - running!
manifestpath: C:\pyinstaller-devel\TestImport\dist\TestImport\TestImport.exe.manifest
Activation context created
Activation context activated
C:\pyinstaller-devel\TestImport\dist\TestImport\python27.dll
Manipulating evironment
PYTHONPATH=C:/pyinstaller-devel/TestImport/dist/TestImport
PYTHONHOME=C:/pyinstaller-devel/TestImport/dist/TestImport/
importing modules from CArchive
extracted iu
extracted struct
extracted archive
Installing import hooks
out00-PYZ.pyz
Running scripts
Traceback (most recent call last):
File "<string>", line 65, in <module>
File "E:\Software\Python\PyInstaller\pyinstaller-pyinstaller-2d4aecc\PyInstaller\loader\iu.py", line 386, in importHook
mod = _self_doimport(nm, ctx, fqname)
File "E:\Software\Python\PyInstaller\pyinstaller-pyinstaller-2d4aecc\PyInstaller\loader\iu.py", line 480, in doimport
exec co in mod.__dict__
File "C:\pyinstaller-devel\TestImport\build\pyi.win32\TestImport\out00-PYZ.pyz\win32com", line 6, in <module>
File "E:\Software\Python\PyInstaller\pyinstaller-pyinstaller-2d4aecc\PyInstaller\loader\iu.py", line 409, in importHook
raise ImportError("No module named %s" % fqname)
**ImportError: No module named pythoncom**
RC: -1 from pyi_rth_win32comgenpy
OK.
Deactivating activation context
Releasing activation context
Done
I get import error saying no module named pythoncom, to avoid this , i have also tried to explicitly import pythoncom in my script, the script runs fine but the error with executable still remains.
I have tried with earlier versions of pandas as well as pywin32 (i could go back to pywin 213 only)
I noticed in issue #1136 that the problem was resolved with developer version of pandas, but it doesn't work for me..
Any quick pointers will be really appreciated a lot.
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] | closed | false | null | [] | null | 0 | "2012-08-20T14:31:12Z" | "2012-08-22T18:38:29Z" | "2012-08-22T18:38:29Z" | CONTRIBUTOR | null | ``` python
In [132]: df
Out[132]:
A B
a 10 8
b 12 16
c 12 24
In [133]: df.dtypes
Out[133]:
A int64
B int64
In [134]: df.pct_change()
Out[134]:
A B
a NaN NaN
b 0.2 1.0
c 0.0 0.5
In [135]: df['A'].pct_change()
---------------------------------------------------------------------------
...
ValueError: Invalid dtype for padding
```
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} | 1 | "2012-08-20T16:04:24Z" | "2013-12-04T00:43:04Z" | "2012-09-10T18:40:25Z" | CONTRIBUTOR | null | I'm not sure whether this is something that should be fixed/changed or not, but right now infer_freq takes the first value in a DatetimeIndex and uses that to impose end of period assumptions. E.g., if I have
```
dates = sm.tsa.datetools.dates_from_range('1959Q2', '2009Q3')
pandas.Index(dates).inferred_freq
```
I get 'Q-JUN' just because of where the series starts. Would it be better to just use the default 'Q' -> 'Q-DEC'?
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} | 0 | "2012-08-20T18:06:19Z" | "2012-09-04T01:45:05Z" | "2012-09-04T01:45:05Z" | NONE | null | Creating a DatetimeIndex from a list of timezone-aware datetimes shift the times according to the offset of the timezone.
Using an easy way to get a list of datetimes by creating a DatetimeIndex and casting it to a list:
dr = pandas.date_range('2012-06-02', periods=10, tz=pytz.timezone('US/Eastern'))
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-06-02 00:00:00, ..., 2012-06-11 00:00:00]
Length: 10, Freq: D, Timezone: US/Eastern
pandas.DatetimeIndex(list(a))
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-06-02 04:00:00, ..., 2012-06-11 04:00:00]
Length: 10, Freq: None, Timezone: US/Eastern
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} | 0 | "2012-08-20T18:17:04Z" | "2012-09-09T17:16:52Z" | "2012-09-09T17:16:52Z" | CONTRIBUTOR | null | Was playing around with the new string functions and noticed this. Don't really know if it's a bug or expected
```
np.version.full_version
#'1.6.1'
df = pandas.DataFrame(['jack and jill','jesse and frank'])
split_df = df[0].str.split(r'\s+and\s+')
split_df.to_csv('test')
#<snip>
# 75 shape = values.shape
# 76 result = np.empty(shape, dtype=bool)
#---> 77 vec = lib.isnullobj(values.ravel())
# 78 result[:] = vec.reshape(shape)
# 79
#
#/usr/local/lib/python2.7/dist-packages/pandas-0.8.2.dev_3942eb0-py2.7-linux-x86_64.egg/pandas/lib.so in pandas.lib.isnullobj (pandas/src/tseries.c:5355)()
#
#ValueError: Does not understand character buffer dtype format string ('s')
from pandas import lib
lib.isnullobj(np.array(split_df.ix[0]))
#<snip>
#
#ValueError: Does not understand character buffer dtype format string ('s')
```
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} | 1 | "2012-08-20T18:50:10Z" | "2014-06-18T22:10:25Z" | "2012-09-08T21:10:54Z" | CONTRIBUTOR | null | This fixes the issue I described in #1741.
When running uWSGI with the `--optimize` option set to `2`, it passes the `-OO` option to the interpreter and then there are no docstrings and pandas errors out during import.
I also noticed there was a note about this [here](https://github.com/pydata/pandas/blob/master/pandas/util/decorators.py#L74).
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```
In [1162]: Period('2012', freq='A-DEC') - Period(2002', freq='A-DEC')
File "<ipython-input-1162-41a08553f136>", line 1
Period('2012', freq='A-DEC') - Period(2002', freq='A-DEC')
^
SyntaxError: invalid syntax
```
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} | 2 | "2012-08-21T01:30:35Z" | "2013-12-28T19:03:30Z" | "2012-11-20T19:29:33Z" | NONE | null | Currently, `data` should have a `__len__` method. However in some cases I'd like to pass a iterator with a known size (mostly in the case of database, where I can run the `SELECT SUM(...) FROM table`, for the size. Then `SELECT * FROM table` and pass the cursor as `data`.
Currently I need to create in memory copy of the rows in the cursor.
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} | 1 | "2012-08-21T14:14:07Z" | "2014-05-29T04:13:49Z" | "2014-05-29T04:13:49Z" | NONE | null | For my scientific experiments I have a lot of data which are time-related. The absolute date here is irrelevant, what matters is the time since the start of the test. I also get results from different devices, with different sampling intervals and have to combine those.
Now I do this by setting all experiments on a standard date, and adding the time difference with a timedelta. If I need the time since the start again I substract this standard date. This seems a bit unnecessary to me and complicates things, so would it be possible to have a list of timedeltas or times as indices, not datetimes.
some example data
# File 1
"File Number";"D0";"D1";"E"
0;303,014862061;303,014862061;0
1;303,014862061;303,028381348;4,46159210696e-05
2;303,014862061;302,939239502;-0,000249567150604
3;303,014862061;302,947784424;-0,000221367474296
# File 2
Count;Filename_0;Filename_1;Time_0;Time_1;load;;;;Voltage_0;Voltage_1;Voltage_2;Voltage_3
0;G:/Limess data/Maarten/Sabic\PB-0_23_1-0000_0.tif;G:/Limess data/Maarten/Sabic\PB- \ 0_23_1-0000_1.tif;2610,926;2610,937;6,416;4,883;0,000;0,000;6,421;0,000;-0,049;0,024
1;G:/Limess data/Maarten/Sabic\PB-0_23_1-0001_0.tif;G:/Limess data/Maarten/Sabic\PB- \ 0_23_1-0001_1.tif;2805,331;2805,343;0,190;0,000;0,000;0,000;0,190;0,000;-0,088;-0,127
2;G:/Limess data/Maarten/Sabic\PB-0_23_1-0002_0.tif;G:/Limess data/Maarten/Sabic\PB- \ 0_23_1-0002_1.tif;2805,570;2805,570;0,190;4,883;0,000;0,000;0,195;0,000;-0,088;-0,127
3;G:/Limess data/Maarten/Sabic\PB-0_23_1-0003_0.tif;G:/Limess data/Maarten/Sabic\PB- \ 0_23_1-0003_1.tif;2805,849;2805,877;0,186;4,883;0,000;0,000;0,190;0,000;-0,088;-0,127
# File 3
Time;Extension;Load;Compressive strain (Extension);Compressive stress
"0,00000";"0,00000";"0,35976";"0,00000";"0,01799"
"0,10000";"0,00019";"-0,46161";"0,00001";"-0,02308"
"0,20000";"0,00063";"0,18237";"0,00003";"0,00912"
"0,30000";"0,00168";"0,42205";"0,00007";"0,02110"
"0,40000";"0,00532";"5,28095";"0,00022";"0,26405"
"0,50000";"0,00821";"9,91459";"0,00034";"0,49573"
"0,60000";"0,00973";"9,33031";"0,00040";"0,46652"
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} | 1 | "2012-08-21T20:21:47Z" | "2012-09-18T21:30:14Z" | "2012-09-18T21:29:51Z" | CONTRIBUTOR | null | When using xs in combination with a MultiIndex, before it was not needed to specify the _level_ argument if _key_ started from level 0 and referred to consecutive levels (see also the examples described in #1684), now this no longer works and _level_ needs to be defined in case _key_ is a list.
``` python
In [74]: df
Out[74]:
A B C D
lvl0 lvl1 lvl2
foo one -1 -0.945315 0.355323 -1.292034 -0.241153
1 -0.798705 -0.155502 0.269164 2.659962
two -1 1.059727 -1.624291 -0.199047 -1.080560
1 0.937473 -0.726302 0.848377 0.450377
bar one -1 0.473692 0.135078 0.066102 -0.922182
1 -0.059887 0.306716 1.933680 -1.314217
two -1 -0.182526 -0.074111 -1.305636 -0.404485
1 -0.913486 1.758551 0.575856 0.560052
In [75]: df.xs(['foo', 'one'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-75-deeb03cdd75e> in <module>()
----> 1 df.xs(['foo', 'one'])
...
TypeError:
In [76]: df.xs(['foo', 'one'], level=[0, 1])
Out[76]:
A B C D
lvl2
-1 -0.945315 0.355323 -1.292034 -0.241153
1 -0.798705 -0.155502 0.269164 2.659962
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1797/events | https://github.com/pandas-dev/pandas/pull/1797 | 6,365,516 | MDExOlB1bGxSZXF1ZXN0MjEyMDg2OA== | 1,797 | Inplace in more places | {
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} | 1 | "2012-08-21T23:10:30Z" | "2014-06-20T12:13:03Z" | "2012-09-08T21:02:06Z" | CONTRIBUTOR | null | I found it confusing that you can set_index(inplace=True), but not reset_index(inplace=True), so I went ahead and added this capability for both Series and DataFrame
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} | 5 | "2012-08-22T09:09:49Z" | "2012-09-26T23:10:51Z" | "2012-09-26T23:10:51Z" | CONTRIBUTOR | null | Pandas 0.8.1:
```
import pandas as pd
a=pd.Series([0, 1, 3, 6])
a.mean(), a.std(), a.var()
a.values.mean(), a.values.std(), a.values.var()
```
I would expect both last lines to return the same result. However, the former returned:
```
(2.5, 2.6457513110645907, 7.0)
```
and the latter:
```
(2.5, 2.2912878474779199, 5.25)
```
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} | 3 | "2012-08-22T12:11:18Z" | "2012-09-11T02:40:30Z" | "2012-09-11T02:40:30Z" | CONTRIBUTOR | null | from [mailinglist](https://groups.google.com/forum/?fromgroups=#!topic/pystatsmodels/6n0wj9iLKTI)
cc @ruidc
```
With the following DataFrame:
df = pandas.DataFrame(index=[1, 10, 'C', 'E'], columns=[1, 2, 3])
df.ix[df.index[:-1]] works
df.ix[:-1] doesn't
df.ix[[1, 10]] does not work
df.ix[pandas.Index([1, 10], dtype=df.index.dtype)] works
```
```
df.ix[10] does not work
```
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} | 5 | "2012-08-22T12:16:11Z" | "2016-10-12T23:03:09Z" | "2013-09-21T13:36:28Z" | NONE | null | When I plot one column of a df as a bar, run twinx(), and plot another as a line, I get the following error:
(using pandas 0.7.0 and numpy 1.5.1)
In [94]: Traceback (most recent call last):
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/figure.py", line 886, in draw
func(_args)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/axes.py", line 1983, in draw
a.draw(renderer)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/axis.py", line 1041, in draw
ticks_to_draw = self._update_ticks(renderer)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/axis.py", line 931, in _update_ticks
tick_tups = [ t for t in self.iter_ticks()]
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/axis.py", line 878, in iter_ticks
majorLocs = self.major.locator()
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/dates.py", line 752, in __call__
self.refresh()
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/dates.py", line 761, in refresh
dmin, dmax = self.viewlim_to_dt()
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/dates.py", line 533, in viewlim_to_dt
return num2date(vmin, self.tz), num2date(vmax, self.tz)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/dates.py", line 292, in num2date
if not cbook.iterable(x): return _from_ordinalf(x, tz)
File "/intucell/IntuRMS/intustall/deps/osx/lib/python2.7/site-packages/matplotlib/dates.py", line 206, in _from_ordinalf
dt = datetime.datetime.fromordinal(ix)
ValueError: ordinal must be >= 1
If you suspect this is an IPython bug, please report it at:
https://github.com/ipython/ipython/issues
or send an email to the mailing list at [email protected]
You can print a more detailed traceback right now with "%tb", or use "%debug"
to interactively debug it.
Extra-detailed tracebacks for bug-reporting purposes can be enabled via:
%config Application.verbose_crash=True
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