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https://api.github.com/repos/pandas-dev/pandas/issues/2201
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PR: unicode, mostly
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| 1
| 2012-11-08T23:41:48Z
| 2014-06-16T22:34:21Z
| 2012-11-09T17:29:02Z
|
NONE
| null |
f2db4c1 fixes the `UnboundLocalError` mentioned in #2200.
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https://api.github.com/repos/pandas-dev/pandas/issues/2202
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MDU6SXNzdWU4MjMyNjY0
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timeseries: shortcut to unique
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| 4
| 2012-11-09T10:24:18Z
| 2012-11-09T23:52:59Z
| 2012-11-09T18:08:06Z
|
CONTRIBUTOR
| null |
what do you think is it worthwhile to add a shortcur to get unique values of a index:
hours = nanops.unique1d(series.index.hour)
nanops.unique1d(series.index.days)
|
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| 8,236,796
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MDExOlB1bGxSZXF1ZXN0Mjk0MzcwNQ==
| 2,203
|
link the info on scikits.timeseries
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| 0
| 2012-11-09T12:37:26Z
| 2013-12-04T00:57:52Z
| 2012-11-09T17:19:06Z
|
CONTRIBUTOR
| null |
link to the FAQ, devel pages and issue list.
|
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| 8,236,829
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MDExOlB1bGxSZXF1ZXN0Mjk0MzcyMA==
| 2,204
|
set link target for faq site
|
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| 2012-11-09T12:39:28Z
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MDU6SXNzdWU4MjQ0NzIy
| 2,205
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Add option to disable pandas-style tick locator and formatter for plotting
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| 0
| 2012-11-09T16:35:34Z
| 2012-11-12T04:39:39Z
| 2012-11-12T04:39:39Z
|
CONTRIBUTOR
| null |
so no conversion to Period (for compatibility with irregular timeseries on twinx etc)
|
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MDExOlB1bGxSZXF1ZXN0Mjk0ODExOA==
| 2,206
|
ENH: eliminate _str() in favor of pprint_thing
|
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| 1
| 2012-11-09T17:59:02Z
| 2012-11-09T19:12:37Z
| 2012-11-09T19:12:37Z
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NONE
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| 8,249,302
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MDU6SXNzdWU4MjQ5MzAy
| 2,207
|
MultiIndex with Heterogenous dtype
|
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| 5
| 2012-11-09T19:24:44Z
| 2013-01-20T19:51:45Z
| 2013-01-20T19:51:45Z
|
CONTRIBUTOR
| null |
I'm not sure if this is supposed to work or not.
I'm reading in a csv file:
`df = pd.read_csv('nc201052.dat', index_col=['FLOW', 'PERIOD', 'DECLARANT', 'PARTNER', 'PRODUCT_NC'], nrows=1000)`
```
df.head(5)
STAT_REGIME VALUE_1000ECU QUANTITY_TON SUP_QUANTITY
FLOW PERIOD DECLARANT PARTNER PRODUCT_NC
1 201052 001 3 01 4 41818.21 13419.4 0
4 01 4 17667.97 3939.6 0
5 01 4 6956.63 1181.9 0
6 01 4 44011.98 1031.2 0
7 01 4 7141.68 559.0 0
```
The 'DECLARANT' columns index type is 'object' (they coded one item as 'EU').
`df.index[0]` gives `(1, 201052, '001', 3, '01')`, but `df.ix[1, 201052, '001']` raises [this](https://gist.github.com/b735fad192ce878dc353) error.
When I only import the first 10 rows, the parser infers the "DECLARANT" column as an integer, and the slice works. That's what is leading me to guess that it's the type of the index that's messing things up.
Sorry if this isn't actually a bug. Still very new to python. Thanks!
|
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MDU6SXNzdWU4MjUwMzM2
| 2,208
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Test case for skipinitialspace
|
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| 4
| 2012-11-09T20:01:58Z
| 2012-11-17T21:37:22Z
| 2012-11-17T21:20:59Z
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MEMBER
| null |
https://gist.github.com/4016025
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|
Ability to handle fractional seconds in date_converters
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| 5
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| 2012-12-01T00:08:23Z
| 2012-11-30T23:58:49Z
|
MEMBER
| null |
cc @michaelaye
```
I have a 'second' column that contains microseconds like these:
In [52]: df.second.values
Out[52]:
array([ 4.50000000e-02, 4.50000000e-02, 4.50000000e-02, ...,
5.99920000e+01, 5.99920000e+01, 5.99920000e+01])
and had used this column to produce a DateTime index like this before:
d = dataframe
di = pandas.io.date_converters.parse_all_fields(
d.year, d.month, d.date, d.hour, d.minute, d.second)
but the resulting datetime index lost the microseconds:
MultiIndex
[(1.0, 1.0, <Timestamp: 2009-07-20 00:00:00>)
(1.0, 2.0, <Timestamp: 2009-07-20 00:00:00>)
(1.0, 3.0, <Timestamp: 2009-07-20 00:00:00>) ...,
(9.0, 19.0, <Timestamp: 2009-07-20 00:59:59>)
(9.0, 20.0, <Timestamp: 2009-07-20 00:59:59>)
(9.0, 21.0, <Timestamp: 2009-07-20 00:59:59>)]
Is there a way to keep the microseconds?
```
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Formatting DataFrame for input to SQL
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MEMBER
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xref: http://stackoverflow.com/questions/13116394/pandas-flatten-df-with-delimiter
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MEMBER
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New API for expressing pairwise column operations
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MEMBER
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http://stackoverflow.com/questions/13143050/calculating-subtractions-of-pairs-of-columns-in-pandas-dataframe
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MDU6SXNzdWU4MjUyMTY2
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Specify format for strptime in to_datetime
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| 2013-07-08T09:27:57Z
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MEMBER
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http://stackoverflow.com/questions/13133458/pandas-casting-iso-string-to-datetime64
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| 0
| 2012-11-09T23:34:41Z
| 2014-07-02T07:41:20Z
| 2012-11-14T00:02:28Z
|
NONE
| null |
fixes #1926 (partialy at least)
|
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| 8,255,863
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MDU6SXNzdWU4MjU1ODYz
| 2,215
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integrate scatter plot into DataFrame/Series plot (or scatter) method
|
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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| 0
| 2012-11-09T23:50:36Z
| 2013-10-17T12:50:56Z
| 2013-10-17T12:50:56Z
|
CONTRIBUTOR
| null |
from mailing list:
"I've been searching the API/Internet to find the easiest way to make call plt.scatter() with a dataframe. I figured there would be a keyword if df.plot(kind='scatter') or df.scatter() but this seems not to be the case. I saw from the API that kind only accepts three arguments:
kind : {‘line’, ‘bar’, ‘barh’}
Although this is out of date.
"
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| 8,262,225
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MDExOlB1bGxSZXF1ZXN0Mjk1NTQyNQ==
| 2,216
|
Small tweaks to vb_suite
|
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| 0
| 2012-11-10T14:23:31Z
| 2013-12-04T00:57:53Z
| 2012-11-13T23:51:46Z
|
NONE
| null |
I had a couple of false starts with vbench in the past, Hopefully these trivial changes
will help make it work out of the box for more people.
|
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| 8,267,497
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MDU6SXNzdWU4MjY3NDk3
| 2,217
|
Can't slice and dice time series df (pandas 0.9)
|
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| 2012-11-11T01:30:27Z
| 2012-11-11T03:22:01Z
| 2012-11-11T03:22:01Z
|
NONE
| null |
I'm usin pythonxy 2.7.3.0
# Create a dataframe with dates as your index
data = [1,2,3,4,5,6,7,8,9,10]
idx = date_range('1/1/2012', periods=10)
df = DataFrame(data, index=idx, columns=['Revenue'])
df
df[datetime(2012,1,1)]
---
KeyError Traceback (most recent call last)
<ipython-input-47-49bb4e9bc6db> in <module>()
2
3 print 'First record:'
----> 4 df[datetime(2012,1,1)]
5 #print df['2012'], '-'
6 #print df['Revenue'], '--'
C:\Python27\lib\site-packages\pandas\core\frame.pyc in **getitem**(self, key)
1702 raise ValueError('Cannot index using non-boolean DataFrame')
1703 else:
-> 1704 return self._get_item_cache(key)
1705
1706 def _getitem_array(self, key):
C:\Python27\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
472 return cache[item]
473 except Exception:
--> 474 values = self._data.get(item)
475 res = self._box_item_values(item, values)
476 cache[item] = res
C:\Python27\lib\site-packages\pandas\core\internals.pyc in get(self, item)
825
826 def get(self, item):
--> 827 _, block = self._find_block(item)
828 return block.get(item)
829
C:\Python27\lib\site-packages\pandas\core\internals.pyc in _find_block(self, item)
939
940 def _find_block(self, item):
--> 941 self._check_have(item)
942 for i, block in enumerate(self.blocks):
943 if item in block:
C:\Python27\lib\site-packages\pandas\core\internals.pyc in _check_have(self, item)
946 def _check_have(self, item):
947 if item not in self.items:
--> 948 raise KeyError('no item named %s' % str(item))
949
950 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: 'no item named 2012-01-01 00:00:00'
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MDU6SXNzdWU4MjcxMTAy
| 2,218
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df with dupe cols should raise KeyError on accessing non-existent col via list
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| 0
| 2012-11-11T12:18:35Z
| 2012-11-13T23:27:13Z
| 2012-11-13T23:27:13Z
|
NONE
| null |
``` python
from pandas import DataFrame
df=DataFrame([[1,2,3],[4,5,6]],columns=['a','a','b'])
try:
df[['baf']]
except KeyError:
pass
else:
self.fail("Dataframe failed to raise KeyError")
```
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MDU6SXNzdWU4MjcxMTIw
| 2,219
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df.iteritems() should yield Series even with non-unique column labels
|
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| 3
| 2012-11-11T12:21:29Z
| 2012-11-13T23:26:18Z
| 2012-11-13T23:26:18Z
|
NONE
| null |
``` python
df=DataFrame([[1,2,3],[4,5,6]],columns=['a','a','b'])
for k,v in df.iteritems():
self.assertEqual(type(v),Series)
```
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| 8,271,155
|
MDU6SXNzdWU4MjcxMTU1
| 2,220
|
1 ** Sparse df with nans makes sp_values() produce strange results under df[] vs df.take([])
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| 0
| 2012-11-11T12:26:32Z
| 2012-11-14T07:51:36Z
| 2012-11-13T23:51:13Z
|
NONE
| null |
``` python
df = SparseDataFrame({ 'A' : [nan, 0, 1] }) # note : no error without nan
df = 1 ** df # note that 2 ** df works fine, also df ** 1
r1=df.take([0],1)['A']
r2=df['A']
assert len(r2.sp_values) == len(r1.sp_values)
```
- I'm not familiar with the block management code so I can't figure this out
with reasonable effort right now.
- as a side issue, it looks like 1 *\* nan == 1 in this context, seems suspect.
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MDExOlB1bGxSZXF1ZXN0Mjk1ODA3Mw==
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|
fixes for #2218, #2219
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| 0
| 2012-11-11T12:30:20Z
| 2014-07-07T19:30:39Z
| 2012-11-13T23:27:47Z
|
NONE
| null |
4a5b75b (the fix for #2219) triggers the issue in #2220 which makes a test fail,
afiact, that's a genuine issue.
|
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MDExOlB1bGxSZXF1ZXN0Mjk1ODA5Ng==
| 2,222
|
beef-up tests of arith operators on panel and df
|
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| 2012-11-12T16:41:42Z
|
NONE
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MDExOlB1bGxSZXF1ZXN0Mjk1ODExMQ==
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|
CLN: use com._is_sequence instead of duplicating code
|
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| 2012-11-11T12:46:01Z
| 2012-11-12T16:23:20Z
| 2012-11-12T16:23:10Z
|
NONE
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| 8,274,181
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MDExOlB1bGxSZXF1ZXN0Mjk1ODg5Mw==
| 2,224
|
Unicode : change df.to_string() and friends to always return unicode objects
|
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| 2012-11-11T18:25:14Z
| 2014-06-13T01:01:53Z
| 2012-11-27T02:46:35Z
|
NONE
| null |
closes #2225
**Note**: Although all the tests pass with minor fixes, this PR has an above-average chance of
breaking things for people who have relied on broken behaviour thus far.
`df.tidy_repr` combines several strings to produce a result. when one component is unicode
and other other is a non-ascii bytestring, it tries to convert the latter back to a unicode string
using the 'ascii' codec and fails.
I suggest that `_get_repr` -> `to_string` should always return unicode, as implemented by this PR,
and that the `force_unicode` argument be deprecated everyhwere.
The `force_unicode` argument in `to_string` conflates two things:
- which codec to use to decode the string (which can only be a hopeful guess)
- whether to return a unicode() object or str() object,
The first is now no longer necessary since `pprint_thing` already resorts to the same hack
of using utf-8 (with errors='replace') as a fallback.
I believe making the latter optional is wrong, precisely because it brings about situations
like the test case above.
`to_string`, like all internal functions , should utilize unicode objects, whenever feasible.
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MDU6SXNzdWU4Mjc0MjY3
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BUG: series tidy_repr UnicodeDecodeError
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| 2014-04-05T19:32:40Z
| 2012-11-27T15:33:55Z
|
NONE
| null |
``` python
a=pd.Series([u"\u05d0"]*1000)
a.name= 'title1'
repr(a)
pandas/pandas/core/series.py in _tidy_repr(self, max_vals)
869 name=False)
870 result = head + '\n...\n' + tail
--> 871 return '%s\n%s' % (result, self._repr_footer())
872
873 def _repr_footer(self):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd7 in position 6: ordinal not in range(128)
```
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MDU6SXNzdWU4Mjc0OTc0
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Better error message in pandas.core.index
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| 2012-11-11T19:42:34Z
| 2012-11-12T04:37:38Z
| 2012-11-12T04:37:38Z
|
CONTRIBUTOR
| null |
If sortlevel is called with an out-of-range level, the error message is a bit confusing, particularly if the level is equal to the maximum number of levels.
```
# Example from "Python for Data Analysys", page 148
import pandas as pd
import numpy as np
frame = pd.DataFrame(np.arange(12).reshape((4,3)),
index=[list('aabb'), [1, 2, 1, 2]],
columns=[['Ohio','Ohio','Colorado'],['Green','Red','Green']])
frame.sortlevel(2)
.
.
.
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-
packages/pandas/core/index.pyc in _get_level_number(self, level)
1363 elif level >= self.nlevels:
1364 raise ValueError('Index has only %d levels, not %d'
-> 1365 % (self.nlevels, level))
1366 return level
1367
ValueError: Index has only 2 levels, not 2
```
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MDU6SXNzdWU4Mjc1MDAx
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SparseDataFrame.icol(j) returns Series rather then SparseSeries
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| 2012-11-14T16:50:29Z
| 2012-11-14T16:50:29Z
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NONE
| null |
``` python
In [38]: df=SparseDataFrame([[1,2],[4,5]],columns=['a','b'])
...: type(df.icol(0))
Out[38]: pandas.core.series.Series
```
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MDU6SXNzdWU4Mjc1MDg1
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df.icol([0]) fails when df has non-unique column labels
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| 0
| 2012-11-11T19:52:49Z
| 2012-11-14T00:10:35Z
| 2012-11-14T00:10:35Z
|
NONE
| null |
``` python
df=DataFrame([[1,2,3],[4,5,6]],columns=['a','b','b'])
df.icol([0])
pandas/pandas/core/index.pyc in get_indexer(self, target, method, limit)
747
748 if not self.is_unique:
--> 749 raise Exception('Reindexing only valid with uniquely valued Index '
750 'objects')
751
Exception: Reindexing only valid with uniquely valued Index objects
```
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MDU6SXNzdWU4Mjc1MjQ3
| 2,229
|
SparseSeries.from_array returns Series rather then SparseSeries
|
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| 2012-11-11T20:09:20Z
| 2012-11-14T16:50:46Z
| 2012-11-14T16:50:46Z
|
NONE
| null |
``` python
In [16]: s=SparseSeries.from_array(np.array([1,2,3]),index=Index(range(3)))
...: type(s)
...:
Out[16]: pandas.core.series.Series
```
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| 8,275,921
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MDExOlB1bGxSZXF1ZXN0Mjk1OTQ0Ng==
| 2,230
|
use boolean indexing via getitem to trigger masking; add inplace keyword to where
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| 16
| 2012-11-11T21:19:09Z
| 2014-06-12T19:22:25Z
| 2012-11-13T22:52:23Z
|
CONTRIBUTOR
| null |
in core/frame.py
changed method _getitem_ to use _mask_ directly (e.g. df.mask(df > 0) is equivalent semantically to df[df>0])
this would be a small API change as before df[df >0] returned a boolean np array
added inplace keyword to _where_ method (to update the dataframe in place, default is NOT to use inplace, and return a new dataframe)
changed method _boolean_set_ to use where and inplace=True (this allows alignment of the passed values and is slightly less strict than the current method)
all tests pass (as well as an added test in boolean frame indexing)
if included in 0.9.1 would be great (sorry for the late addition)
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MDExOlB1bGxSZXF1ZXN0Mjk2MDE0Ng==
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|
BUG: Incorrect error message due to zero based levels. #2226
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| 2012-11-12T00:44:52Z
| 2012-11-12T04:37:41Z
| 2012-11-12T04:37:37Z
|
NONE
| null |
Simple fix in error message that assume levels starting at 1 instead of 0.
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| 8,280,254
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MDU6SXNzdWU4MjgwMjU0
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|
Inconsistent behaviour of to_period
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| 2012-11-12T04:45:54Z
| 2016-10-12T23:03:19Z
| 2012-11-29T21:17:06Z
|
NONE
| null |
to_period works correctly on individual timestamp objects with timezone information. When it is applied to datetime indexes with timezone information though, the conversion appears to happen based on the underlying UTC timestamp.
For example:
index = date_range('2000-1-1', periods=1000, freq='D', tz = 'Australia/Sydney')
index[0].to_period('D') # gives a different result from:
index.to_period('D')[0]
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| 8,280,809
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MDU6SXNzdWU4MjgwODA5
| 2,233
|
Strict mode for date parsing/conversion
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| 4
| 2012-11-12T05:42:01Z
| 2013-09-21T13:20:47Z
| 2013-09-21T13:20:47Z
|
CONTRIBUTOR
| null |
Sometimes if there are invalid entries in a large series of datetime data the conversion fails but it's not clear why.
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MDU6SXNzdWU4Mjg5NjQz
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Can't create a DataFrame from an empty Series
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| 3
| 2012-11-12T13:56:11Z
| 2012-11-14T17:44:28Z
| 2012-11-14T17:44:28Z
|
NONE
| null |
For a project I am working on it would be convenient if you could create a DataFrame from an empty Series. I have some library code that will create a Series object constructed from a dict, most of the time this dict will have some entries, but occasionally it does not. Currently you can create an empty DataFrame by not supplying anything to the constructor, and you can create a DataFrame or a Series from an empty dict, but if you try to create a DataFrame from an Series constructed from an empty dict pandas throws an AssertionError. I can code around this easily enough, but it would be preferable just to return an empty DataFrame. Example code below:
``` python
In [73]: pandas.__version__
Out[73]: '0.9.0'
In [74]: df = DataFrame()
In [75]: df = DataFrame({})
In [76]: s = Series({}, name="foo")
In [77]: df = DataFrame(s)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-77-4c4337b321e1> in <module>()
----> 1 df = DataFrame(s)
/nfs/users/nfs_g/gr5/software/epd-7.2-2-rh5-x86_64/lib/python2.7/site-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
392 else:
393 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 394 copy=copy)
395 elif isinstance(data, list):
396 if len(data) > 0:
/nfs/users/nfs_g/gr5/software/epd-7.2-2-rh5-x86_64/lib/python2.7/site-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy)
504 columns = _ensure_index(columns)
505
--> 506 block = make_block(values.T, columns, columns)
507 return BlockManager([block], [columns, index])
508
/nfs/users/nfs_g/gr5/software/epd-7.2-2-rh5-x86_64/lib/python2.7/site-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in make_block(values, items, ref_items, do_integrity_check)
459
460 return klass(values, items, ref_items, ndim=values.ndim,
--> 461 do_integrity_check=do_integrity_check)
462
463 # TODO: flexible with index=None and/or items=None
/nfs/users/nfs_g/gr5/software/epd-7.2-2-rh5-x86_64/lib/python2.7/site-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in __init__(self, values, items, ref_items, ndim, do_integrity_check)
24
25 assert(values.ndim == ndim)
---> 26 assert(len(items) == len(values))
27
28 self.values = values
AssertionError:
```
Cheers,
Graham
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MDU6SXNzdWU4Mjk4MDE2
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prettier tick labelling etc for irregular timeseries
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| 1
| 2012-11-12T18:10:36Z
| 2018-06-27T18:06:49Z
| 2018-06-27T18:06:49Z
|
CONTRIBUTOR
| null |
xref: http://stackoverflow.com/questions/13347288/how-do-i-get-nice-tick-labels-for-an-irregularly-sampled-time-series
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Error converting DataFrame with duplicate columns to ndarray
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| 5
| 2012-11-12T20:34:27Z
| 2014-06-13T02:57:06Z
| 2012-11-13T21:54:20Z
|
NONE
| null |
Installed latest version of pandas 0.9.0 in case this was an error
Trying to read Excel file. That part seems ok.
Originally, I was trying iteritems() for each row of the pandas dataframe, as the id_company had to be verified against a mysql database (code not included). Same/similar error message to putting it into a tuple (code is below). Error message follows.
Note there is a .reindex() but it didn't work before, either. The reindex() was kind of a hail-mary.
As a work-around, I'm probably going to simply import from my target sql and do a join. I'm concerned because of the size of the datasets.
```
import pandas as pd
def runNow():
#identify sheet
source = 'C:\Users\jlalonde\Desktop\startup_geno\startupgenome_w_id_xl_20121109.xlsx'
xls_file = pd.ExcelFile(source)
sd = xls_file.parse('Sheet1')
source_u = sd.drop_duplicates(cols = 'id_company', take_last=False)
source_r = source_u[['id_company','id_good','description', 'website','keyword', 'company_name','founded_month', 'founded_year', 'description']]
source_i = source_r.reindex() #hail mary
tup_r = [tuple(x) for x in source_i.values]
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
sg_sql_2.runNow()
File "sg_sql_2.py", line 31, in runNow
tup_r = [tuple(x) for x in source_r.values]
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1443, in as_matrix
return self._data.as_matrix(columns).T
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 723, in as_matrix
mat = self._interleave(self.items)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 743, in _interleave
indexer = items.get_indexer(block.items)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 748, in get_indexer
raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects
```
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MDU6SXNzdWU4MzA2MjEw
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parallel_coordinates incorrect auto legend
|
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| 2
| 2012-11-12T22:43:41Z
| 2016-10-12T23:03:19Z
| 2012-11-14T19:36:35Z
|
CONTRIBUTOR
| null |
Just tried the latest version of the parallel coordinates plot, but the default legend appears to be incorrect: instead of just choosing the four expected groups, one legend entry is included per row, many of them set to "None".
To reproduce, follow steps in the [pandas guide](http://pandas.pydata.org/pandas-docs/stable/visualization.html#parallel-coordinates).
## System info:
Arch Linux 64-bit
pandas 0.9.1rc1 (git)
numpy 1.7.0b2
matplotlib 1.2.0
## Screenshot
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| 8,322,184
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MDU6SXNzdWU4MzIyMTg0
| 2,238
|
Aggregated datetime64 column converted to float
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| 3
| 2012-11-13T14:04:49Z
| 2012-11-14T00:14:59Z
| 2012-11-14T00:12:34Z
|
NONE
| null |
See this [Stackoverflow question](http://stackoverflow.com/questions/13361326/preserving-datetime-index-in-groupby-operation/13362096#13362096)
It appears that when grouping and then aggregating a datetime column the dtype converts to float
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MDU6SXNzdWU4MzMxMTQx
| 2,239
|
win py3.3 packages?
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| 8
| 2012-11-13T18:37:02Z
| 2013-07-29T03:44:16Z
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|
CONTRIBUTOR
| null |
Could you please also make pandas win py3.3 available at::
http://pypi.python.org/pypi/pandas/0.9.0
Thanks
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cannot write resampled frame to excel TypeError: 'Period' object does not support indexing
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| 1
| 2012-11-13T23:30:13Z
| 2016-10-12T23:03:19Z
| 2012-11-14T16:55:47Z
|
NONE
| null |
Excel_to function is not working with some dataframes with period index
# this works for a dataframe with timeseries index
close.to_excel("temp.xlsx")
# this does not work. fails with error shown above
monthly=close.resample('M', kind='period')
monthly.to_excel("temp.xlsx")
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MDExOlB1bGxSZXF1ZXN0Mjk5MDQ1NQ==
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Ignore terminal width when not running in an interactive shell
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| 0
| 2012-11-14T00:29:59Z
| 2014-07-04T07:51:38Z
| 2012-11-14T17:29:58Z
|
NONE
| null |
shell use with python/ipython is unaffected, but redirecting to a file
gives you all the columns unles print_config.max_columns is set.
#1610.
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| 10
| 2012-11-14T04:29:16Z
| 2014-06-12T10:20:37Z
| 2012-12-02T23:03:23Z
|
CONTRIBUTOR
| null |
## Panel4D
( note - this superseeds the prior branch, FDPanel and this is 0.9rc1 compatible)
Panel4D is like a Panel object, but provides 4 dimensions
labels, items, major_axis, minor_axis
instead of using a dict of Panels to hold data, the Panel4D provides a convenient represenation in pandas space with named dimensions to allow easy axis swapping and slicing
# testing
tests/test_panel4d.py provides a similar methodology to test_panel.py
Panel4D required an overhall of many methods in panel.py and one change in core/index.py (regarding multi-indexing)
almost all methods in a Panel are extended to Panel4D (with the exception in that Panel
now allows a multi-axis on axis 0)
docstrings need to be refreshed a bit and made a bit more general
all tests that are not skipped pass (tested with 0.9rc1)
join is a work in progress
# further
panelnd.py provides a factory function for creation of generic panel-like ND structures with custom named dimensions
(this works, but not fully tested - examples are in the docstring)
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MDU6SXNzdWU4MzQ1NDI0
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reset_index on DataFrame with PeriodIndex changes to ordinal representation
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| 0
| 2012-11-14T05:16:56Z
| 2012-11-23T05:43:17Z
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|
CONTRIBUTOR
| null |
Should be array of Period objects probably
Note this is due to PeriodIndex.values converting to array of ordinals
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MDExOlB1bGxSZXF1ZXN0Mjk5MjU4MA==
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BUG: ExcelWriter raises exception on PeriodIndex #2240
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| 2012-11-14T05:37:28Z
| 2014-07-15T12:47:03Z
| 2012-11-14T16:55:39Z
|
CONTRIBUTOR
| null |
convert to_timestamp first
otherwise roundtripping is hard and the output isn't all that useful for processing in Excel
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Exception during resample w/ tz
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| 1
| 2012-11-14T15:03:51Z
| 2014-10-22T10:46:09Z
| 2012-11-14T16:34:16Z
|
NONE
| null |
from pandas import date_range, Series
idx = date_range('2001-09-20 15:59','2001-09-20 16:00', freq='T', tz='Australia/Sydney')
s = Series([1,2], index=idx)
s.resample('D')
## In [4]: s.resample('D')
ValueError Traceback (most recent call last)
<ipython-input-4-5ce76d689c21> in <module>()
----> 1 s.resample('D')
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\core\generic.pyc in resample(self, rule, how, axis, fill_method, closed,
label, convention, kind, loffset, limit, base)
187 fill_method=fill_method, convention=convention,
188 limit=limit, base=base)
--> 189 return sampler.resample(self)
190
191 def first(self, offset):
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\tseries\resample.pyc in resample(self, obj)
66
67 if isinstance(axis, DatetimeIndex):
---> 68 rs = self._resample_timestamps(obj)
69 elif isinstance(axis, PeriodIndex):
70 offset = to_offset(self.freq)
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\tseries\resample.pyc in _resample_timestamps(self, obj)
180 axlabels = obj._get_axis(self.axis)
181
--> 182 binner, grouper = self._get_time_grouper(obj)
183
184 # Determine if we're downsampling
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\tseries\resample.pyc in _get_time_grouper(self, obj)
95
96 if self.kind is None or self.kind == 'timestamp':
---> 97 binner, bins, binlabels = self._get_time_bins(axis)
98 else:
99 binner, bins, binlabels = self._get_time_period_bins(axis)
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\tseries\resample.pyc in _get_time_bins(self, axis)
125
126 # general version, knowing nothing about relative frequencies
--> 127 bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed)
128
129 if self.closed == 'right':
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\lib.pyd in pandas.lib.generate_bins_dt64 (pandas\src\tseries.c:61444)()
ValueError: Values falls after last bin
|
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MDExOlB1bGxSZXF1ZXN0Mjk5OTAwMg==
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|
ENH: Import DataReader and Options into namespace
|
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| 2012-11-14T16:14:20Z
| 2012-11-14T16:19:19Z
| 2012-11-14T16:19:19Z
|
CONTRIBUTOR
| null |
Don't know if there's any reason not to import these. I thought maybe they'd be better off in the data namespace though. Thoughts?
|
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MDU6SXNzdWU4MzYyMTEz
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Detection of integer overflows in parsers
|
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| 0
| 2012-11-14T17:58:47Z
| 2012-11-28T04:09:40Z
| 2012-11-28T04:09:40Z
|
MEMBER
| null |
xref: http://stackoverflow.com/questions/13293810/import-pandas-dataframe-column-as-string-not-int
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MDU6SXNzdWU4MzYyMjA5
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Exception during tz_localize w/ empty Series
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| 1
| 2012-11-14T18:01:54Z
| 2012-11-14T20:22:25Z
| 2012-11-14T20:22:25Z
|
NONE
| null |
from pandas import Series
s = Series()
s.tz_localize('UTC')
Q:\GAARD\Prod\Apps\Pandas64\4.36\pandas\core\series.pyc in tz_localize(self, tz, copy)
2730 localized : TimeSeries
2731 """
-> 2732 new_index = self.index.tz_localize(tz)
2733
2734 new_values = self.values
AttributeError: 'Index' object has no attribute 'tz_localize'
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MDU6SXNzdWU4MzY3NzQ4
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icol() should propegate fill_value for sparse data frames
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| 0
| 2012-11-14T20:45:31Z
| 2012-11-15T00:37:59Z
| 2012-11-15T00:37:59Z
|
NONE
| null |
``` python
from pandas.sparse.api import SparseSeries,SparseDataFrame
from pandas import Series, DataFrame, bdate_range, Panel
data = {'A' : [0,1 ]}
iframe = SparseDataFrame(data, default_kind='integer')
print type(iframe['A'].sp_index)
print type(iframe.icol(0).sp_index)
```
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MDExOlB1bGxSZXF1ZXN0MzAwMzQ3Mg==
| 2,250
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BUG: icol() should propegate fill_value for sparse data frames #2249
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| 0
| 2012-11-14T20:46:34Z
| 2014-07-17T12:26:16Z
| 2012-11-15T00:38:09Z
|
NONE
| null |
#2249
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MDU6SXNzdWU4MzY3ODU1
| 2,251
|
BUG: icol() doesn't preserve index type on sparse DataFrames
|
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| 1
| 2012-11-14T20:48:59Z
| 2012-11-15T00:35:25Z
| 2012-11-15T00:35:25Z
|
NONE
| null |
``` python
from pandas.sparse.api import SparseSeries,SparseDataFrame
from pandas import Series, DataFrame, bdate_range, Panel
data = {'A' : [0,1 ]}
iframe = SparseDataFrame(data, default_kind='integer')
print type(iframe['A'].sp_index)
print type(iframe.icol(0).sp_index)
<type 'pandas._sparse.IntIndex'>
<type 'pandas._sparse.BlockIndex'>
```
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MDU6SXNzdWU4MzcyNzM1
| 2,252
|
Losing Nanosecond precision upon conversion to DatetimeIndex
|
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| 7
| 2012-11-14T23:24:59Z
| 2012-11-20T15:28:41Z
| 2012-11-20T15:28:41Z
|
NONE
| null |
All,
I am experiencing a problem when converting from a TimeSeries to a DatetimeIndex. In the code below, I create a TimeSeries of nanosecond precision TimeStamps. I then convert it to a DatetimeIndex and lose the nanosecond precision (but maintain the microseconds). This appears to be a bug as the resulting elements of DatetimeIndex are of time TimeStamp and can handle nanosecond precision. Please see the snippet below.
Any help/resolution would be greatly appreciated.
Thanks,
Joe
```
In [233]: t1 = pandas.Timestamp((1352934390*1000000000)+1000000+1000+1)
In [234]: t2 = pandas.Timestamp((1352934390*1000000000)+1000000+1000+5)
In [235]: timeseries = pandas.TimeSeries([t1,t2])
In [236]: timeseries[0]
Out[236]: <Timestamp: 2012-11-14 23:06:30.001001001>
In [237]: datetimeindex = pandas.DatetimeIndex(timeseries)
In [238]: datetimeindex[0]
Out[238]: <Timestamp: 2012-11-14 23:06:30.001001>
In [236]: timeseries[0]
Out[236]: <Timestamp: 2012-11-14 23:06:30.001001001>
```
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MDExOlB1bGxSZXF1ZXN0MzAwNjg0NQ==
| 2,253
|
CLN: df.iteritems() can use self.icol()
|
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| 1
| 2012-11-15T00:46:32Z
| 2012-11-17T02:55:45Z
| 2012-11-17T02:55:43Z
|
NONE
| null |
now that all the issues have been fixed.
|
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MDExOlB1bGxSZXF1ZXN0MzAwNzQ5MQ==
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update 0.9.1 whatsnew to add where/mask commentary
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| 1
| 2012-11-15T02:03:33Z
| 2014-06-26T19:47:36Z
| 2012-11-15T03:14:09Z
|
CONTRIBUTOR
| null |
update whatsnew docs for where/mask (prob could be added to main docs at some point)
|
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MDU6SXNzdWU4Mzg2NTc4
| 2,255
|
dtype=object not obeyed and integer converted to float
|
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| 0
| 2012-11-15T13:23:22Z
| 2012-11-24T00:32:19Z
| 2012-11-24T00:32:19Z
|
CONTRIBUTOR
| null |
reading http://pandas.pydata.org/pandas-docs/dev/gotchas.html it implies that using dtype=object should preserve the integer, passing the data via a numpy array preserves the type as I would like:
```
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.9.1'
In [3]: data = [(6260L, 20302010L), (6262L, None)]
In [4]: df = pandas.DataFrame(data, dtype=object)
In [5]: df
Out[5]:
0 1
0 6260 2.030201e+07
1 6262 NaN
In [6]: df.dtypes
Out[6]:
0 object
1 object
In [7]: type(df.values[0][1])
Out[7]: float
In [8]: df = pandas.DataFrame(data)
In [9]: df.dtypes
Out[9]:
0 int64
1 float64
In [10]: type(df.values[0][1])
Out[10]: numpy.float64
In [11]: df = pandas.DataFrame(pandas.np.array(data, dtype=object))
In [12]: df.dtypes
Out[12]:
0 object
1 object
In [13]: type(df.values[0][1])
Out[13]: long
```
The place where this seems to start breaking is in the call to maybe_convert_objects from _convert_object_array, in pandas.core.frame.py L 5221 (for 0.9.1)
```
In [3]: type(pandas.lib.maybe_convert_objects(pandas.np.array([20302010, None], dtype=object), try_float=False)[0])
Out[3]: numpy.float64
```
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| 8,388,237
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MDExOlB1bGxSZXF1ZXN0MzAxMzMzNQ==
| 2,256
|
Fixes - Block.repr unicode and docstring tweak
|
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| 1
| 2012-11-15T14:31:05Z
| 2014-06-27T13:41:00Z
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NONE
| null |
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MDU6SXNzdWU4Mzg4NTQx
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Regression issue with concat of panels in 0.9.1
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| 2
| 2012-11-15T14:40:30Z
| 2012-11-19T05:14:07Z
| 2012-11-19T05:14:07Z
|
CONTRIBUTOR
| null |
concat of panels with the same items and minor_axis (but different major_axis) on axis=2 worked in 0.8.1, but fails in 0.9.1
```
In [1]: import numpy as np
In [2]: import pandas
In [3]: pandas.__version__
Out[3]: '0.9.1'
In [4]: def make_panel():
...: index = 5
...: cols = 3
...: return pandas.Panel(dict([ ("Item%s" % x,pandas.DataFrame(np.random.randn(index,cols),index = [ "I%s" % i for i in range(index) ], columns = [ "C%s" % i for i in range(cols) ])) for x in ['A','B','C'] ]))
...:
In [5]: panel1 = make_panel()
In [6]: panel1
Out[6]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major) x 3 (minor)
Items: ItemA to ItemC
Major axis: I0 to I4
Minor axis: C0 to C2
In [7]: panel2 = make_panel()
In [8]: panel2 = panel2.rename_axis(dict([ (x,"%s_1" % x) for x in panel2.major_axis ]), axis=1)
In [9]: panel2
Out[9]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major) x 3 (minor)
Items: ItemA to ItemC
Major axis: I0_1 to I4_1
Minor axis: C0 to C2
In [10]: panel3 = make_panel()
In [11]: panel3 = panel2.rename_axis(dict([ (x,"%s_1" % x) for x in panel2.major_axis ]), axis=1).rename_axis(dict([ (x,"%s_1" % x) for x in panel2.minor_axis ]), axis=2)
In [12]: panel3
Out[12]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major) x 3 (minor)
Items: ItemA to ItemC
Major axis: I0_1_1 to I4_1_1
Minor axis: C0_1 to C2_1
```
this works in 0.8.1 & 0.9.1
```
In [14]: pandas.concat([ panel1, panel2 ], axis = 1, verify_integrity = True)
Out[14]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 10 (major) x 3 (minor)
Items: ItemA to ItemC
Major axis: I0 to I4_1
Minor axis: C0 to C2
```
fails under 0.9.1 (worked in 0.8.1)
```
In [16]: pandas.concat([ panel1, panel3 ], axis = 1, verify_integrity = True)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-16-72a0267c3f61> in <module>()
----> 1 pandas.concat([ panel1, panel3 ], axis = 1, verify_integrity = True)
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)
877 keys=keys, levels=levels, names=names,
878 verify_integrity=verify_integrity)
--> 879 return op.get_result()
880
881
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in get_result(self)
959 else:
960 new_data = self._get_concatenated_data()
--> 961 return self.objs[0]._from_axes(new_data, self.new_axes)
962
963 def _get_fresh_axis(self):
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in _from_axes(cls, data, axes)
241 items, major, minor = axes
242 return cls(data, items=items, major_axis=major,
--> 243 minor_axis=minor, copy=False)
244
245 def _init_dict(self, data, axes, dtype=None):
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in __init__(self, data, items, major_axis, minor_axis, copy, dtype)
221 mgr = data
222 elif isinstance(data, dict):
--> 223 mgr = self._init_dict(data, passed_axes, dtype=dtype)
224 copy = False
225 dtype = None
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in _init_dict(self, data, axes, dtype)
280 arrays.append(values)
281
--> 282 return self._init_arrays(arrays, items, axes)
283
284 def _init_arrays(self, arrays, arr_names, axes):
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in _init_arrays(self, arrays, arr_names, axes)
285 # segregates dtypes and forms blocks matching to columns
286 blocks = form_blocks(arrays, arr_names, axes)
--> 287 mgr = BlockManager(blocks, axes).consolidate()
288 return mgr
289
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check)
484
485 if do_integrity_check:
--> 486 self._verify_integrity()
487
488 @classmethod
/usr/local/lib/python2.7/site-packages/pandas-0.9.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _verify_integrity(self)
566 for block in self.blocks:
567 assert(block.ref_items is self.items)
--> 568 assert(block.values.shape[1:] == mgr_shape[1:])
569 tot_items = sum(len(x.items) for x in self.blocks)
570 assert(len(self.items) == tot_items)
AssertionError:
```
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MDU6SXNzdWU4Mzk0NDY4
| 2,258
|
Memory leak when subsetting and using timeit.Timer
|
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[] | null | 2
| 2012-11-15T17:40:34Z
| 2012-11-15T18:01:05Z
| 2012-11-15T18:01:05Z
|
NONE
| null |
When subsetting a Series object inside of a function that is being run with Python's timeit.Timer module, memory usage grows without bound. Tested with pandas 0.9.1 on Python 2.7.3 32-bit on Windows.
```
from timeit import Timer
def test():
for i in range(1000000):
s = pd.Series(np.random.randn(150))
s[0:100]
t = Timer(lambda: test())
t.timeit(1)
```
If I remove the line s[0:100] the problem disappears.
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MDU6SXNzdWU4Mzk1MTMx
| 2,259
|
icol([0]) fails with non-unique, integer columns
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| 1
| 2012-11-15T18:02:29Z
| 2012-12-14T02:41:34Z
| 2012-12-14T02:41:34Z
|
NONE
| null |
``` python
In [14]: df=pd.DataFrame([[1,2,3],[4,5,6]],columns=[1,1,2])
In [15]: df.icol([0])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-15-bb76389fbd62> in <module>()
----> 1 df.icol([0])
/home/user1/src/pandas/pandas/core/frame.pyc in icol(self, i)
1877 return self[name]
1878 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1879 (type(self).__name__, name))
1880
1881 def __setattr__(self, name, value):
/home/user1/src/pandas/pandas/core/frame.pyc in reindex(self, index, columns, method, level, fill_value, limit, copy)
2503 necessary. Setting to False will improve the performance of this
2504 method
-> 2505
2506 Examples
2507 --------
/home/user1/src/pandas/pandas/core/frame.pyc in _reindex_columns(self, new_columns, copy, level, fill_value, limit)
2590 levels are named. If None then the index name is repeated.
2591
-> 2592 Returns
2593 -------
2594 resetted : DataFrame
/home/user1/src/pandas/pandas/core/index.pyc in reindex(self, target, method, level, limit)
870 if self._join_precedence < other._join_precedence:
871 how = {'right': 'left', 'left': 'right'}.get(how, how)
--> 872 result = other.join(self, how=how, level=level,
873 return_indexers=return_indexers)
874 if return_indexers:
/home/user1/src/pandas/pandas/core/index.pyc in get_indexer(self, target, method, limit)
789 is_contained : ndarray (boolean dtype)
790 """
--> 791 value_set = set(values)
792 return lib.ismember(self._array_values(), value_set)
793
Exception: Reindexing only valid with uniquely valued Index objects
```
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MDU6SXNzdWU4Mzk2Mzcz
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Series append function drops timezone information
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| 2012-11-15T18:34:08Z
| 2012-12-10T15:33:03Z
| 2012-12-10T15:33:03Z
|
NONE
| null |
Except when one of the two series is empty; in that case a tz-aware Series is returned
Code to reproduce:
import numpy as np
import pandas as pd
rng1 = pd.date_range('1/1/2011 01:00', periods=1, freq='H')
rng2 = pd.date_range('1/1/2011 02:00', periods=1, freq='H')
ts1 = pd.Series(np.random.randn(len(rng1)), index=rng1).tz_localize('UTC')
ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2).tz_localize('UTC')
ts_result = ts1.append(ts2)
Result in IPython:
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: rng1 = pd.date_range('1/1/2011 01:00', periods=1, freq='H')
In [4]: rng2 = pd.date_range('1/1/2011 02:00', periods=1, freq='H')
In [5]: ts1 = pd.Series(np.random.randn(len(rng1)), index=rng1).tz_localize('UTC')
In [6]: ts1
Out[6]:
2011-01-01 01:00:00+00:00 1.870471
Freq: H
In [7]: ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2).tz_localize('UTC')
In [8]: ts2
Out[8]:
2011-01-01 02:00:00+00:00 1.266536
Freq: H
In [9]: ts_result = ts1.append(ts2)
In [10]: ts_result
Out[10]:
2011-01-01 01:00:00 1.870471
2011-01-01 02:00:00 1.266536
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MDExOlB1bGxSZXF1ZXN0MzAxNzg1Ng==
| 2,261
|
pytables selection enhancements (to close GH #1966)
|
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| 2012-11-15T19:09:12Z
| 2014-06-19T00:02:49Z
| 2012-11-15T20:35:39Z
|
CONTRIBUTOR
| null |
in pandas/io/pytables.py
1. added **str** (to do **repr**)
2. row removal in tables is much faster if rows are consecutive
3. added Term class, refactored Selection (this is backdwards compatible)
Term is a concise way of specifying conditions for queries, e.g.
```
Term(dict(field = 'index', op = '>', value = '20121114'))
Term('index', '20121114')
Term('index', '>', '20121114')
Term('index', ['20121114','20121114'])
Term('index', datetime(2012,11,14))
Term('index>20121114')
```
updated tests for same
this should close GH #1996
also potentially a starting point to do things like this:
df['df>0']...e.g allowing simple string expressions in criteria (Term is indpendent; I could move to a separete module.... - just a thought)
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MDU6SXNzdWU4Mzk4MTEy
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|
DataFrame.reset_index() loses timezone
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| 0
| 2012-11-15T19:11:09Z
| 2012-12-10T15:38:43Z
| 2012-12-10T15:38:43Z
|
NONE
| null |
When timezone-aware datetime.datetime objects are used as the index of a DataFrame via set_index(), undoing the operation with reset_index() loses the timezone.
After the round-trip, the datetime.datetime column becomes a datetime64[ns] column, which can be considered a feature, not a bug. The times are correctly converted to UTC (which is good if you're going to lose the timezone), but the loss of timezone information seems like a bug.
This has been tested against version 0.9.1 on Python 2.7 (Windows 32-bit)
## Code to recreate
```
import datetime, dateutil, pandas as pd
tzoffset = dateutil.tz.tzoffset(None, -5*60*60)
ts = datetime.datetime(2012,11,15,10,38,4,tzinfo=tzoffset)
print ts # this has a timezone
df = pd.DataFrame([[ts, 15]], columns=['ts', 'val'])
dfi = df.set_index('ts')
print dfi.index # this has a timezone
df2 = dfi.reset_index() # <-- This is where the timezone gets lost
print df2['ts'][0] # this doesn't have a timezone
print df2['ts'][0].to_datetime() # neither does this
```
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MDU6SXNzdWU4Mzk4Mzgy
| 2,263
|
read_csv dateparser empty date's are assigned today's date
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| 2
| 2012-11-15T19:20:13Z
| 2012-11-28T00:43:03Z
| 2012-11-28T00:43:03Z
|
NONE
| null |
Hi,
``` python
import StringIO
s = StringIO.StringIO("Date, test\n2012-01-01, 1\n,2")
pd.read_csv(s, parse_dates=["Date"])
Date test
0 2012-01-01 00:00:00 1
1 2012-11-15 00:00:00 2
```
second row should be None.
is this the default behaviour?
this is a quick fix for me
``` python
from dateutil.parser import parse as dateparser
def parse_date(d):
if d.strip():
return dateparser(d)
pd.read_csv(s, parse_dates=["Date"], date_parser=parse_date)
using pandas 0.9.1
```
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| 8,400,210
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MDExOlB1bGxSZXF1ZXN0MzAxODkzNA==
| 2,264
|
Pytables selection enhancement & docs update for HDF5 tables
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| 2012-11-15T20:16:53Z
| 2014-06-12T20:20:07Z
| 2012-11-24T18:09:04Z
|
CONTRIBUTOR
| null |
1. added **str** (to do **repr**)
2. row removal in tables is much faster if rows are consecutive
3. added Term class, refactored Selection (this is backwards compatible)
Term is a concise way of specifying conditions for queries, e.g.
```
Term(dict(field = 'index', op = '>', value = '20121114'))
Term('index', '20121114')
Term('index', '>', '20121114')
Term('index', ['20121114','20121114'])
Term('index', datetime(2012,11,14))
Term('index>20121114')
```
updated tests for same
this should close GH #1996
4. added docs for HDF5 table in io.html
5. append on a table that didn't exist was failing (because of testing of the index_kind attribute first - which may not exist)
fixed & added test
6. added create_table_index method to create indicies on tables (which, btw now works quite well as Int64 indicies are used as opposed to the Time64Col which has a bug); includes a check on the pytables version requirement
this should close GH #698
7. added min_itemsize as a paremeter to append; allows bigger default indexer columns upon table creation (even if you don't append something that big - but might later, avoid the truncation issue)
8. incorporated 0.9.1 whatsnew docs for where & mask into Indexing Section of main docs
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MDExOlB1bGxSZXF1ZXN0MzAxOTk1NA==
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BUG: loses nano precision when converting Timestamp objects #2252
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| 2012-11-15T21:19:02Z
| 2014-06-22T14:35:35Z
| 2012-11-23T21:11:39Z
|
CONTRIBUTOR
| null |
quick and dirty: add nanoseconds after conversion to datetimestruct #2252
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MDU6SXNzdWU4NDAyMzk0
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diff() behavior causes memory problems
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| 3
| 2012-11-15T21:23:40Z
| 2016-10-12T23:03:20Z
| 2012-11-17T02:57:28Z
|
NONE
| null |
diff(n) with a negative n used to be a recipe for obtaining a forward-looking diff. Now it causes memory problems.
The change was introduced in commit 06f74e5d06c4766ec59564374640c4758a72409d and "improved" in commit 0d6fb1b381ef3882ceb766fecb0e28a5d5c2bd2bw . The first commit produces unexpected results silently. The second commit causes an outright coredump with
**\* glibc detected **\* /usr/bin/python: double free or corruption (out): 0x000000000591db00 ***
This is a simple script to trigger this bug:
import pandas
import numpy as np
x = np.sin(np.arange(24))
x = x.reshape((4,6))
df = pandas.DataFrame(x,columns=['a','b','c','d','e','f'])
print df
df2 = -df.diff(-1)
print df2
The results used to be mathematically correct:
```
a b c d e f
```
0 0.000000 0.841471 0.909297 0.141120 -0.756802 -0.958924
1 -0.279415 0.656987 0.989358 0.412118 -0.544021 -0.999990
2 -0.536573 0.420167 0.990607 0.650288 -0.287903 -0.961397
3 -0.750987 0.149877 0.912945 0.836656 -0.008851 -0.846220
a b c d e f
0 -0.279415 -0.184484 0.080061 0.270998 0.212781 -0.041066
1 -0.257157 -0.236820 0.001249 0.238169 0.256118 0.038593
2 -0.214414 -0.270290 -0.077662 0.186368 0.279052 0.115177
3 NaN NaN NaN NaN NaN NaN
Now they are not even consistent column-by-column:
```
a b c d e f
```
0 0.000000 0.841471 0.909297 0.141120 -0.756802 -0.958924
1 -0.279415 0.656987 0.989358 0.412118 -0.544021 -0.999990
2 -0.536573 0.420167 0.990607 0.650288 -0.287903 -0.961397
3 -0.750987 0.149877 0.912945 0.836656 -0.008851 -0.846220
a b c d e f
0 NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 0.750987 0.691594 -0.003648 -0.695536 -0.747951 -0.112704
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| 8,413,152
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MDU6SXNzdWU4NDEzMTUy
| 2,267
|
Allow TimeSeries to be initialized using a data vector of type Series
|
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[] | null | 5
| 2012-11-16T08:01:24Z
| 2012-11-16T16:06:53Z
| 2012-11-16T14:37:14Z
|
NONE
| null |
Suppose one has the following data:
``` python
from pandas import Series, TimeSeries, date_range
foo = date_range('2012-1-1', '2012-1-4')
bar = Series(range(0,4))
```
Currently (as of pandas 0.9.1), initializing a `TimeSeries` object using `data` of type `Series` produces unexpected results---all data values are converted to `NaN`.
```
TimeSeries(data = bar, index = foo)
2012-01-01 NaN
2012-01-02 NaN
2012-01-03 NaN
2012-01-04 NaN
Freq: D
```
However, casting the `Series` object to a list produces intuitive results:
```
TimeSeries(data = bar.tolist(), index = foo)
2012-01-01 0
2012-01-02 1
2012-01-03 2
2012-01-04 3
Freq: D
```
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| 8,425,347
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MDU6SXNzdWU4NDI1MzQ3
| 2,268
|
Duplicate row indices (bootstrapped dataframes)
|
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| 5
| 2012-11-16T17:00:04Z
| 2016-12-29T11:24:30Z
| 2016-12-29T11:24:30Z
|
NONE
| null |
I'm running into issues using bootstrap samples of the rows of a DataFrame. Example:
<pre>
import pandas
import numpy
numpy.random.seed(1)
df=pandas.DataFrame({'a':numpy.random.randn(20),'b':numpy.random.randn(20)})
nrows=df.shape[0]
# make a bootstrap sample of the rows (nrows indices with replacement)
boot_indxs=numpy.random.random_integers(0,nrows-1,nrows)
# make a dataframe with sampled rows
df2=df.irow(boot_indxs)
print df2
# this complains but that's ok since row 5 of df wasn't sampled
print df2.ix[:5,:]
# this complains, but perhaps it shouldn't since I'm just asking for the first nine
# rows of the data frame
print df2.irow(range(10))
# this works however which is great
print df2.irow(slice(10))
# the problem is then accessing rows without slices
# use case: taking a random subset of the rows of the bootstrapped dataframe
# to fit a predictive model and test it on the remaining rows
print df2.irow(numpy.random.permutation(range(nrows))[:15])
</pre>
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MDU6SXNzdWU4NDI1NDE5
| 2,269
|
In read_csv, header=0 and passed names should discard first row
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| 1
| 2012-11-16T17:02:12Z
| 2012-11-27T02:38:42Z
| 2012-11-27T02:38:42Z
|
MEMBER
| null |
Currently does not
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MDU6SXNzdWU4NDI1NDkw
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usecols conflicts with leading columns handling in read_csv
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| 2
| 2012-11-16T17:04:34Z
| 2012-11-18T18:53:34Z
| 2012-11-18T18:53:34Z
|
MEMBER
| null |
E.g. 3-column file, `usecols=[1, 2]`
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MDExOlB1bGxSZXF1ZXN0MzAzMTc1Mw==
| 2,271
|
ENH: pprint_thing escapes tabs. close #2038
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| 1
| 2012-11-16T17:20:54Z
| 2012-11-17T03:53:16Z
| 2012-11-17T03:53:16Z
|
NONE
| null |
another win for DRY.
|
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| 8,428,085
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MDU6SXNzdWU4NDI4MDg1
| 2,272
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datetime64 ns representation error in Series repr
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| 2
| 2012-11-16T18:41:09Z
| 2012-11-18T22:04:18Z
| 2012-11-18T22:04:18Z
|
MEMBER
| null |
Will post example
|
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| 8,429,456
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MDU6SXNzdWU4NDI5NDU2
| 2,273
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Significant performance degradation in 0.9.1 for SparseDataFrame methods like to_dense() and save() and for arithmetic operations
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| 4
| 2012-11-16T19:32:08Z
| 2012-11-17T02:53:26Z
| 2012-11-17T02:53:26Z
|
NONE
| null |
This is what I have in version 0.9.0:
```
import pandas as pd
pd.__version__
```
> '0.9.0'
```
barra_industry_exposures
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 253738 entries, (20061229, '00036110') to (20120928, 'Y8564W10')
> Data columns:
> MINING_METALS 253738 non-null values
> GOLD 253738 non-null values
> FORESTRY_PAPER 253738 non-null values
> CHEMICAL 253738 non-null values
> ENERGY_RESERVES 253738 non-null values
> OIL_REFINING 253738 non-null values
> OIL_SERVICES 253738 non-null values
> FOOD_BEVERAGES 253738 non-null values
> ALCOHOL 253738 non-null values
> TOBACCO 253738 non-null values
> HOME_PRODUCTS 253738 non-null values
> GROCERY_STORES 253738 non-null values
> CONSUMER_DURABLES 253738 non-null values
> MOTOR_VEHICLES 253738 non-null values
> APPAREL_TEXTILES 253738 non-null values
> CLOTHING_STORES 253738 non-null values
> SPECIALTY_RETAIL 253738 non-null values
> DEPARTMENT_STORES 253738 non-null values
> CONSTRUCTION 253738 non-null values
> PUBLISHING 253738 non-null values
> MEDIA 253738 non-null values
> HOTELS 253738 non-null values
> RESTAURANTS 253738 non-null values
> ENTERTAINMENT 253738 non-null values
> LEISURE 253738 non-null values
> ENVIRONMENTAL_SERVICES 253738 non-null values
> HEAVY_ELECTRICAL_EQUIPMENT 253738 non-null values
> HEAVY_MACHINERY 253738 non-null values
> INDUSTRIAL_PARTS 253738 non-null values
> ELECTRICAL_UTILITY 253738 non-null values
> GAS_WATER_UTILITY 253738 non-null values
> RAILROADS 253738 non-null values
> AIRLINES 253738 non-null values
> FREIGHT 253738 non-null values
> MEDICAL_SERVICES 253738 non-null values
> MEDICAL_PRODUCTS 253738 non-null values
> DRUGS 253738 non-null values
> ELECTRONIC_EQUIPMENT 253738 non-null values
> SEMICONDUCTORS 253738 non-null values
> COMPUTER_HARDWARE 253738 non-null values
> COMPUTER_SOFTWARE 253738 non-null values
> DEFENCE_AEROSPACE 253738 non-null values
> TELEPHONE 253738 non-null values
> WIRELESS 253738 non-null values
> INFORMATION_SERVICES 253738 non-null values
> INDUSTRIAL_SERVICES 253738 non-null values
> LIFE_HEALTH_INSURANCE 253738 non-null values
> PROPERTY_CASUALTY_INSURANCE 253738 non-null values
> BANKS 253738 non-null values
> THRIFTS 253738 non-null values
> ASSET_MANAGEMENT 253738 non-null values
> FINANCIAL_SERVICES 253738 non-null values
> INTERNET 253738 non-null values
> REITS 253738 non-null values
> BIOTECH 253738 non-null values
> dtypes: int64(55)
```
sparse = barra_industry_exposures.to_sparse(fill_value=0)
sparse
```
> <class 'pandas.sparse.frame.SparseDataFrame'>
> MultiIndex: 253738 entries, (20061229, '00036110') to (20120928, 'Y8564W10')
> Columns: 55 entries, AIRLINES to WIRELESS
> dtypes: float64(55)
```
%timeit sparse / 100.
```
> 100 loops, best of 3: 6.64 ms per loop
```
%timeit sparse.to_dense()
```
> 10 loops, best of 3: 127 ms per loop
```
%timeit sparse.save('test.pkl')
```
> 1 loops, best of 3: 16.9 ms per loop
Now this is what I get in 0.9.1:
```
import pandas as pd
pd.__version__
```
> '0.9.1'
```
%timeit sparse / 100.
```
> 1 loops, best of 3: 92.2 s per loop
```
%timeit sparse.to_dense()
```
> 1 loops, best of 3: 99.8 s per loop
```
%timeit sparse.save('test.pkl')
```
> 1 loops, best of 3: 100 s per loop
So, in the new version SparseDataFrame methods that used to run in less than 7-130 ms now run in more than 90 s. Ouch! What happened?
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|
Added support for specifying colors in parallel_plot()
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| 6
| 2012-11-16T20:52:32Z
| 2012-11-19T00:27:59Z
| 2012-11-19T00:11:39Z
|
CONTRIBUTOR
| null |
Sometimes the random colors chosen in parallel_plot are very unfortunate, so I thought it would be nice to provide an option to pass a list of colors in.
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| 12
| 2012-11-16T21:57:13Z
| 2013-04-19T14:51:49Z
| 2012-11-30T23:30:45Z
|
NONE
| null |
Printing a `pandas.DataFrame` in IPython's qtconsole tries to use `_repr_html_`. This method returns `None` if the terminal window that started the IPython session is too small.
I'm not sure this behaviour is right. When you're in the qtconsole, `_repr_html_` should check the size of the qtconsole window and not the the size of the terminal that started the session.
Here are some steps to reproduce the issue:
1. Create a very small terminal window and run `ipython qtconsole`.
2. Create a `pandas.DataFrame` called `df`.
3. Run `df.head()` in IPython. You should notice that the HTML representation of the table is not used.
4. Maximize the small terminal window from step 1.
5. Run `df.head()` in IPython. The HTML representation is now used.
I would guess the easiest way to fix this would be to add a qtconsole "os" in `get_terminal_size` in `util/terminal.py` and use Qt hooks to figure out the dimensions of the qtconsole.
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ENH: add encoding/decoding error handling
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| 1
| 2012-11-16T21:58:47Z
| 2014-06-12T13:19:13Z
| 2012-11-17T03:50:06Z
|
CONTRIBUTOR
| null |
When encoding/decoding strings with errors allow you to pass error handling strings. This works the same as error handling for other encode/decode functions. Defaults to 'strict', but you can pass 'ignore', 'replace'.
Extends work done in #1706.
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MDU6SXNzdWU4NDM3MTI0
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Series.reset_index not working with "inplace=True"
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| 4
| 2012-11-17T01:31:18Z
| 2012-11-23T05:53:42Z
| 2012-11-23T05:53:31Z
|
NONE
| null |
When I call reset_index on a Series object with arguments inplace=True, it does not work. See the code example below. Am I missing something here? (I am using "0.9.0", checked with pandas.**version**)
``` python
ts_a = pandas.Series([1]*3+[2]*3+[3]*4, range(10))
ts_b = pandas.Series([2]*3+[3]*3+[1]*4, range(10))
ts_c = pandas.Series(numpy.random.randn(10), range(10))
df = pandas.DataFrame({"A": ts_a, "B": ts_b, "C":ts_c})
df = df.set_index(['A', 'B'])['C'] # I select column C here to have a Series object
# print df
# A B
#1 2 1.032
# 2 0.139
# 2 -0.708
#2 3 -1.268
# 3 0.138
# 3 0.497
#3 1 0.973
# 1 -0.634
# 1 -0.112
# 1 -0.997
# now let's reset index 'B': inplace does not work?
df.reset_index('B', inplace=True)
# print df
# A B
#1 2 1.032
# 2 0.139
# 2 -0.708
#2 3 -1.268
# 3 0.138
# 3 0.497
#3 1 0.973
# 1 -0.634
# 1 -0.112
# 1 -0.997
# when copied, it does work
df = df.reset_index('B')
# print df
# Name: C
# B C
# A
#1 2 1.032
#1 2 0.139
#1 2 -0.708
#2 3 -1.268
#2 3 0.138
#2 3 0.497
#3 1 0.973
#3 1 -0.634
#3 1 -0.112
#3 1 -0.997
```
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MDU6SXNzdWU4NDM3NjUx
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Unstack is not careful about potential memory use problems
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| 0
| 2012-11-17T02:32:14Z
| 2012-11-22T20:34:04Z
| 2012-11-22T20:34:04Z
|
MEMBER
| null |
Cartesian product problem; unstacks relative to hypothetical possibilities instead of observed combinations. I'm claiming this unless someone else wants to look inside the reshape code
```
import pandas as pd
import numpy as np
# Generate Long File & Test Pivot
NUM_ROWS = 1000000
df = pd.DataFrame({'A' : np.random.randint(100, size=NUM_ROWS),
'B' : np.random.randint(300, size=NUM_ROWS),
'C' : np.random.randint(-7, 7, size=NUM_ROWS),
'D' : np.random.randint(-19,19, size=NUM_ROWS),
'E' : np.random.randint(3000, size=NUM_ROWS),
'F' : np.random.randn(NUM_ROWS)})
df_pivoted = df.pivot_table(rows=['A', 'B', 'C'], cols='E', values='F')
df_pivoted
```
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Easy vbench testing of HEAD against a known-good baseline
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| 2012-11-25T04:52:55Z
| 2012-11-23T16:33:53Z
|
NONE
| null |
I hope to avoid more performance regressions in the future, in my own PR's
if no one else's...
Her'es some example output:
```
groupby_apply_dict_return 33.20 33.26 1.00
frame_reindex_axis1 2.32 2.32 1.00
frame_fillna_inplace 18.34 18.31 1.00
frame_ctor_nested_dict_int64 106.89 106.40 1.00
index_int64_union 94.21 93.68 1.01
write_csv_standard 309.77 307.62 1.01
groupby_multi_different_functions 16.72 16.60 1.01
frame_ctor_nested_dict 75.67 74.62 1.01
groupby_multi_python 54.00 53.18 1.02
frame_to_csv 356.20 347.55 1.02
groupby_multi_cython 21.21 20.63 1.03
groupby_indices 9.22 8.95 1.03
frame_ctor_list_of_dict 95.60 91.88 1.04
series_ctor_from_dict 2.84 2.65 1.07
sort_level_zero 6.46 6.00 1.08
groupby_simple_compress_timing 70.36 62.56 1.12
Columns: test_name | head_time [ms] | baseline_time [ms] | ratio
- a Ratio of 1.30 means HEAD is 30% slower then the Baseline.
Head [951d13b] : ENH: Add test_perf.sh, make vbench dead easy.
Baseline [bdbca8e] : BUG: fix borked data-copying iteritems performance affecting DataFrame and SparseDataFrame. close #2273
*** Results were also written to the logfile at '/home/user1/src/pandas/vb_suite.log'
```
benchmarks for which HEAD takes less then 1ms are dropped.
## Remaining issues
1) vb_suite/join_merge.py contains two tests named 'join_dataframe_index_single_key_bigger'
, easy enough to fix, but that might break existing dbs, i'm not sure, so I left it alone.
2) Runner seems to choke on refs which are not an ancestor of the current HEAD
this looks like a shortcoming in vbench.GitRepo.
example trace:
``` python
Traceback (most recent call last):
File "test_perf.py", line 134, in <module>
main()
File "test_perf.py", line 94, in main
runner._run_and_write_results(h_baseline)
File "/usr/local/lib/python2.7/dist-packages/vbench/runner.py", line 87, in _run_and_write_results
n_active_benchmarks, results = self._run_revision(rev)
File "/usr/local/lib/python2.7/dist-packages/vbench/runner.py", line 119, in _run_revision
need_to_run = self._get_benchmarks_for_rev(rev)
File "/usr/local/lib/python2.7/dist-packages/vbench/runner.py", line 173, in _get_benchmarks_for_rev
timestamp = self.repo.timestamps[rev]
File "/home/user1/src/pandas/pandas/core/series.py", line 470, in __getitem__
return self.index.get_value(self, key)
File "/home/user1/src/pandas/pandas/core/index.py", line 692, in get_value
raise e1
KeyError: 'bdbca8e'
```
3) redirecting stdout/stderr does no work, maybe because os.system is used
under the covers. It frustrated my efforts funnel the crud into a log file,
and keep the output readable.
4) it would be nice if vbench.db.BenchmarkDB.get_ref_result() returned a fully
labeled DataFrame, rather then just a partial dict (test names are not included)
Something like:
``` python
def get_rev_full_results(self, revision):
tab = self._results
bench = self._benchmarks
stmt = sql.select([tab.c.timestamp, tab.c.revision, tab.c.ncalls,
tab.c.timing, tab.c.traceback, tab.c.description,
tab.c.checksum,bench.c.name],
sql.and_(tab.c.revision == revision,tab.c.checksum == bench.c.checksum))
results = self.conn.execute(stmt)
df = _sqa_to_frame(results).set_index('name')
return df.sort_index()
```
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| 8,452,058
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MDU6SXNzdWU4NDUyMDU4
| 2,280
|
test_transform_broadcast failure on ubuntu 13.04
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| 4
| 2012-11-18T17:35:32Z
| 2012-11-19T20:30:49Z
| 2012-11-19T01:41:28Z
|
NONE
| null |
on ubuntu 13.04 test_transform_broadcast fails due to rounding issues.
I'm not sure if this is a bug in the test or if some underlying component may have changed to cause rounding errors when there were non before.
a possible reason might be numpy 1.7 because the test works fine in ubuntu 12.10 with numpy 1.6
see also https://launchpadlibrarian.net/122195559/buildlog_ubuntu-raring-i386.pandas_0.8.1-1_FAILEDTOBUILD.txt.gz
it affects git HEAD too.
```
test_transform_broadcast (__main__.TestGroupBy) ... > /usr/lib/python2.7/unittest/case.py(425)assertTrue()
-> raise self.failureException(msg)
(Pdb) bt
/usr/lib/python2.7/unittest/case.py(332)run()
-> testMethod()
/tmp/pandas/pandas/tests/test_groupby.py(404)test_transform_broadcast()
-> self.assert_((res[col] == agged[col]).all())
> /usr/lib/python2.7/unittest/case.py(425)assertTrue()
-> raise self.failureException(msg)
(Pdb) up
> /tmp/pandas/pandas/tests/test_groupby.py(404)test_transform_broadcast()
-> self.assert_((res[col] == agged[col]).all())
(Pdb) p res[col][0]
0.14593691347699592
(Pdb) p agged[col]
0.14593691347699594
```
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MDU6SXNzdWU4NDUyNzg3
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Assigning PeriodIndex to DataFrame columns should box period objects
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| 1
| 2012-11-18T19:02:11Z
| 2012-11-23T05:43:27Z
| 2012-11-23T05:43:27Z
|
MEMBER
| null |
http://stackoverflow.com/questions/13432213/pandas-time-period-data-type-prints-as-numbers
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Ideas about random sampling
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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| 1
| 2012-11-18T19:37:15Z
| 2013-09-21T13:49:37Z
| 2013-09-21T13:49:37Z
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MEMBER
| null |
http://stackoverflow.com/questions/13395725/efficient-way-of-doing-permutations-with-pandas-over-a-large-dataframe
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MDExOlB1bGxSZXF1ZXN0MzA0MzMxMQ==
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ENH: google analytics integration using oauth2
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| 2014-06-14T13:14:30Z
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CONTRIBUTOR
| null |
skips tests if no network or authentication config file is missing
@wesm, mainly just want a quick review of the testing suite to make sure it skips the test in the right situations.
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API: fillna method argument should be None by default and raise on both ...
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| 2012-11-18T22:28:48Z
| 2014-06-12T07:17:06Z
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CONTRIBUTOR
| null |
...value and method #2027
@wesm what do you think of the API change? If you're ok with it I'll propagate to DataFrame and Panel.
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Replace with regular expression
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| 2012-11-19T00:00:17Z
| 2013-05-29T18:03:46Z
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CONTRIBUTOR
| null |
xref: http://stackoverflow.com/questions/13445241/replacing-blank-values-white-space-with-nan-in-pandas
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Additional parameterization for groupby.agg
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| 2012-11-19T00:43:13Z
| 2017-05-04T13:37:41Z
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CONTRIBUTOR
| null |
more explicit control over whether the agg func is applied per column or over each group DataFrame.
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Support for customizing parallel_plot() x axis tickmarks
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| 0
| 2012-11-19T03:26:26Z
| 2012-12-11T20:34:24Z
| 2012-12-11T20:34:24Z
|
CONTRIBUTOR
| null |
If the columns are already numeric, then you can simply use "use_columns=True" to have x-axis scaled according to those values. Otherwise the "xticks" parameter can be used to manually specify xticks to use.
Feedback or suggestions are welcome!
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| 8,459,442
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MDU6SXNzdWU4NDU5NDQy
| 2,288
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Replace all usages of assert keyword/function with AssertionErrors
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| 2012-11-19T04:56:16Z
| 2012-11-20T19:36:07Z
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|
MEMBER
| null |
This is probably long overdue. Brought up by #2057. If you help with this please make the new error message informative!
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MDU6SXNzdWU4NDcwMDQ0
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Add "day of month" (e.g., 15th of month) frequencies, easier extension?
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| 5
| 2012-11-19T14:15:50Z
| 2019-10-21T19:34:15Z
| null |
MEMBER
| null |
http://stackoverflow.com/questions/13445174/date-ranges-in-pandas
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MDU6SXNzdWU4NDc0NjI3
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|
Dependency on weird statsmodels import causes large slowdown
|
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| 2012-11-19T16:36:48Z
| 2014-05-19T15:52:34Z
| 2012-12-12T12:48:32Z
|
NONE
| null |
I filed a bug on this over with scikits.statsmodels, but in the meantime I thought Pandas folks might want to know, so here's the bug report below:
Within statsmodels/tools/data.py, there's a piece of code in the function "_is_using_larry" that tries an import of the la module and walks the Python path looking for the module.
When it's not installed, this substantially slows down several programs. Probably, this module should just be included with statsmodels, or a global variable within data.py should be used to prevent multiple expensive searches for importing the file.
In my use case, I am working with Python Pandas and doing several large rolling OLS calculations distributed on many processors. The compounded effect of all of these searches to import la is pretty drastic. When I just added my own la.py file to a regular directory in my path, and all the file contained was: "larry = type(None)", this sped up my code by a factor of 50. This shouldn't be needed though, for overcoming a simple module import.
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BUG: dtype=object should stop conversion from object in frame constructo...
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| 1
| 2012-11-19T19:22:53Z
| 2014-06-25T01:09:13Z
| 2012-11-24T00:31:38Z
|
CONTRIBUTOR
| null |
...r #2255
don't call maybe_convert_objects in _convert_object_array if dtype is object.
Note that it also stops other type conversions too.
|
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sum over objects
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| 5
| 2012-11-19T20:25:46Z
| 2012-11-27T02:37:27Z
| 2012-11-27T02:37:27Z
|
CONTRIBUTOR
| null |
Was surprised by this. I don't recall seeing this behavior before. I guess there are some object types that you do want to try to add together? Still seems like this could lead to problems
```
[~/statsmodels/statsmodels-skipper/statsmodels/sandbox/examples/]
[20]: import statsmodels.api as sm
[~/statsmodels/statsmodels-skipper/statsmodels/sandbox/examples/]
[21]: aml = sm.datasets.get_rdataset("leukemia", "survival")
[~/statsmodels/statsmodels-skipper/statsmodels/sandbox/examples/]
\[22\]: aml.data.head(2)
[22]:
time status x
0 9 1 Maintained
1 13 1 Maintained
[~/statsmodels/statsmodels-skipper/statsmodels/sandbox/examples/]
\[23\]: aml.data.sum()
[23]:
time 678
status 18
x MaintainedMaintainedMaintainedMaintainedMainta...
```
|
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| 8,482,316
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MDExOlB1bGxSZXF1ZXN0MzA1NTczNw==
| 2,293
|
BUG: remove asserts from core #2288
|
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| 1
| 2012-11-19T20:46:08Z
| 2014-06-12T16:07:43Z
| 2012-11-20T19:36:00Z
|
CONTRIBUTOR
| null |
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| 8,483,279
|
MDU6SXNzdWU4NDgzMjc5
| 2,294
|
pandas export_to_excel (time stamp in tuple)
|
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| 0
| 2012-11-19T21:18:58Z
| 2012-12-02T01:06:34Z
| 2012-12-02T01:06:34Z
|
NONE
| null |
``` python
import pandas as pd
import StringIO
s = StringIO.StringIO(
"""Date,ticker,type,value
2001-01-01,x,close,12.2
2001-01-01,x,open ,12.1
2001-01-01,y,close,12.2
2001-01-01,y,open ,12.1
2001-02-01,x,close,12.2
2001-02-01,x,open ,12.1
2001-02-01,y,close,12.2
2001-02-01,y,open ,12.1
2001-03-01,x,close,12.2
2001-03-01,x,open ,12.1
2001-03-01,y,close,12.2
2001-03-01,y,open ,12.1""")
df = pd.read_csv(s, parse_dates=["Date"])
pdf = df.pivot_table(values="value", rows=["ticker"], cols=["Date", "type"])
pdf.to_excel('test.xlsx')
```
xlsx erros in
```
Traceback (most recent call last):
File "ipython_log.py", line 19, in <module>
pdf.to_excel('test.xlsx')
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 1343, in to_excel
index_label=index_label)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 1198, in _helper_csvexcel
writer.writerow(encoded_labels + encoded_cols)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 1932, in writerow
self._writerow_xlsx(row, sheet_name)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 1977, in _writerow_xlsx
sheet.append(conv_row)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/openpyxl/writer/dump_worksheet.py", line 238, in append
cell = self._string_builder.add(cell)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/openpyxl/writer/strings.py", line 82, in add
key = key.strip()
AttributeError: 'tuple' object has no attribute 'strip'
```
xls errors in
```
pdf.to_excel('test.xls')
```
```
Traceback (most recent call last):
File "ipython_log.py", line 19, in <module>
pdf.to_excel('test.xls')
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 1343, in to_excel
index_label=index_label)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 1198, in _helper_csvexcel
writer.writerow(encoded_labels + encoded_cols)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 1934, in writerow
self._writerow_xls(row, sheet_name)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.9.2.dev_c103897-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 1956, in _writerow_xls
sheetrow.write(i, val)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Row.py", line 257, in write
self.__rich_text_helper(col, label, style, style_index)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Row.py", line 283, in __rich_text_helper
raise Exception ("Unexpected data type %r" % type(data))
Exception: Unexpected data type <class 'pandas.lib.Timestamp'>
```
a Fix seams relatively easy . just test for tuple or MultiIndex and fix the output (similar to csv parser)
I was thinking of adding an ExcelFormatter similar to the HTMLFormatter which would merge cells , bold header....
I am currently using the to_html and then importing in excel but would like to have the ability to dump multiple dataframe in different tabs.
did anyone else start a branch? otherwise i would give it a go
Thanks
|
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MDU6SXNzdWU4NDg1ODI1
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|
Error setting multiple columns via hierarchical indexing in DataFrame
|
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| 4
| 2012-11-19T22:41:40Z
| 2012-11-24T00:14:41Z
| 2012-11-24T00:14:41Z
|
NONE
| null |
I encountered the following:
```
for i in xrange(len(reg_results)):
name = names[i]
reg_result = reg_results[i]
betas[name] = reg_result._beta_raw
```
> ---
>
> AssertionError Traceback (most recent call last)
> <ipython-input-26-86f8dc5d0623> in <module>()
> 5 print(betas[name].shape)
> 6 print(reg_result._beta_raw.shape)
> ----> 7 betas[name] = reg_result._beta_raw
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in **setitem**(self, key, value)
> 1802 else:
> 1803 # set column
> -> 1804 self._set_item(key, value)
> 1805
> 1806 def _boolean_set(self, key, value):
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in _set_item(self, key, value)
> 1842 """
> 1843 value = self._sanitize_column(key, value)
> -> 1844 NDFrame._set_item(self, key, value)
> 1845
> 1846 def insert(self, loc, column, value):
>
> C:\Python27\lib\site-packages\pandas\core\generic.pyc in _set_item(self, key, value)
> 491 if len(key) != self.columns.nlevels:
> 492 key += ('',)*(self.columns.nlevels - len(key))
> --> 493 self._data.set(key, value)
> 494
> 495 try:
>
> C:\Python27\lib\site-packages\pandas\core\internals.pyc in set(self, item, value)
> 882 if value.ndim == self.ndim - 1:
> 883 value = value.reshape((1,) + value.shape)
> --> 884 assert(value.shape[1:] == self.shape[1:])
> 885 if item in self.items:
> 886 i, block = self._find_block(item)
>
> AssertionError:
Data shapes seem to be equal:
```
print(betas[name].shape)
print(reg_result._beta_raw.shape)
betas[name].shape == reg_result._beta_raw.shape
```
> (1257, 3)
> (1257L, 3L)
> True
The walkaround that works is the following:
```
for i in xrange(len(reg_results)):
name = names[i]
reg_result = reg_results[i]
for i in xrange(len(data_columns)):
column = data_columns[i]
betas[name, column] = reg_result._beta_raw[:, i]
```
Is this the way to go or did I encounter a bug?
|
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MDU6SXNzdWU4NDg2OTc0
| 2,296
|
bug in csv_reader?
|
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| 2
| 2012-11-19T23:17:27Z
| 2012-11-20T04:54:46Z
| 2012-11-20T04:53:42Z
|
NONE
| null |
Some previously-functioning code of mine broke this morning after upgrading to the newest Ubuntu package of Pandas v. 0.9.2. It seems to be a bug in the upgraded read_csv parser. Beyond that, the error message (below) is fairly unhelpful to the uninitiated. Maybe this is a bug, or maybe the new version uses different syntax (although some of the .csv files are able to be imported...).
I am sending a copy of the offending file to Wes, as I've got no quick place to put it.
Thanks,
Richard
aHe_df = pd.readcsv('aHe_aliquots.csv')
---
CParserError Traceback (most recent call last)
/home/itchy/ecopetrol/ec-working/data/<ipython-input-29-9ec771184960> in <module>()
----> 1 aHe_df =pd.read_csv('aHe_aliquots.csv')
/usr/lib/pymodules/python2.7/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, header, index_col, names, skiprows, skipfooter, skip_footer, na_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
361 buffer_lines=buffer_lines)
362
--> 363 return _read(filepath_or_buffer, kwds)
364
365 parser_f.__name__ = name
/usr/lib/pymodules/python2.7/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
185
186 # Create the parser.
--> 187 parser = TextFileReader(filepath_or_buffer, **kwds)
188
189 if nrows is not None:
/usr/lib/pymodules/python2.7/pandas/io/parsers.pyc in **init**(self, f, engine, **kwds)
465 self.options, self.engine = self._clean_options(options, engine)
466
--> 467 self._make_engine(self.engine)
468
469 def _get_options_with_defaults(self, engine):
/usr/lib/pymodules/python2.7/pandas/io/parsers.pyc in _make_engine(self, engine)
567 def _make_engine(self, engine='c'):
568 if engine == 'c':
--> 569 self._engine = CParserWrapper(self.f, **self.options)
570 else:
571 if engine == 'python':
/usr/lib/pymodules/python2.7/pandas/io/parsers.pyc in **init**(self, src, *_kwds)
787 ParserBase.**init**(self, kwds)
788
--> 789 self._reader = _parser.TextReader(src, *_kwds)
790
791 # XXX
/usr/lib/pymodules/python2.7/pandas/_parser.so in pandas._parser.TextReader.__cinit__ (pandas/src/parser.c:3357)()
/usr/lib/pymodules/python2.7/pandas/_parser.so in pandas._parser.TextReader._get_header (pandas/src/parser.c:4283)()
/usr/lib/pymodules/python2.7/pandas/_parser.so in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:5731)()
/usr/lib/pymodules/python2.7/pandas/_parser.so in pandas._parser.raise_parser_error (pandas/src/parser.c:13774)()
CParserError: Error tokenizing data. C error: no error message set
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MDExOlB1bGxSZXF1ZXN0MzA1ODAyNA==
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ENH: DataFrame.from_records takes iterator #1794
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| 1
| 2012-11-19T23:20:40Z
| 2012-11-20T19:29:28Z
| 2012-11-20T19:29:28Z
|
CONTRIBUTOR
| null |
@wesm are there other cases you can think of we should add as test case?
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| 8,497,822
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MDU6SXNzdWU4NDk3ODIy
| 2,298
|
read_csv fails for UTF-16 with BOM (maybe also other encodings with BOM) and skiprows
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| 10
| 2012-11-20T09:27:11Z
| 2012-12-06T18:37:11Z
| 2012-12-06T18:37:11Z
|
NONE
| null |
Unfortunately I can't send the file but from the output of head filename -n 7
```
��Name Ad performance report
Type Ad
Frequency One time
Date range Custom date range
Dates Sep 19, 2012-Nov 19, 2012
Account Day Campaign Ad group Ad ID Client name Destination URL Impressions Clicks Cost Avg. position Status Conv. (1-per-click)
Categories 2 15.11.2012 something: ;C7:8 [somethinglse]{test}: ;C7:8 16902484818 Categories 2 http://www.someurl?ad=291012 333 2 4.7 5.5 approved 0
```
I guess that the beginning of the file is the BOM and that this causes problems when skipping the rows. Without skiprows everything gets read into one row with the first column containing the BOM.
```
<class 'pandas.core.frame.DataFrame'>
Index: 0 entries
Data columns:
��Name\tAd performance report\t\t\t\t\t\t\t\t\t\t\t
...
```
The error raised is:
```
pd.read_csv('/home/arthur/Desktop/client 139 - ads report/test_pandas.csv', sep='\t', skiprows=5)
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, header, index_col, names, skiprows, skipfooter, skip_footer, na_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
361 buffer_lines=buffer_lines)
362
--> 363 return _read(filepath_or_buffer, kwds)
364
365 parser_f.__name__ = name
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
185
186 # Create the parser.
--> 187 parser = TextFileReader(filepath_or_buffer, **kwds)
188
189 if nrows is not None:
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
465 self.options, self.engine = self._clean_options(options, engine)
466
--> 467 self._make_engine(self.engine)
468
469 def _get_options_with_defaults(self, engine):
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _make_engine(self, engine)
567 def _make_engine(self, engine='c'):
568 if engine == 'c':
--> 569 self._engine = CParserWrapper(self.f, **self.options)
570 else:
571 if engine == 'python':
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in __init__(self, src, **kwds)
787 ParserBase.__init__(self, kwds)
788
--> 789 self._reader = _parser.TextReader(src, **kwds)
790
791 # XXX
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader.__cinit__ (pandas/src/parser.c:3579)()
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_b8dae94-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._get_header (pandas/src/parser.c:4590)()
CParserError: Passed header=0 but only 0 lines in file
```
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MDU6SXNzdWU4NDk5NDgy
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Retrieving large frames with sparse data from hdf5 - 'NoneType' object is not iterable error
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| 3
| 2012-11-20T10:30:45Z
| 2014-09-19T12:20:26Z
| 2012-11-26T01:40:39Z
|
NONE
| null |
I have been using pandas within my scripts for some time now, especially to store large data sets in an easily accessible way. I have stumbled upon this problem a couple of days ago and have not been able to solve it so far.
The problem is that after I store a huge data frame into an hdf5 file, when I later load it back, it sometimes has one or more columns (only from the object type columns) completely inaccessible and returning the 'NoneType object is not iterable' error.
While I use the frame in memory there are no problems, even with moderately larger data sets than the example below. It is worth mentioning that the frame contains either multiple datetime columns or multiple VMS timestamps (http://labs.hoffmanlabs.com/node/735), as well as string and char and integer columns. All non-object columns can and do have missing values.
At first I thought I was saving 'NA' values in one of the 'object type' columns. Then I tried to update to latest pandas version (0.9.1). I was using 0.9.0 when this problem first occurred. Neither seem to be the solution.
I have been able to reproduce the error with the following code:
<pre lang="python"><code>
import pandas as pd
import numpy as np
import datetime
# Get VMS timestamps for today
time_now = datetime.datetime.today()
start_vms = datetime.datetime(1858, 11, 17)
t_delta = (time_now - start_vms)
vms_time = t_delta.total_seconds() * 10000000
# Generate Test Frame (dense)
test_records = []
vms_time1 = vms_time
vms_time2 = vms_time
for i in range(2000000):
vms_time1 += 15 * np.random.randn()
vms_time2 += 25 * np.random.randn()
vms_time_diff = vms_time2 - vms_time1
string1 = 'XXXXXXXXXX'
string2 = 'XXXXXXXXXX'
string3 = 'XXXXX'
string4 = 'XXXXX'
char1 = 'A'
char2 = 'B'
char3 = 'C'
char4 = 'D'
number1 = np.random.randint(1,10)
number2 = np.random.randint(1,100)
number3 = np.random.randint(1,1000)
test_records.append((char1, string1, vms_time1, number1, char2, string2, vms_time2, number2, char3, string3, vms_time_diff, number3, char4, string4))
df = pd.DataFrame(test_records, columns = ["column_1", "column_2", "column_3", "column_4", "column_5", "column_6", "column_7", "column_8", "column_9", "column_10", "column_11", "column_12", "column_13", "column_14"])
# Generate Test Frame (sparse)
test_records = []
vms_time1 = vms_time
vms_time2 = vms_time
count = 0
for i in range(2000000):
if (count%23 == 0):
vms_time1 += 15 * np.random.randn()
string1 = 'XXXXXXXXXX'
string2 = ' '
string3 = 'XXXXX'
string4 = 'XXXXX'
char1 = 'A'
char2 = 'B'
char3 = 'C'
char4 = 'D'
number1 = None
number2 = np.random.randint(1,100)
number3 = np.random.randint(1,1000)
test_records.append((char1, string1, vms_time1, number1, char2, None, None, number2, char3, string3, None, number3, None, string4))
else:
vms_time1 += 15 * np.random.randn()
vms_time2 += 25 * np.random.randn()
vms_time_diff = vms_time2 - vms_time1
string1 = 'XXXXXXXXXX'
string2 = 'XXXXXXXXXX'
string3 = 'XXXXX'
string4 = 'XXXXX'
char1 = 'A'
char2 = 'B'
char3 = 'C'
char4 = 'D'
number1 = np.random.randint(1,10)
number2 = np.random.randint(1,100)
number3 = np.random.randint(1,1000)
test_records.append((char1, string1, vms_time1, number1, char2, string2, vms_time2, number2, char3, string3, vms_time_diff, number3, char4, string4))
count += 1
df1 = pd.DataFrame(test_records, columns = ["column_1", "column_2", "column_3", "column_4", "column_5", "column_6", "column_7", "column_8", "column_9", "column_10", "column_11", "column_12", "column_13", "column_14"])
store_loc = "foo.h5"
h5_store = pd.HDFStore(store_loc )
h5_store['df1'] = df
h5_store['df2'] = df1
h5_store.close()
</code></pre>
When I try to load from this store now the 'df1' is behaving normally, but the 'df2' is producing the following error:
<code>
TypeError: 'NoneType' object is not iterable
</code>
Additionally I just tried to reproduce this error on pandas version 0.8.1. It does not seem to be present there. So it is probably connected with the I/O changes introduced in 0.9.0?
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MDU6SXNzdWU4NTA1NTcx
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BinGrouper AttributeError when returning a Series from DataFrame.apply
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| 0
| 2012-11-20T14:09:25Z
| 2012-12-07T20:48:03Z
| 2012-12-07T20:48:03Z
|
CONTRIBUTOR
| null |
http://nbviewer.ipython.org/4118124/
``` python
import pandas as pd
from pandas.tseries.resample import TimeGrouper
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = pd.DataFrame({'open':1, 'close':2}, index=ind)
tg = TimeGrouper('M')
grouper = tg.get_grouper(df)
# Errors
df.groupby(grouper).apply(lambda df: df['close'] / df['open'])
# AttributeError: 'BinGrouper' object has no attribute 'groupings'
```
Error does not occur for regular Grouper.
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