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} | 3 | "2013-04-09T23:07:59Z" | "2013-04-10T12:09:40Z" | "2013-04-10T12:09:40Z" | NONE | null | This is a very hard to diagnose bug as caching muddles its effects. I saw similar issues before, but not quite the same so I'm posting below. I'm not sure whether this affects DataFrames, but it seems likely.
``` python
from pandas import Panel, DataFrame
import numpy as np
import datetime
import pandas as pn
import pytz
est = pytz.timezone('US/Eastern')
dt1 = est.localize(datetime.datetime(2013,3,22,11,0,0))
dt2 = est.localize(datetime.datetime(2013,3,22,15,0,0))
dt1p = pn.Timestamp(dt1)
dt2p = pn.Timestamp(dt2)
df1 = DataFrame(np.arange(20).reshape(2,10) + .1, index = [dt1p, dt2p])
df2 = DataFrame(np.arange(20).reshape(2,10) + 20.1, index = [dt1p, dt2p])
panel = Panel({'a':df1,'b':df2}).transpose(1,2,0)
panel.ix[dt2,5,:] = 100
#the issue is that get_loc returns different locations even though they are equal by comparison
print panel.items.get_loc(dt2p)
print panel.items.get_loc(dt2)
print dt2==dt2p
#by merely printing it, the value is cached.
print panel[dt2]
#the following print statement returns the same value because dt2p == dt2
print panel[dt2p]
#if the order is reversed, panel[dt2p] is output instead
panel = Panel({'a':df1,'b':df2}).transpose(1,2,0)
panel.ix[dt2,5,:] = 100
# the assignment of 100 is not there!
print panel[dt2p]
print panel[dt2]
```
I am using python 2.7 on pandas 0.10.1
The most insiduous part is that if one tries to iterate over panel:
``` python
#print panel.ix[dt2, 5, :]
for t in panel:
if t == dt2:
print panel.ix[t,5,:]
```
and tries diagnose this issue by commenting/uncommenting the first print statement, the caching causes it to change the behavior in the loop! It almost caused me to believe there was a bug in the "print" statement.
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} | 7 | "2013-04-10T02:21:27Z" | "2018-07-06T22:37:15Z" | "2018-07-06T22:37:15Z" | NONE | null | No exception is raised when requesting a column that doesn't exist in any of the key tables.
I think the desired behavior in this situation would be to check if the requested columns exist before performing retrieving the data. If any of the columns do not exist, raising an exception that lists the invalid columns would be very helpful.
When I finally finish my wrapper code, I will have a reverse dict that looks up the tables that the requested columns belong too (as suggest by @jreback on SO). I will add this safety check there as well, but it seems like a safe thing to add to select_as_multiple, too.
``` python
>>> pd.__version__
'0.11.0.dev-eb07c5a'
>>> tmp = store.select_as_multiple(['chunk0','chunk1'], columns=['SalesID','INVALID_COLUMN'])
>>> tmp
<class 'pandas.core.frame.DataFrame'>
Int64Index: 401125 entries, 0 to 1124
Data columns (total 1 columns):
SalesID 401125 non-null values
dtypes: int64(1)
```
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} | 0 | "2013-04-10T02:56:47Z" | "2013-04-12T04:44:35Z" | "2013-04-12T04:44:35Z" | CONTRIBUTOR | null | I probably shouldn't have been doing this in the first place, but I'm able to consistently generate a segfault with:
``` python
import pandas as pd
pd.__version__ # '0.11.0.dev-809b238'
d = {'t1': [2, 2.5, 3], 't2': [4, 5, 6]}
df = pd.DataFrame(d)
tuples = [(0, 1), (0, 2), (1, 2)]
df['tuples'] = tuples
df.set_index(pd.MultiIndex.from_tuples(df['tuples']))
```
https://github.com/pydata/pandas/issues/3028 is probably relevant. I have epsilon experience with Cython so I won't really be able to help out with this one. Happy to do more debugging if you need any though.
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} | 1 | "2013-04-10T07:00:41Z" | "2013-04-10T08:00:48Z" | "2013-04-10T08:00:48Z" | MEMBER | null | matplotlib v 1.1.1. maybe i should upgrade?
```
23:57 ~/code/pandas (master)$ nosetests pandas/tests/test_graphics.py
........F...............................
======================================================================
FAIL: test_bar_log (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/wesm/code/pandas/pandas/tests/test_graphics.py", line 414, in test_bar_log
self.assertEqual(ax.yaxis.get_ticklocs()[0],1.0)
AssertionError: 0.10000000000000001 != 1.0
----------------------------------------------------------------------
Ran 40 tests in 76.852s
FAILED (failures=1)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3310 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3310/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3310/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3310/events | https://github.com/pandas-dev/pandas/pull/3310 | 13,020,000 | MDExOlB1bGxSZXF1ZXN0NTA4Mjc3Ng== | 3,310 | TST/CLN: legacy pickle testing | {
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} | 5 | "2013-04-10T13:59:18Z" | "2014-08-06T13:12:24Z" | "2013-04-12T05:07:46Z" | CONTRIBUTOR | null | CLN:
- standardized testing data to live under tests/data (for that set of tests),
- added method `util/testing.get_data_path` to give you this path
TST:
- added `io/tests/test_pickle.py` and `io/tests/generate_legacy_pickles.py`
to generate legacy pickle files and provide comparsions
just run `python path_to_generate_legacy_pickles.py`
it will create a version of pickle for that python version, arch, and system, and store in `pandas/io/tests/data/legacy_pickle/VERSION`
- sample pickles for windows-64/2.7/0.10.1 and linux/2.7/0.10.1 and linux/2.7/0.11.0 added
- need to run this on various combinations to generate the reference library
- allows backwards compatibility testing for these pickles
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} | 1 | "2013-04-10T14:16:26Z" | "2013-08-11T02:11:03Z" | "2013-04-10T18:25:46Z" | CONTRIBUTOR | null | I think something broke:
see: http://stackoverflow.com/questions/15927451/filter-on-pandas-dataframe-with-datetime-columns-raises-error/15927797?noredirect=1#comment22689922_15927797
This is wrong (should give rows > 0)
```
In [3]: df = pd.DataFrame(dict(A = pd.date_range('20130102',periods=5), B = pd.date_range('20130104',periods=5)))
In [4]: df
Out[4]:
A B
0 2013-01-02 00:00:00 2013-01-04 00:00:00
1 2013-01-03 00:00:00 2013-01-05 00:00:00
2 2013-01-04 00:00:00 2013-01-06 00:00:00
3 2013-01-05 00:00:00 2013-01-07 00:00:00
4 2013-01-06 00:00:00 2013-01-08 00:00:00
In [5]: df[df>pd.Timestamp('20130103')]
Out[5]:
A B
0 2013-01-02 00:00:00 2013-01-04 00:00:00
1 2013-01-03 00:00:00 2013-01-05 00:00:00
2 2013-01-04 00:00:00 2013-01-06 00:00:00
3 2013-01-05 00:00:00 2013-01-07 00:00:00
4 2013-01-06 00:00:00 2013-01-08 00:00:00
```
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} | 1 | "2013-04-10T15:01:47Z" | "2018-07-23T04:16:06Z" | "2013-04-10T18:25:33Z" | CONTRIBUTOR | null | closes #3311
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} | 0 | "2013-04-10T15:08:27Z" | "2014-06-17T19:53:14Z" | "2013-04-10T18:24:33Z" | CONTRIBUTOR | null | ...stems)
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} | 9 | "2013-04-10T23:47:41Z" | "2013-04-13T00:21:55Z" | "2013-04-13T00:21:54Z" | CONTRIBUTOR | null | At the moment only some of the arguments are shown in the docstring for to_datetime, which can [lead to unnecessary confusion](http://stackoverflow.com/questions/15929861/pandas-to-datetime-inconsistent-time-string-format/15937890#15937890). This adds all of them to the docstring.
~~Also a cheeky enhancement to use print configs default to set date (copied from `parse_time_string`), not sure how to test that one.... tbh **I think it would make sense to default dayfirst to None**, the current behaviour can be strange (as seen in the above question).~~
Thoughts?
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} | 2 | "2013-04-11T00:41:06Z" | "2013-07-29T06:41:19Z" | "2013-07-29T06:41:19Z" | NONE | null | I am seeing the following behavior when dividing dataframes A and B below. It seems there is cross divisions between common indexes as in Out[268] below. I am hoping for Out[269].
Is the former the intended behavior?

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} | [] | closed | false | null | [] | null | 0 | "2013-04-11T01:19:57Z" | "2014-07-08T12:01:29Z" | "2013-04-11T05:03:16Z" | NONE | null | New script to get a change hirsoty for a functions across the
code base.
Initially, I hope this well help define "golden" commits for the various
adhoc versions of pickled pandas objects, so we can finish off #3310
, get robust back-compat testing and feel easier about touching
the serialization code (which always scares me a little, pesonally).
That's also prep for #3297, and the changes jeff suggested there.
Example usage (check out the --help for filtering):
```
λ cd /tmp/pandas/pandas
λ ~/src/pandas/scripts/find_commits_touching_func.py __getstate__
These commits touched the __getstate__ method in these files on these dates:
138b3ef6 2010-01-05 * Cleaned up and sped up DataMatrix and DataFram ... core/frame.py
f49a3719 2010-06-04 code reorg in Series, miscellaneous groupby refa ... core/frame.py
9bddd0f4 2010-12-11 working toward consistent column ordering in Dat ... core/frame.py
c35a39de 2011-06-12 bit the bullet and did the big merge. now time t ... core/frame.py
739f8a1f 2011-06-09 picklability, column reordering issues, more ref ... core/internals.py
3133a6b0 2011-06-12 need some changes to block internals, getting st ... core/internals.py
1ef60ee0 2011-06-12 unit tests pass...winning! core/internals.py
32b9481d 2011-06-21 mixed-type fixes in DataFrame statistical methods core/internals.py
4833ea9d 2011-06-30 biting the bullet, working to make generic n-dim ... core/internals.py
80b91769 2011-08-15 ENH: support picklability of Index subclasses core/internals.py
0f6d8b43 2009-09-01 latest edits, miscellaneous cleanup and bug fixe ... core/matrix.py
138b3ef6 2010-01-05 * Cleaned up and sped up DataMatrix and DataFram ... core/matrix.py
3ef46c24 2010-04-12 code rearrangement core/matrix.py
739f8a1f 2011-06-09 picklability, column reordering issues, more ref ... core/matrix.py
6fd65acd 2009-11-20 merging latest developments, in particular LongP ... core/panel.py
bf621508 2010-01-09 fleshing out WidePanel functionality. miscellane ... core/panel.py
00494462 2010-06-21 committing tests and changes in broken state, to ... core/panel.py
9eba5770 2011-06-30 removed usages of indexMap, misc panel stuff core/panel.py
8b4d2084 2011-05-03 skeleton of sparse WidePanel, unit tests and stu ... core/sparse.py
4218857a 2011-05-13 correct pickling of sparse frame/panel. setitem/ ... core/sparse.py
b3c12b15 2011-12-10 REF: broke apart sparse.py, created sparse subpackage sparse/frame.py
a5f773bd 2012-06-04 BUG: cast other datetime64 units to nanos from I ... sparse/frame.py
b3c12b15 2011-12-10 REF: broke apart sparse.py, created sparse subpackage sparse/panel.py
5fda0afc 2012-04-04 ENH: non-unique index support. much more testing ... sparse/panel.py
```
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} | 6 | "2013-04-11T02:36:41Z" | "2013-08-21T12:45:07Z" | "2013-08-21T12:45:07Z" | NONE | null | Given:
```
from numpy import ones
from pandas import DataFrame, __version__
df = DataFrame(ones((3, 3)), index=[datetime(2012, 1, 1), datetime(2012, 1, 2), datetime(2012, 1, 3)], columns=['a', 'b', 'c'])
time_freq = date_range('2012-01-01', '2012-01-03', freq='d')
some_cols = ['a', 'b']
```
We get:
```
__version__
```
> '0.10.1'
```
df.reindex(index=time_freq).index.freq
```
> <1 Day>
```
df.reindex(index=time_freq, columns=some_cols).index.freq
```
> None
```
df.reindex(index=time_freq).reindex(columns=some_cols).index.freq
```
> <1 Day>
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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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} | 4 | "2013-04-11T04:25:02Z" | "2013-09-20T18:05:33Z" | "2013-09-20T18:05:33Z" | NONE | null | When storing a dataframe using append, np.nan rows are dropped from the stored frame_tables:
``` python
test = pd.Series(range(401125), dtype=np.float64)
idx = np.random.permutation(test.index)[:50000]
test[idx] = np.nan
# check that there are indeed np.nan in the series
test.isnull().value_counts()
False 351125
True 50000
# create a store
tmp = pd.HDFStore('tmp.h5', 'w')
# add as a DataFrame using put
tmp.put('df_put', pd.DataFrame(test))
tmp.df_put.squeeze().isnull().value_counts()
False 351125
True 50000
# add as a DataFrame using append
tmp.append('df_append', pd.DataFrame(test))
tmp.df_append.squeeze().isnull().value_counts()
False 351125
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3319/events | https://github.com/pandas-dev/pandas/issues/3319 | 13,066,705 | MDU6SXNzdWUxMzA2NjcwNQ== | 3,319 | Selection Issues | {
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} | [] | closed | false | null | [] | null | 12 | "2013-04-11T13:20:28Z" | "2013-04-12T11:21:53Z" | "2013-04-12T11:21:53Z" | NONE | null | I am getting the following error when selecting multiple columns:
TypeError: unsupported operand type(s) for |: 'bool' and 'NotImplementedType'
Code:
``` sql
df.columns
```
Index([KeyNum, ServiceAddrID, ActionStatus, LDCServiceLocationSystemID, LDCCustomerSystemID, TurnOnDate, TurnOffDate, ...etc , dtype=object)
If I select any single column, I get no error.
``` sql
df['TurnOffDate']
```
Selecting multiple columns gives me the error above:
``` sql
df[['TurnOffDate', 'TurnOnDate']]
```
Here is more of the error message:
---
TypeError Traceback (most recent call last)
<ipython-input-30-83763ede32b0> in <module>()
4 # Filter the florida accounts
5 #fl = df.loc[cols][df['State'] == 'FL']
----> 6 df[['TurnOffDate', 'TurnOnDate']]
7 # Rename second 'ServiceAddrID'
8 #fl.columns.values[11] = 'SAID'
C:\Python27\lib\site-packages\pandas\core\frame.pyc in **getitem**(self, key)
1882 if isinstance(key, (np.ndarray, list)):
1883 # either boolean or fancy integer index
-> 1884 return self._getitem_array(key)
1885 elif isinstance(key, DataFrame):
1886 return self._getitem_frame(key)
C:\Python27\lib\site-packages\pandas\core\frame.pyc in _getitem_array(self, key)
1914 return self.take(indexer, axis=0, convert=False)
1915 else:
-> 1916 indexer = self.ix._convert_to_indexer(key, axis=1)
1917 return self.take(indexer, axis=1, convert=True)
1918
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _convert_to_indexer(self, obj, axis)
575 if not to_or.any():
576 raise KeyError('%s not in index' % str(x))
--> 577 mask |= to_or
578
579 indexer = check = mask.nonzero()[0]
TypeError: unsupported operand type(s) for |: 'bool' and 'NotImplementedType'
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https://api.github.com/repos/pandas-dev/pandas/issues/3320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3320/events | https://github.com/pandas-dev/pandas/issues/3320 | 13,067,659 | MDU6SXNzdWUxMzA2NzY1OQ== | 3,320 | inconsistent behavior between DataFrame and Series when underlying objects are datetime | {
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} | [] | closed | false | null | [] | null | 9 | "2013-04-11T13:43:12Z" | "2013-04-11T22:36:16Z" | "2013-04-11T22:36:16Z" | NONE | null | I just upgraded from 0.10 to to 0.10.1 to fix a previous issue I had, and i think I'm getting regressions or inconsistent behavior as it relates to issue #2627
the test below is a comparison on 0.10.0 and 0.10.1 under windows 32 bit python 2.7.2
``` python
import numpy
print numpy.__version__
import pandas
from pandas import DataFrame, Series
print pandas.__version__
import datetime
dt1d = [datetime.datetime(2013,1,1) + datetime.timedelta(days= i) for i in range(5)]
dtSeries = pandas.Series(dt1d)
print dtSeries.dtype
print dtSeries.apply(lambda x: x.date())
#this works
print [x.date() for x in dtSeries.values]
dt2d = [[datetime.datetime(2013,1,1) + datetime.timedelta(days= i)] for i in range(5)]
dtDf = DataFrame(dt2d)
print dtDf.dtypes
print dtDf.icol(0).apply(lambda x: x.date())
#the following do not work on 0.10.1 but work on 0.10.0
print [x.date() for x in dtDf.values[:,0]]
print [x.date() for x in dtDf.icol(0).values]
```
## The output for 0.10.0 in ipython:
1.6.2
0.10.0
object
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
4 2013-01-05
[datetime.date(2013, 1, 1), datetime.date(2013, 1, 2), datetime.date(2013, 1, 3)
, datetime.date(2013, 1, 4), datetime.date(2013, 1, 5)]
0 object
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
4 2013-01-05
Name: 0
[datetime.date(2013, 1, 1), datetime.date(2013, 1, 2), datetime.date(2013, 1, 3)
, datetime.date(2013, 1, 4), datetime.date(2013, 1, 5)]
[datetime.date(2013, 1, 1), datetime.date(2013, 1, 2), datetime.date(2013, 1, 3)
, datetime.date(2013, 1, 4), datetime.date(2013, 1, 5)]
## The output in 0.10.1:
1.6.2
0.10.1
object
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
4 2013-01-05
[datetime.date(2013, 1, 1), datetime.date(2013, 1, 2), datetime.date(2013, 1, 3)
, datetime.date(2013, 1, 4), datetime.date(2013, 1, 5)]
0 datetime64[ns]
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
4 2013-01-05
## Name: 0
AttributeError Traceback (most recent call last)
<ipython-input-86-f277941f299d> in <module>()
18 print dtDf.icol(0).apply(lambda x: x.date())
19 #the following do not work on 0.10.1 but work on 0.10.0
---> 20 print [x.date() for x in dtDf.values[:,0]]
21 print [x.date() for x in dtDf.icol(0).values]
AttributeError: 'numpy.datetime64' object has no attribute 'date'
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} | 4 | "2013-04-11T16:58:43Z" | "2016-07-04T10:50:34Z" | "2016-07-04T10:50:34Z" | NONE | null | ``` python
ix = pd.date_range('1/1/2013', periods=12, freq='M')
ts = Series(np.arange(12), index=ix)
ts
2013-01-31 0
2013-02-28 1
2013-03-31 2
2013-04-30 3
2013-05-31 4
2013-06-30 5
2013-07-31 6
2013-08-31 7
2013-09-30 8
2013-10-31 9
2013-11-30 10
2013-12-31 11
Freq: M, dtype: int64
ts.resample('1D', how='ffill')
2013-01-31 0
2013-02-28 1
2013-03-31 2
2013-04-30 3
2013-05-31 4
2013-06-30 5
2013-07-31 6
2013-08-31 7
2013-09-30 8
2013-10-31 9
2013-11-30 10
2013-12-31 11
Freq: M, dtype: int64
```
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} | 1 | "2013-04-11T21:48:58Z" | "2013-04-12T04:14:49Z" | "2013-04-12T04:14:37Z" | CONTRIBUTOR | null | ```
df = DataFrame({'A' : [1.1,2.2,3.3], 'B' : [5.0,6.1,7.2]}, index = [2010, 2011, 2012])
df.index = df.index.astype(int)
df.ix[2010]
```
was failing - fixed up
from the mailing list
https://groups.google.com/forum/?fromgroups#!topic/pydata/ClJA5ldNQNQ
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https://api.github.com/repos/pandas-dev/pandas/issues/3323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3323/events | https://github.com/pandas-dev/pandas/issues/3323 | 13,094,868 | MDU6SXNzdWUxMzA5NDg2OA== | 3,323 | ENH/API: Allow dicts with tuple keys in DataFrame constructor | {
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} | 15 | "2013-04-11T23:20:57Z" | "2014-07-01T11:30:28Z" | "2014-04-09T23:32:24Z" | MEMBER | null | related #4805
It would be nice to allow automatic conversion to a MultiIndex when a `dict` with tuple keys is passed into the `DataFrame` constructor. Here's what currently happens:
``` python
from pandas import Index, DataFrame
from numpy.random import rand
import itertools as itools
d = {(i, j): rand(10) for i, j in itools.product(xrange(3), repeat=2)}
df = DataFrame(d)
assert type(df.columns) == Index
```
The same issue shows up in `pd.concat` when you pass in a `dict` of sequences (lists and ndarrays and friends) and `axis=1`, however if you have a `dict` of `DataFrames` the columns keys are converted to a MultiIndex. E.g.,
``` python
from pandas import MultiIndex
d = {(i, j): DataFrame(rand(10, 2), columns=['a', 'b']) for i, j in itools.product(xrange(3), repeat=2)}
df = pd.concat(d, axis=1)
assert type(df.columns) == MultiIndex
```
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] | closed | false | null | [] | null | 9 | "2013-04-12T02:39:06Z" | "2017-03-14T13:45:01Z" | "2017-03-14T13:45:01Z" | CONTRIBUTOR | null | Many timeseries-related functions have a `freq` argument that accepts strings representing some offset value. The specific valid options are apparently known as "offset aliases." Not exactly intuitive search terms but I did eventually find https://github.com/pydata/pandas/blob/master/doc/source/timeseries.rst which lists the valid aliases.
I can understand why it's suboptimal to list all valid aliases in the docstring of every function with a `freq` argument but it's also frustrating for users to be told "use one of these" without being told what "these" are. Can a balance be struck?
Perhaps the aliases are already defined in some docstring and I have not found them. In this case, adding a "see here for valid aliases" would probably be sufficient. If they are not, however, what would be the best way of incorporating them?
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} | 2 | "2013-04-12T11:00:29Z" | "2013-09-22T00:06:13Z" | "2013-09-22T00:06:13Z" | CONTRIBUTOR | null | need to run io/tests/generate_legacy_pickles.py on various systems/py/pandas versions to generate more legacy pickles
user contrib welcome
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} | 36 | "2013-04-12T12:51:13Z" | "2013-04-20T09:56:06Z" | "2013-04-20T09:56:06Z" | NONE | null | Here are all the vbenches which differ by more then 15 percent,
best of 5.
I hope to have this automated and realtime by the time the
next release comes around, along with bisection.
1st run
```
λ cat r1/report.txt
Worse
getattr_dataframe_index 2.000000
frame_multi_and_no_ne 1.319728
series_constructor_ndarray 1.329545
ctor_index_array_string 1.651376
frame_wide_repr 48.154345
groupby_sum_booleans 1.152285
indexing_dataframe_boolean_rows 1.168337
series_getitem_scalar 1.862069
dataframe_getitem_scalar 1.214286
datamatrix_getitem_scalar 1.190476
concat_small_frames 1.159975
series_align_left_monotonic 1.289482
reindex_daterange_backfill 1.190402
reindex_daterange_pad 1.185000
timeseries_large_lookup_value 235.037234
dtype: float64
Better
frame_multi_and_st 0.580615
frame_multi_and 0.597748
frame_fancy_lookup 0.792336
frame_get_dtype_counts 0.000404
frame_fancy_lookup_all 0.797565
series_string_vector_slice 0.801726
frame_reindex_upcast 0.523595
frame_reindex_axis0 0.509034
groupby_first_float32 0.043276
groupby_last_float32 0.044075
groupby_transform 0.413400
indexing_dataframe_boolean_st 0.094886
indexing_dataframe_boolean 0.095269
frame_to_csv 0.737868
frame_to_csv2 0.121081
frame_to_csv_mixed 0.381196
write_csv_standard 0.198206
append_frame_single_mixed 0.805745
reindex_frame_level_align 0.790248
reindex_frame_level_reindex 0.787495
```
2nd run
```
Worse
frame_multi_and_no_ne 1.322615
series_constructor_ndarray 1.284091
ctor_index_array_string 1.557522
frame_wide_repr 47.085119
indexing_dataframe_boolean_rows 1.158785
series_getitem_scalar 1.896552
dataframe_getitem_scalar 1.214286
datamatrix_getitem_scalar 1.190476
series_align_left_monotonic 1.293073
reindex_daterange_backfill 1.192585
reindex_daterange_pad 1.176850
frame_reindex_columns 1.231402
timeseries_large_lookup_value 370.207254
dtype: float64
Better
frame_multi_and_st 0.584512
frame_multi_and 0.592406
frame_fancy_lookup 0.794964
frame_get_dtype_counts 0.000393
series_string_vector_slice 0.793987
frame_reindex_upcast 0.562601
frame_reindex_axis0 0.512411
groupby_first_float32 0.042893
groupby_last_float32 0.043322
groupby_transform 0.412138
indexing_dataframe_boolean_st 0.093816
indexing_dataframe_boolean 0.094429
frame_to_csv 0.721259
frame_to_csv2 0.120910
frame_to_csv_mixed 0.399276
write_csv_standard 0.197096
append_frame_single_mixed 0.795502
reindex_frame_level_align 0.787276
reindex_frame_level_reindex 0.786307
dtype: float64
```
Until test_perf gets validated in it's compare mode, re instability
```
#!/bin/bash
# profile current HEAD, against the commit
# specified on the command line
# assume you're running in a venv, and
# that upstream pandas is a git remote named
# "upstream"
PREV_VER=$1
THRESH=0.15
NITER=5
UPSTREAM=upstream/master
git reset --hard $UPSTREAM
H1=$(git log --format="%h" -1)
python setup.py develop
./test_perf.sh -H -N $NITER -c 1 -d $PWD/HEAD-$H1.pickle "$2"
git reset --hard $PREV_VER
H2=$(git log --format="%h" -1)
git checkout upstream/master vb_suite setup.py # bring back the updated suite and test_perf, and build cache
python setup.py develop
./test_perf.sh -H -N $NITER -c 1 -d $PWD/PREV-$H2.pickle "$2"
# back to master
git reset --hard $UPSTREAM
python setup.py develop
SCR=$(tee <<EOF
import pandas as pd
H=(pd.load("HEAD-$H1.pickle").min(1)/pd.load("PREV-$H2.pickle").min(1))
print "Worse"
print H[(H-1)>$THRESH]
print "\nBetter"
print H[(1-H)>$THRESH]
EOF
)
python -c "$SCR"
```
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} | [] | closed | false | null | [] | null | 1 | "2013-04-12T13:35:07Z" | "2014-07-07T14:09:14Z" | "2013-04-12T13:53:41Z" | CONTRIBUTOR | null | fixes an issue I when using non-int64 Int64Indexes (which is very rare) anyhow
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_large_lookup_value | 0.0213 | 2.5117 | 0.0085 |
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [0933ba3] : PERF: fixed int64 indexing perf issue when conversion to int64
Base [8f92d9a] : Merge PR #2881 and fix unit test
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3328/events | https://github.com/pandas-dev/pandas/issues/3328 | 13,121,803 | MDU6SXNzdWUxMzEyMTgwMw== | 3,328 | Wierd issue with series and dataframe creation (clean_index_list error) | {
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} | 4 | "2013-04-12T14:20:32Z" | "2013-04-17T19:10:43Z" | "2013-04-17T19:10:43Z" | NONE | null | Running on Python 3.2 on Windows, using the version I downloaded from Christoph Gohlke's website. Using pandas 10.1, after I recently upgraded from the .9.1 version.
I have the following (new) problem:
obj2 = pd.Series([4,7,-5,3], index = ['d','b','a','c'])
or
df2 = pd.DataFrame([4,7,-5,3], index = ['d','b','a','c'])
both return error: AttributeError: 'module' object has no attribute 'clean_index_list'
I'm pretty sure that is a standard array-like index and therefore this code should work without error.
If I force the index to be an array then this works. That is, these work fine:
obj2 = pd.Series([4,7,-5,3], index = np.array(['d','b','a','c']))
df2 = pd.DataFrame([4,7,-5,3], index = np.array(['d','b','a','c']))
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} | [] | closed | false | null | [] | null | 0 | "2013-04-12T14:40:13Z" | "2013-04-12T15:09:16Z" | "2013-04-12T15:09:16Z" | CONTRIBUTOR | null | This was meant to fix the vbench: ctor_index_array_string
see #3326
not sure if this is that stable of a perf fix
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_get_dtype_counts | 0.0960 | 0.1700 | 0.5647 |
frame_insert_500_columns | 87.8456 | 142.5257 | 0.6163 |
frame_iteritems_cached | 0.0820 | 0.1043 | 0.7860 |
stat_ops_level_frame_sum | 2.5051 | 2.8849 | 0.8683 |
ctor_index_array_string | 0.0333 | 0.0383 | 0.8693 |
stat_ops_level_series_sum_multiple | 5.6090 | 6.4037 | 0.8759 |
frame_fancy_lookup | 2.7517 | 3.1263 | 0.8802 |
Target [f3b19d6] : PERF/CLN: infer Period in infer_dtype, later index inference is faster
Base [df2c21c] : Merge pull request #3327 from jreback/index_perf
```
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} | 1 | "2013-04-12T15:50:17Z" | "2014-01-10T12:14:17Z" | "2014-01-10T12:14:17Z" | NONE | null | https://github.com/pydata/pandas/pull/3314#issuecomment-16300121, `options.display.dayfirst` modifies parsing behaviour in `parse_time_string`, which controls "smart slicing" for timeseries.
Consider seperating the display and parsing behaviour. the parsing
behaviour belongs in `options.mode`. with a warning in 0.12 and the change
in 0.13 if we do it.
opinions?
```
In [2]: pd.describe_option("dayfirst")
display.date_dayfirst:
: boolean
When True, prints and parses dates with the day first, eg 20/01/2005
```
This has ancient roots: https://github.com/pydata/pandas/commit/6876725e#diff-7c0967e4a4ffec815d7be12079eaed6aR126
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} | [] | closed | false | null | [] | null | 1 | "2013-04-12T16:08:41Z" | "2013-04-12T16:14:26Z" | "2013-04-12T16:08:52Z" | CONTRIBUTOR | null | fixes an issue I when using non-int64 Int64Indexes (which is very rare) anyhow
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timeseries_large_lookup_value | 0.0213 | 2.5117 | 0.0085 |
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [0933ba3] : PERF: fixed int64 indexing perf issue when conversion to int64
Base [8f92d9a] : Merge PR #2881 and fix unit test
```
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} | [] | closed | false | null | [] | null | 0 | "2013-04-12T16:18:05Z" | "2013-04-12T16:18:14Z" | "2013-04-12T16:18:14Z" | CONTRIBUTOR | null | This was meant to fix the vbench: ctor_index_array_string
see #3326
not sure if this is that stable of a perf fix
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_get_dtype_counts | 0.0960 | 0.1700 | 0.5647 |
frame_insert_500_columns | 87.8456 | 142.5257 | 0.6163 |
frame_iteritems_cached | 0.0820 | 0.1043 | 0.7860 |
stat_ops_level_frame_sum | 2.5051 | 2.8849 | 0.8683 |
ctor_index_array_string | 0.0333 | 0.0383 | 0.8693 |
stat_ops_level_series_sum_multiple | 5.6090 | 6.4037 | 0.8759 |
frame_fancy_lookup | 2.7517 | 3.1263 | 0.8802 |
Target [f3b19d6] : PERF/CLN: infer Period in infer_dtype, later index inference is faster
Base [df2c21c] : Merge pull request #3327 from jreback/index_perf
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3333/events | https://github.com/pandas-dev/pandas/pull/3333 | 13,134,136 | MDExOlB1bGxSZXF1ZXN0NTEzNzkyNQ== | 3,333 | PERF: series construction perf enhancements, use a fast path based on dt... | {
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} | [] | closed | false | null | [] | null | 0 | "2013-04-12T18:50:22Z" | "2014-06-21T22:22:12Z" | "2013-04-12T19:07:36Z" | CONTRIBUTOR | null | This is only 1us improvement, but non-zero
(I measure the existing one at 9us, this puts it at 10, before
this change was about 11)
difference is 3 function calls, so not much more we can do I think
(as this is handling more cases)
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
series_constructor_ndarray | 0.0106 | 0.0120 | 0.8874 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [c54848f] : PERF: series construction perf enhancements, use a fast path based on dtype
Base [9764ea6] : Merge pull request #3332 from jreback/period_perf2
```
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} | 3 | "2013-04-13T00:52:37Z" | "2013-08-12T13:21:57Z" | "2013-08-12T13:21:57Z" | MEMBER | null | > I'm trying to find the most efficient way to tabulate responses to a survey using pandas. Here's a sample survey:
>
> ```
> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'],
> 'Type' : ['A', 'A', 'B', 'B', 'B', 'C']})
> ```
>
> I want to count the number of responses of each value (Y or N), with the different types across the top.
>
> 1) A simple way to do this is
```
df.groupby('Type')['Response'].value_counts().unstack(level=0)
Type A B C
N 1 1 1
Y 1 2 NaN
```
> This is almost what I want, except I'd like row and column totals as well. Of course, I could calculate these manually, but that seems ugly. What I'd really like to do is create a pivot table with margins=True, but this isn't quite as easy as I thought it would be.
>
> 2) Again without totals, I can use
```
pivot_table(df, rows='Response',cols='Type',aggfunc=len)
```
> This produces almost the same output as above, though now the index has a name:
```
Type A B C
Response
N 1 1 1
Y 1 2 NaN
```
> So far so good, but adding margins=True results in an error.
>
> 3) To get exactly what I want, the following works, but it seems there should be a nicer way:
```
df['Count'] = 1
pivot_table(df, rows='Response',cols='Type',values='Count',aggfunc=len,margins=True) # Can also use sum
```
> I have three questions:
>
> 1) Why does adding 'margins=True' in #2 above result in an error, while it works in #3? I'm not sure I understand what's happening here.
>
> 2) If this behavior is intentional, is there a better way to get the output I'm looking for, without introducing an extra, unwanted column?
>
> 3) How do I remove the name of the index from a dataframe (to get the output from #2 to look the same as in #1)?
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|
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as the try: except: is mainly to catch a decoding error
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} | 22 | "2013-04-13T08:39:36Z" | "2016-10-12T23:03:42Z" | "2013-04-22T14:38:23Z" | NONE | null | Hi all,
after having updated my local branch with the current dev master ( pd.**version** == '0.11.0rc1' -- compiling under Mac OS 10.8 went smoothly) some of my scripts in which I handle large data frames (2.6 million rows, 10 columns) stopped working.
It turned out that accessing the df's repr method but also looking up docstrings seems to make ipython unresponsive and creates the impression as if it takes forever.
This little snippet reproduces the error with fake data ('tested' with EPD Python and IPython 7.2 (32-bit)):
``` Python
import numpy as np
import pandas as pd
data = np.random.random((3000000, 10))
df = pd.DataFrame(data)
print df # does the same as `df.__repr__()` or `df?`
```
Can anyone confirm this?
It must have been some of the more recent changes as I did not have trouble with this earlier.
Any pointers and comments are highly welcome.
Cheers,
Denis
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} | 10 | "2013-04-13T11:40:49Z" | "2013-10-04T23:17:53Z" | "2013-10-04T23:17:53Z" | NONE | null | continuing #3112 , get rid of all the shocking blue in [visualization.rst](http://pandas.pydata.org/pandas-docs/dev/visualization.html)
scatter_plot still uses the evil blue, for one.
Also, grid defaults.
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} | 1 | "2013-04-13T12:41:05Z" | "2015-01-26T00:50:09Z" | "2015-01-26T00:50:09Z" | CONTRIBUTOR | null | allow GroupBy getitem to access group key values
```
In [8]: df = pd.DataFrame({'A': ['foo', 'bar'] * 3,
'B': np.random.randn(6),
'C': np.random.randint(0, 20, 6)})
# this gets you a new grouper
In [22]: df.groupby(['A'])['B']
Out[22]: <pandas.core.groupby.SeriesGroupBy at 0x3152810>
# get based on a value
In [10]: df.groupby(['A']).get_group('bar')
Out[10]:
A B C
1 bar -1.303892 19
3 bar 0.916340 8
5 bar -0.387371 7
# could be allowed
In [11]: df.groupby(['A'])['bar']
KeyError: 'bar'
```
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} | 8 | "2013-04-13T14:17:50Z" | "2013-09-22T00:32:10Z" | "2013-09-22T00:32:10Z" | NONE | null | Hi there,
this is one that has puzzled me for quite some time, but somehow it never asked about it. It seems whether an conditional assignment to a df column is performed or not depends on the order of indexing / member getting inside the left-hand expression of the assignment statement. See this snippet:
``` Python
import pandas as pd
# create some data
data = {'foo': list('abcde'),
'bar': range(5)}
df = pd.DataFrame(data)
# create arbitrary index.
my_idx = df.bar > 2
print df[my_idx]
# assign to a member which was accessed after indexing.
df[my_idx].foo = 'X'
# see there is no change
print df
# assign to member which was accessed before indexing.
df.foo[my_idx] = 'X'
# now it has worked
print df
```
Is there a deliberate reason for this behaviour? My feeling about this is that it may confuse new users and may be hard to track down, as this goes silently and looks like correct code.
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} | 15 | "2013-04-13T14:34:45Z" | "2018-07-09T17:28:37Z" | "2018-07-06T22:19:40Z" | CONTRIBUTOR | null | xref #12585
I think this ought to raise a ValueErrror:
```
In [1]: pd.to_datetime('01-13-2012', dayfirst=True)
Out[1]: datetime.datetime(2012, 1, 13, 0, 0)
```
_This means if my data is bad (i.e. somehow I have a mixture of american and british string-dates), I won't be told there was an issue when parsing! I can't think of a usecase where this kind of precedence would be appropriate, and it means you'll end up with incorrect dates._
The [precedence behaviour](http://labix.org/python-dateutil#head-b95ce2094d189a89f80f5ae52a05b4ab7b41af47) appears to be a feature from `dateutil` (which `tslib.array_to_datetime` calls):
```
In [2]: from dateutil.parser import parse as parse_date
In [3]: parse_date('1/12/2012', dayfirst=True)
Out[3]: datetime.datetime(2012, 12, 1, 0, 0)
In [4]: parse_date('1/13/2012', dayfirst=True)
Out[4]: datetime.datetime(2012, 1, 13, 0, 0)
```
It should raising like this:
```
In [5]: parse_date('14/14/2012', dayfirst=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-ca14fc16e421> in <module>()
----> 1 parse_date('14/14/2012', dayfirst=True)
/Library/Python/2.7/site-packages/dateutil/parser.pyc in parse(timestr, parserinfo, **kwargs)
718 return parser(parserinfo).parse(timestr, **kwargs)
719 else:
--> 720 return DEFAULTPARSER.parse(timestr, **kwargs)
721
722
/Library/Python/2.7/site-packages/dateutil/parser.pyc in parse(self, timestr, default, ignoretz, tzinfos, **kwargs)
315 if value is not None:
316 repl[attr] = value
--> 317 ret = default.replace(**repl)
318 if res.weekday is not None and not res.day:
319 ret = ret+relativedelta.relativedelta(weekday=res.weekday)
ValueError: month must be in 1..12
```
... :(
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} | 9 | "2013-04-13T16:28:49Z" | "2013-04-14T18:14:50Z" | "2013-04-14T15:14:43Z" | NONE | null | This, in part addresses our discussion from #3337.
This PR moves the doc string from **init** to the more common class location.
(cf.. numpy, rest of pandas classes that have a doc string)
Besides STY / consistency this would allow for more directly accessing the doc string in a debugging (pdb) session:
``` Python
print mydf.__doc__
```
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} | 6 | "2013-04-13T23:20:06Z" | "2016-08-01T15:35:58Z" | "2016-07-31T18:39:04Z" | NONE | null | Pandas does not handle Series in CSV format very well.
First problem: read_csv(squeeze=True) does not work, see:
http://stackoverflow.com/questions/15760856/how-to-read-a-pandas-series-from-a-csv-file/15767171
Secondly: both read_csv() and pandas.Series.from_csv() can't handle row-oriented CSV files. As an example, try to read the following as a Series:
import StringIO
string = "somefeature,anotherfeature,f3,f4,f5,f6,f7,lastfeature"
s = StringIO.StringIO(string)
X = pandas.Series.from_csv(s)
Result: X =
somefeature anotherfeature
dtype: object
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} | 17 | "2013-04-13T23:25:19Z" | "2013-04-17T20:48:49Z" | "2013-04-17T20:48:49Z" | NONE | null | Automatic index matching is really great, but sometimes you need a real matrix multiplication, for example to compute the Covariance matrix (in my case using weights, hence .cov() is not usable).
Current workarounds include using numpy.outer() (which is still not really a real matrix multiplication, because it will always give the outer product), or t = t[:, None], but I feel this last solution may hamper the performances on big dataset.
It would be really nice to have a method that just does a "blind" matrix multiplication, multiplying the rows of the first matrix with the columns of the second one, without caring about index matching.
Edit: I'm here talking about DataFrame and Series, which could respectively be considered as matrix and vector.
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} | 1 | "2013-04-13T23:28:42Z" | "2016-11-11T10:10:37Z" | "2016-11-11T10:10:31Z" | NONE | null | Series should be standardized with DataFrame and provide the same structural infos methods, for example .info()
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} | 1 | "2013-04-14T05:45:47Z" | "2018-11-16T13:55:54Z" | "2013-07-15T15:48:59Z" | NONE | null | Rework travis (yet again) to do more testing per build. since tests are
concurrent we gain little by having some jobs take less time (except
a headstart on the next build)
Specifically:
- roll LOCALE_OVERRIDE into an extra step on one of the other jobs
- do "not slow" followed by full_deps install followed by "slow and not network" or just "slow",
fail if either fail.
For example, right now "slow" tests that use 2.7 syntax should fail the build
but don't , since the 2.6 job doesn't run them.
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} | 15 | "2013-04-14T05:57:00Z" | "2017-05-15T10:04:13Z" | "2013-09-27T00:30:19Z" | NONE | null | ``` python
In [42]: labels = [('foo', 1),
...: ('foo', 2),
...: ('bar', 3),
...: ('bar', 4)]
...: pd.MultiIndex.from_tuples(labels)
Out[42]:
MultiIndex
[foo 1, 2, bar 3, 4]
```
hiding the repeated `foo` and `bar` is nice for small multindices, but
for more levels and more complex structures, it can becomes
harder to read. The blanks also keep their width, and the result
isn't very attractive when presented as a single line:
``` python
labels = [('foo', '2012-07-26T00:00:00', 'b5c2700'),
...: ('foo', '2012-08-06T00:00:00', '900b2ca'),
...: ('foo', '2012-08-15T00:00:00', '07f1ce0'),
...: ('foo', '2012-09-25T00:00:00', '5c93e83'),
...: ('foo', '2012-09-25T00:00:00', '9345bba')]
...: pd.MultiIndex.from_tuples(labels)
MultiIndex
[append_frame_single_homogenous 2012-07-26 b5c2700, 2012-08-06 00b2ca, 2012-08-15 07f1ce0, 2012-09-25 5c93e83, 9345bba]
```
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} | 1 | "2013-04-14T06:02:35Z" | "2013-04-14T17:12:19Z" | "2013-04-14T17:12:19Z" | NONE | null | Note the jump from 49 to 150.
``` python
In [55]: pd.MultiIndex.from_tuples(zip(*[range(200),range(200)]))
Out[55]:
MultiIndex
[0 0 , 1 1 , 2 2 , 3 3 , 4 4 , 5 5 , 6 6 , 7 7 , 8 8 , 9 9 , 10 10, 11 11, 12 12, 13 13, 14 14, 15 15, 16 16, 17 17, 18 18, 19 19, 20 20, 21 21, 22 22, 23 23, 24 24, 25 25, 26 26, 27 27, 28 28, 29 29, 30 30, 31 31, 32 32, 33 33, 34 34, 35 35, 36 36, 37 37, 38 38, 39 39, 40 40, 41 41, 42 42, 43 43, 44 44, 45 45, 46 46, 47 47, 48 48, 49 49, 150 150, 151 151, 152 152, 153 153, 154 154, 155 155, 156 156, 157 157, 158 158, 159 159, 160 160, 161 161, 162 162, 163 163, 164 164, 165 165, 166 166, 167 167, 168 168, 169 169, 170 170, 171 171, 172 172, 173 173, 174 174, 175 175, 176 176, 177 177, 178 178, 179 179, 180 180, 181 181, 182 182, 183 183, 184 184, 185 185, 186 186, 187 187, 188 188, 189 189, 190 190, 191 191, 192 192, 193 193, 194 194, 195 195, 196 196, 197 197, 198 198, 199 199]
```
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```
commit timing[ms]
f7a11d6 14.730644
3700b70 14.744759
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f971840 16.744852
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} | 4 | "2013-04-14T09:32:44Z" | "2015-01-26T00:51:00Z" | "2015-01-26T00:51:00Z" | NONE | null | recent change in test_perf (now worked around, but present in d7617d3 for example)
disabled the gc when doing multiple runs on the same vb (just in case).
Though usage, this appears to cause memory exhaustion for large N, which is
an indication that cyclic refs are being created somewhere. invoking gc.collect()
clears the memory (so it's not the malloc.trim issue from #2659)
It might be another dependency, not pandas that's causing this but need to check.
repro:
```
git reset --hard d7617d3
python ./setup.py develop
./test_perf.sh -H -N 500 -r frame_get_numeric_data
# monitor memory
```
See also comments in https://github.com/pydata/pandas/issues/4491
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Probably not serious enough to block release, @wesm?

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f84c9c6 5.308533
5d8eb29 4.900217
6dec888 5.946970
d88d76e 5.022049
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7c51d54 0.096846
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0033f0a 0.106812
19d15a3 0.107574
1de6536 0.107765
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https://api.github.com/repos/pandas-dev/pandas/issues/3357 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3357/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3357/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3357/events | https://github.com/pandas-dev/pandas/pull/3357 | 13,172,188 | MDExOlB1bGxSZXF1ZXN0NTE1MTA3Nw== | 3,357 | ENH: HDFStore now auto creates data_columns if they are specified in min_itemsize | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3358 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3358/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3358/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3358/events | https://github.com/pandas-dev/pandas/pull/3358 | 13,173,295 | MDExOlB1bGxSZXF1ZXN0NTE1MTM5MQ== | 3,358 | BLD: Add 2to3 caching, move the build cache logic into use_build_cache.py | {
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} | [] | closed | false | null | [] | null | 0 | "2013-04-14T14:11:56Z" | "2013-04-14T15:04:13Z" | "2013-04-14T14:58:21Z" | NONE | null | I tested this script all the way back to 0.7.0. The build cache code was a lot of noise
to shove into setup.py, I'm glad to pull it back out.
a "goto_ver.sh" script is now really easy to do, and very useful for benchmarking
and bisecting regressions.
.bashrc:
``` bash
# Use the pandas build cache
export BUILD_CACHE_DIR="/home/user1/tmp/.pandas_build_cache/"
if [ ! -e $BUILD_CACHE_DIR ]; then
mkdir $BUILD_CACHE_DIR ;
fi
function cpdev {
scripts/use_build_cache.py # rewire setup.py with build_cache
python ./setup.py clean;
python ./setup.py develop;
git checkout setup.py # restore setup.py
}
```
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} | [] | closed | false | null | [] | null | 0 | "2013-04-14T16:18:01Z" | "2014-06-16T23:22:22Z" | "2013-04-14T16:31:55Z" | CONTRIBUTOR | null | CLN: cleaned up take in tseries/resample and core/internals to use interenals take
```
(which wasn't using the block level take)
```
This is not a big diff on get_numeric_data, about 5us on avg when I test it,
but its non-zero! (and there is some other code cleanups)
The other tests might be noise, not sure.
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
join_dataframe_index_single_key_bigger | 5.0523 | 5.8804 | 0.8592 |
frame_drop_dup_na_inplace | 2.3123 | 2.4796 | 0.9325 |
frame_get_numeric_data | 0.0637 | 0.0664 | 0.9593 |
Target [ff6501e] : PERF: get_numeric_data now a bit faster
Base [c45e769] : Merge pull request #3357 from jreback/hdf_fix
```
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} | 92 | "2013-04-14T17:17:32Z" | "2013-06-17T00:50:21Z" | "2013-04-23T00:09:17Z" | CONTRIBUTOR | null | ```
% uname -a
FreeBSD XXXX.saltant.net 9.1-STABLE FreeBSD 9.1-STABLE #0 r248078: Fri Mar 8 20:36:00 EST 2013 [email protected]:/usr/obj/usr/src/sys/NIPPL amd64
% pkg_info -xE pandas
py27-pandas-0.11.0.r1
% pkg_info -xE ^python
python27-2.7.3_6
% pkg_info -xE numpy
py27-numpy-1.6.2_1,1
% nosetests pandas.tests.test_index:TestMultiIndex.test_legacy_pickle
E
======================================================================
ERROR: test_legacy_pickle (pandas.tests.test_index.TestMultiIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas-0.11.0rc1-py2.7-freebsd-9.1-STABLE-amd64.egg/pandas/tests/test_index.py", line 1060, in test_legacy_pickle
obj = pickle.load(open(ppath, 'r'))
File "/usr/local/lib/python2.7/pickle.py", line 1378, in load
return Unpickler(file).load()
File "/usr/local/lib/python2.7/pickle.py", line 858, in load
dispatch[key](self)
File "/usr/local/lib/python2.7/pickle.py", line 1090, in load_global
klass = self.find_class(module, name)
File "/usr/local/lib/python2.7/pickle.py", line 1124, in find_class
__import__(module)
ImportError: No module named multiarray
----------------------------------------------------------------------
Ran 1 test in 0.002s
FAILED (errors=1)
```
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} | 1 | "2013-04-14T17:55:03Z" | "2013-04-14T18:47:50Z" | "2013-04-14T18:47:50Z" | CONTRIBUTOR | null | ```
In [6]: df
Out[6]:
cols rows values
0 x a 1
1 y b 2
2 z c 3
In [7]: df.pivot_table(rows='rows', aggfunc={'values': ['mean']})
Out[7]:
values
mean
rows
a 1
b 2
c 3
In [8]: df.pivot_table(cols='cols', aggfunc={'values': ['mean']})
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-f8b9a78d8e65> in <module>()
----> 1 df.pivot_table(cols='cols', aggfunc={'values': ['mean']})
/home/chang/code/pandas/pandas/tools/pivot.py in pivot_table(data, values, rows, cols, aggfunc, fill_value, margins)
103 for i in range(len(rows), len(keys))]
104
--> 105 table = agged.unstack(to_unstack)
106
107 if isinstance(table, DataFrame):
/home/chang/code/pandas/pandas/core/frame.py in unstack(self, level)
3947 """
3948 from pandas.core.reshape import unstack
-> 3949 return unstack(self, level)
3950
3951 #----------------------------------------------------------------------
/home/chang/code/pandas/pandas/core/reshape.pyc in unstack(obj, level)
363 def unstack(obj, level):
364 if isinstance(level, (tuple, list)):
--> 365 return _unstack_multiple(obj, level)
366
367 if isinstance(obj, DataFrame):
/home/chang/code/pandas/pandas/core/reshape.pyc in _unstack_multiple(data, clocs)
228 rlocs = [i for i in range(index.nlevels) if i not in clocs]
229
--> 230 clevels = [index.levels[i] for i in clocs]
231 clabels = [index.labels[i] for i in clocs]
232 cnames = [index.names[i] for i in clocs]
AttributeError: 'Index' object has no attribute 'levels'
```
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} | 10 | "2013-04-15T03:21:07Z" | "2014-08-06T22:35:24Z" | "2013-04-18T05:52:58Z" | NONE | null | I installed version pandas-0.11.0.dev-cddde22.win32-py2.7.exe but I always got the errors while importing pandas.Then I tried to install pandas-0.10.1.win32-py2.7.exe (without uninstalling the previous, I didn't know how to do, it didn't change anything. I have vista.
The error is 'cannot import name hashtable' if I use from pandas import *, or 'cannot import name Timestamp' if I use import pandas.
E:\work\work_pzt\measure_data\RNR283>python
Enthought Python Distribution -- www.enthought.com
Version: 7.0-2 (32-bit)
Python 2.7.1 |EPD 7.0-2 (32-bit)| (r271:86832, Dec 2 2010, 10:35:02) [MSC v.150
0 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "C:\Python27\lib\site-packages\pandas__init__.py", line 25, in <module>
> > > import pandas.core.config_init
> > > File "C:\Python27\lib\site-packages\pandas\core\config_init.py", line 3, in <m
> > > odule>
> > > from pandas.core.format import detect_console_encoding
> > > File "C:\Python27\lib\site-packages\pandas\core\format.py", line 12, in <modul
> > > e>
> > > from pandas.core.index import MultiIndex, _ensure_index
> > > File "C:\Python27\lib\site-packages\pandas\core\index.py", line 13, in <module
>
> ```
> from pandas.lib import Timestamp
> ```
>
> ImportError: cannot import name Timestamp
>
> > > from pandas import *
> > > cannot import name hashtable
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "C:\Python27\lib\site-packages\pandas__init__.py", line 6, in <module>
> > > from . import hashtable, tslib, lib
> > > ImportError: cannot import name hashtable
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https://api.github.com/repos/pandas-dev/pandas/issues/3363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3363/events | https://github.com/pandas-dev/pandas/issues/3363 | 13,185,783 | MDU6SXNzdWUxMzE4NTc4Mw== | 3,363 | test failures and errors with v0.11rc1 | {
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} | 29 | "2013-04-15T05:43:20Z" | "2013-04-21T16:56:27Z" | "2013-04-21T16:56:27Z" | NONE | null | I get one failure and 7 errors when I run the test suite on v0.11rc1.
This is the traceback of the error:
``` ======================================================================
FAIL: testWLS (pandas.stats.tests.test_ols.TestOLS)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../pandas/pandas/stats/tests/test_ols.py", line 107, in testWLS
self._check_wls(X, Y, weights)
File "/.../pandas/pandas/stats/tests/test_ols.py", line 130, in _check_wls
self.checkMovingOLS('rolling', x, y, weights=weights)
File "/.../pandas/pandas/stats/tests/test_ols.py", line 204, in checkMovingOLS
result_index=n)
File "/.../pandas/pandas/stats/tests/test_ols.py", line 243, in compare
assert_almost_equal(ref, res)
File "/.../pandas/pandas/util/testing.py", line 139, in assert_almost_equal
assert_almost_equal(a[i], b[i], check_less_precise)
File "/.../pandas/pandas/util/testing.py", line 170, in assert_almost_equal
1, a / b, decimal=decimal, err_msg=err_msg(a, b), verbose=False)
File "/.../python2.7/site-packages/numpy/testing/utils.py", line 468, in assert_almost_equal
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 5 decimals expected 1.62656 but got 1.62097
```
For `test_axis_limits`, `test_line_plot_datetime_frame`,
`test_line_plot_datetime_series`, `test_line_plot_inferred_freq`, `test_line_plot_period_frame`,
`test_line_plot_period_series` and `test_tsplot` I get a `ValueError`:
```
ValueError: matplotlib display text must have all code points < 128 or use Unicode strings
```
The reason for the errors seems to be my language setting, which is `de_DE.UTF-8`. I never got these errors in the pandas test suite before so maybe they are also related to the recent update to matplotlib 1.2.1.
```
print_versions.py
INSTALLED VERSIONS
------------------
Python: 2.7.4.final.0
Cython: 0.18
Numpy: 1.7.1
Scipy: 0.12.0
statsmodels: 0.5.0.dev-1bbd4ca
patsy: 0.1.0
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013b
PyTables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: Not installed
xlrd: Not installed
xlwt: 0.7.4
sqlalchemy: 0.8.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3364/events | https://github.com/pandas-dev/pandas/pull/3364 | 13,204,772 | MDExOlB1bGxSZXF1ZXN0NTE2NTExNA== | 3,364 | CLN: remove some unicode hair from pre-pprint_thing days GH3360 | {
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} | 0 | "2013-04-15T15:20:36Z" | "2014-07-16T08:05:55Z" | "2013-04-23T02:09:41Z" | NONE | null | breaks no tests, and the code it removes resolved an issue
which now works even without it. even so, no sudden moves
a couple of days before a major release, so 0.12.
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} | 0 | "2013-04-15T15:29:33Z" | "2014-06-16T14:25:07Z" | "2013-04-15T15:39:33Z" | CONTRIBUTOR | null | Very simple formatting change.
Note about cursor position in time series plots should not have been a part of changes to Options class in data.io.
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} | 3 | "2013-04-15T15:47:55Z" | "2013-09-22T13:39:19Z" | "2013-09-22T00:12:59Z" | CONTRIBUTOR | null | ``` python
import pandas
df = pandas.DataFrame({"a":["a","b","a","b"], "b":[1,2,3,4], "c":[1.,2.,3.,4.], "d":["a","b","a","b"]})
print df.dtypes
print df
print df[["b","c"]].corr()
groups = df.groupby("a")
highest = groups.apply(lambda group: group.ix[group['b'].idxmax()])
print highest.dtypes
print highest
print highest[["b","c"]].corr()
```
I expected that the dtypes were not changed, e.g. columns b and c should stay int and float.
I had that in my notebook and reran it with a newer version (about 2 month ago vs compiled today) and was wondering why some columns didn't show up in the describe and corr calls...
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} | 11 | "2013-04-15T16:54:49Z" | "2014-07-23T21:28:50Z" | "2014-07-23T21:28:50Z" | NONE | null | If i do:
``` python
from pandas import Series, PeriodIndex, Period
pi = PeriodIndex(start=Period('1950-1'), freq='M', end=Period('1950-7'))
a = Series([1,nan,nan,4,5,nan,7], pi)
b = Series([9,9,9,9,9,9,9], pi)
display(a, b, a.combine_first(b))
```
Then the output is:
```
1950-01 1
1950-02 NaN
1950-03 NaN
1950-04 4
1950-05 5
1950-06 NaN
1950-07 7
Freq: M
1950-01 9
1950-02 9
1950-03 9
1950-04 9
1950-05 9
1950-06 9
1950-07 9
Freq: M
1930-01 NaN
1930-03 NaN
1930-05 NaN
1930-07 NaN
1930-09 NaN
1930-11 NaN
1931-01 NaN
Freq: M
```
As you can see the index is changed and the values are all gone.
I think this is a bug. My Pandas version is `0.10.1`.
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} | 49 | "2013-04-15T18:28:50Z" | "2013-05-04T00:15:40Z" | "2013-05-04T00:15:40Z" | NONE | null | from ML: https://groups.google.com/forum/?fromgroups=#!topic/pydata/q7VVD8YeSLk
User provides HTML string for whatever source he likes, or url.
optionally specify table id, or regex to match against contained cell
content to quickly single out `.+` tables, when multiple exist on the page.
Pseudo:
```
DataFrame.from_html('http://foo.com/tickers?sym=GOOG',match="high")
```
Aside: Perhaps not widely known, but excel and co can import tables directly
from online webpages, a cheap "no code" way to get the data into a form
directly readable by pandas.
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https://api.github.com/repos/pandas-dev/pandas/issues/3370 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3370/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3370/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3370/events | https://github.com/pandas-dev/pandas/issues/3370 | 13,217,047 | MDU6SXNzdWUxMzIxNzA0Nw== | 3,370 | Incorrect ValueError message | {
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} | [] | closed | false | null | [] | null | 2 | "2013-04-15T19:59:25Z" | "2013-04-16T00:57:02Z" | "2013-04-16T00:57:02Z" | NONE | null | I am trying to replace values in a Series with recalculated values, but I get the following error:
```
ValueError: Length of replacements must equal series length
```
However, looking at the lengths of the original values and the replacements, there does not seem to be a problem:
```
In [151]: len(children.wfa[not_null])
Out[151]: 2945
In [152]: len(np.array(wfa))
Out[152]: 2945
In [153]: children.wfa[not_null] = np.array(wfa)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-153-798afd63872b> in <module>()
----> 1 children.wfa[not_null] = np.array(wfa)
/Library/Python/2.7/site-packages/pandas-0.11.0.dev_7996a8c_20130313-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in __setitem__(self, key, value)
811 if _is_bool_indexer(key):
812 key = _check_bool_indexer(self.index, key)
--> 813 self.where(~key,value,inplace=True)
814 else:
815 self._set_with(key, value)
/Library/Python/2.7/site-packages/pandas-0.11.0.dev_7996a8c_20130313-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in where(self, cond, other, inplace)
731 other = other.reindex(ser.index)
732 if len(other) != len(ser):
--> 733 raise ValueError('Length of replacements must equal series length')
734
735 result, changed = com._maybe_upcast_putmask(ser,~cond,other)
ValueError: Length of replacements must equal series length
```
Unfortunately the data I am using is private medical data, so I cannot post the dataset for anyone to look at. However, if there is anything I can do to assist in debugging this, I am willing to iterate through it to get to the bottom of the problem.
Running pandas-0.11.0.dev_7996a8c_2013031 on Python 2.7.2 on OS X 10.8.3.
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} | 0 | "2013-04-15T20:07:32Z" | "2013-09-08T19:46:53Z" | "2013-09-08T19:46:53Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/16023584/pandas-time-delta-from-grouped-neighbors
setup
```
txt = '''ID,DATE
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-05-07 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-06-03 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-13 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-27 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2001-02-01 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2008-01-20 00:00:00
'''
df = pandas.read_csv(StringIO.StringIO(txt))
df = df.sort('DATE')
df.DATE = pandas.to_datetime(df.DATE)
grouped = df.groupby('A')
```
1) this should probably work? (fill in a 0 timedelta)
`grouped.apply(lambda g: g['DATE']-g['DATE'].shift()).fillna(0)`
2) this is returning `timedelta64[us]`, weird numpy error again
```
In [34]: grouped.apply(lambda g: g['DATE']-g['DATE'].shift())
Out[34]:
8 NaT
0 NaT
1 00:00:00
4 NaT
5 00:00:00
2 NaT
3 27 days, 00:00:00
6 NaT
7 14 days, 00:00:00
9 2544 days, 00:00:00
Name: DATE, dtype: timedelta64[us]
```
This DOES work
```
In [57]: df['X_SEQUENCE_GAP'].sort_index().astype('timedelta64[ns]').fillna(0)
Out[57]:
0 00:00:00
1 00:00:00
2 00:00:00
3 27 days, 00:00:00
4 00:00:00
5 00:00:00
6 00:00:00
7 14 days, 00:00:00
8 00:00:00
9 2544 days, 00:00:00
Name: X_SEQUENCE_GAP, dtype: timedelta64[ns]
```
This DOES NOT WORK, (need to use a view of i8 first)
```
In [58]: df['X_SEQUENCE_GAP'].sort_index().astype('timedelta64[ns]').ffill()
ValueError: Invalid dtype for pad_1d [timedelta64[ns]]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3372 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3372/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3372/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3372/events | https://github.com/pandas-dev/pandas/issues/3372 | 13,219,230 | MDU6SXNzdWUxMzIxOTIzMA== | 3,372 | TypeError when writing to excel using numpy.timedelta64 | {
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} | 11 | "2013-04-15T20:53:25Z" | "2015-05-04T12:29:19Z" | "2013-11-10T11:24:26Z" | NONE | null | Let me start by saying that I apologize if this is already on your radar. I looked, but could not find a similar issue. When I try to write out numpy.timedelta64 to excel I get the following error:
```
pandas.__version__
Out[22]: '0.11.0.dev-6cda5dc'
run tester
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/bnice/.virtualenvs/ipython/lib/python2.7/site-packages/IPython/utils/py3compat.pyc in execfile(fname, *where)
176 else:
177 filename = fname
--> 178 __builtin__.execfile(filename, *where)
/Users/bnice/Source/latchmatchcatch/bin/tester.py in <module>()
27 outputter = writeout_string + '.xlsx'
28 writer = pandas.io.parsers.ExcelWriter(outputter)
---> 29 df.to_excel(writer, 'results', index=False)
30
31
/Users/bnice/.virtualenvs/ipython/src/pandas/pandas/core/frame.pyc in to_excel(self, excel_writer, sheet_name, na_rep, float_format, cols, header, index, index_label, startrow, startcol)
1407 formatted_cells = formatter.get_formatted_cells()
1408 excel_writer.write_cells(formatted_cells, sheet_name,
-> 1409 startrow=startrow, startcol=startcol)
1410 if need_save:
1411 excel_writer.save()
/Users/bnice/.virtualenvs/ipython/src/pandas/pandas/io/parsers.pyc in write_cells(self, cells, sheet_name, startrow, startcol)
2192 'cur_sheet property')
2193 if self.use_xlsx:
-> 2194 self._writecells_xlsx(cells, sheet_name, startrow, startcol)
2195 else:
2196 self._writecells_xls(cells, sheet_name, startrow, startcol)
/Users/bnice/.virtualenvs/ipython/src/pandas/pandas/io/parsers.pyc in _writecells_xlsx(self, cells, sheet_name, startrow, startcol)
2210 colletter = get_column_letter(startcol + cell.col + 1)
2211 xcell = wks.cell("%s%s" % (colletter, startrow + cell.row + 1))
-> 2212 xcell.value = _conv_value(cell.val)
2213 if cell.style:
2214 style = CellStyleConverter.to_xlsx(cell.style)
/Users/bnice/.virtualenvs/ipython/lib/python2.7/site-packages/openpyxl/cell.pyc in _set_value(self, value)
322 def _set_value(self, value):
323 """Set the value and infer type and display options."""
--> 324 self.bind_value(value)
325
326 value = property(_get_value, _set_value,
/Users/bnice/.virtualenvs/ipython/lib/python2.7/site-packages/openpyxl/cell.pyc in bind_value(self, value)
311 self.set_value_explicit(value, self.TYPE_NUMERIC)
312 return True
--> 313 self.set_value_explicit(value, self._data_type)
314
315 def _get_value(self):
/Users/bnice/.virtualenvs/ipython/lib/python2.7/site-packages/openpyxl/cell.pyc in set_value_explicit(self, value, data_type)
228 self.TYPE_BOOL: bool, }
229 try:
--> 230 self._value = type_coercion_map[data_type](value)
231 except KeyError:
232 if data_type not in self.VALID_TYPES:
/Users/bnice/.virtualenvs/ipython/lib/python2.7/site-packages/openpyxl/cell.pyc in check_string(self, value)
200 # convert to unicode string
201 if not isinstance(value, unicode):
--> 202 value = unicode(value, self.encoding)
203 value = unicode(value)
204 # string must never be longer than 32,767 characters
TypeError: coercing to Unicode: need string or buffer, numpy.timedelta64 found
```
My code to generate this error is as follows:
```
import pandas, numpy, StringIO, datetime
txt = '''ID,DATE
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
002691c9cec109e64558848f1358ac16,2003-08-13 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-05-07 00:00:00
0088f218a1f00e0fe1b94919dc68ec33,2006-06-03 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
00d34668025906d55ae2e529615f530a,2006-03-09 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-13 00:00:00
0101d3286dfbd58642a7527ecbddb92e,2007-10-27 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2001-02-01 00:00:00
0103bd73af66e5a44f7867c0bb2203cc,2008-01-20 00:00:00
'''
df = pandas.read_csv(StringIO.StringIO(txt))
df = df.sort('DATE')
df.DATE = pandas.to_datetime(df.DATE)
grouped = df.groupby('ID')
df['X_SEQUENCE_GAP'] = pandas.concat([g['DATE'].sub(g['DATE'].shift(), fill_value=0) for title,g in grouped])
now = datetime.datetime.now()
timestamp = now.strftime('%Y%m%d%H%M%S%f')
outfolder = 'output'
out_file_name = 'results'
writeout_string = '../{0}/{1}-{2}'.format(outfolder, out_file_name, timestamp)
outputter = writeout_string + '.xlsx'
writer = pandas.io.parsers.ExcelWriter(outputter)
df.to_excel(writer, 'results', index=False)
```
There is an easy workaround. I can convert the delta to a string in my code. I just wondered if this was expected behavior?
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} | 2 | "2013-04-16T13:30:56Z" | "2013-04-18T20:37:48Z" | "2013-04-16T13:35:25Z" | CONTRIBUTOR | null | With 0.10.1:
``` python
In [1]: pd.__version__
Out[1]: '0.10.1'
In [2]: dates = pd.date_range('01-Jan-2013','01-Jan-2014', freq='H')
...: df = pd.DataFrame(randn(dates.size,5), index=dates)
In [3]: %time print(df)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8761 entries, 2013-01-01 00:00:00 to 2014-01-01 00:00:00
Freq: H
Data columns:
0 8761 non-null values
1 8761 non-null values
2 8761 non-null values
3 8761 non-null values
4 8761 non-null values
dtypes: float64(5)
Wall time: 9 ms
```
With 0.11.0rc1:
``` python
In [1]: pd.__version__
Out[1]: '0.11.0rc1'
In [2]: dates = pd.date_range('01-Jan-2013','01-Jan-2014', freq='H')
...: df = pd.DataFrame(randn(dates.size,5), index=dates)
In [3]: %time print(df)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8761 entries, 2013-01-01 00:00:00 to 2014-01-01 00:00:00
Freq: H
Data columns (total 5 columns):
0 8761 non-null values
1 8761 non-null values
2 8761 non-null values
3 8761 non-null values
4 8761 non-null values
dtypes: float64(5)
Wall time: 3.61 s
```
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} | 1 | "2013-04-16T17:06:56Z" | "2014-04-23T22:23:11Z" | "2014-04-23T22:23:11Z" | MEMBER | null | http://stackoverflow.com/questions/16022094/using-pandas-to-read-text-file-with-leading-whitespace-gives-a-nan-column
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} | 9 | "2013-04-16T17:48:20Z" | "2015-01-26T00:52:19Z" | "2015-01-26T00:52:19Z" | NONE | null | match_time = get_info.index.asof(trade_time)
match_price = get_info[match_time]['Bid Price']
The actual index value:
<Timestamp: 2013-04-10 10:22:01.696815975-0500 CDT, tz=US/Central>
The error returned:
KeyError: u'no item named 2013-04-10 10:22:01.696815-05:00'
It seems to me that it is cutting off the last digits of the timestamp in this example.
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That's the bundled version on precise. Perhaps just headless travis issues.
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} | 2 | "2013-04-17T00:13:10Z" | "2014-06-30T05:21:11Z" | "2013-04-17T22:40:58Z" | MEMBER | null | #2903 solves the case for the first level but using the same variables from that issue, the following code raises a `TypeError` for the second level:
``` python
import pandas as pd
from numpy.random import rand
columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), ('b', 'hello'), ('b', 'world')], names=['lvl0', 'lvl1'])
df = pd.DataFrame(rand(4, 4), columns=columns)
df.xs('foo', level='lvl1', axis=1)
```
This pull request fixes that.
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} | 2 | "2013-04-17T01:02:05Z" | "2014-06-19T05:28:32Z" | "2013-04-23T02:13:36Z" | NONE | null | In the same vein as #3038 , make `repr()` output valid python code where feasible. I find this not
being the case inconvenient for example when issues contain code examples,
and the data needs to be recreated by hand rather then merely copy pasted.
```
In [2]: zs=[Timestamp('99-04-17 00:00:00',tz='UTC'),
...: Timestamp('2001-04-17 00:00:00',tz='UTC'),
...: Timestamp('2001-04-17 00:00:00',tz='America/Los_Angeles'),
...: Timestamp('2001-04-17 00:00:00',tz=None)]
...: for z in zs:
...: print repr(z)
...: assert eval(repr(z)) == z
Timestamp('1999-04-17 00:00:00+0000', tz='UTC')
Timestamp('2001-04-17 00:00:00+0000', tz='UTC')
Timestamp('2001-04-17 00:00:00-0700', tz='America/Los_Angeles')
Timestamp('2001-04-17 00:00:00', tz=None)
```
Misgivings?
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} | 1 | "2013-04-17T13:57:37Z" | "2013-04-20T14:33:09Z" | "2013-04-20T14:33:09Z" | NONE | null | I have a dataset that is inherently hierarchical (from some experiments). For each large category 1, there are several category 2 entries, and in turn for each cat2 entry there are several cat3 entries. For each (cat1, cat2, cat3) tuple I have a measurement value, val below.
The analysis I am doing is per category 1. I first rank all values within cat1, and then I use those ranks to calculate a statistic for each cat2. The code below exemplifies, what I am doing.
When I group by cat1 and apply a function for the calculations, I get an error when I assign to the group. This can be reproduced with the below code.
The error goes away when a copy of the sub-DataFrame is made (in comment below in function f) in the applied function.
I am prepared to think that this should indeed raise an error, as it doesn't strike me as unreasonable that altering the groups over which one is iterating can be bad idea. However, if this is indeed expected behavior, then it's not mentioned in the documentation (as far as I can tell).
Looking forward to any comments on this.
# example of assignment in groupby
mydf = pd.DataFrame({
'cat1' : ['a'] \* 8 + ['b'] \* 6,
'cat2' : ['c'] \* 2 + ['d'] \* 2 + ['e'] \* 2 + ['f'] \* 2 + ['c'] \* 2 + ['d'] \* 2 + ['e'] \* 2,
'cat3' : map(lambda x: 'g{}'.format(x), range(1,15)),
'val' : np.random.randint(100, size=14),
})
def f(x):
# no error if copy is used:
# x = x.copy()
# the following assignment causes an error
x['rank'] = x.val.rank(method='min')
return x.groupby('cat2')['rank'].min()
grpby = mydf.groupby('cat1').apply(f)
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} | 4 | "2013-04-17T14:07:16Z" | "2013-05-10T10:57:28Z" | "2013-05-10T10:57:28Z" | NONE | null | I recently upgraded from pandas 0.90 to 0.10.1 and it seems a merge I was using is now broken. For example, I have two dataframes that I am left merging on a Timestamp typed column.
The first dataframe has three columns: Timestamp, float, Timestamp:
df1 = pd.DataFrame({'time':[pd.Timestamp('2013-01-01'),pd.Timestamp('2013-01-02')],'val':[15.6, 15.9], 'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-01-16')]})
The second DataFrame has three columns: Timestamp, Timestamp. float:
df2 = pd.DataFrame({'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-02-13')], 'badjfact':[.98, .78], 'volmat':[pd.Timestamp('2013-02-15'), pd.Timestamp('2013-03-15')]})
Merging these works using the following command:
> > > pd.merge(df1, df2, on='expiry', how='left')
> > > expiry time val badjfact volmat
> > > 0 2013-01-16 00:00:00 2013-01-01 00:00:00 15.6 0.98 2013-02-15 00:00:00
> > > 1 2013-01-16 00:00:00 2013-01-02 00:00:00 15.9 0.98 2013-02-15 00:00:00
However, when I change the 'val' column in DataFrame 1 to a string, (or if I add a new string column to DataFrame 1), the merge will then have NaN values from the second dataframe:
df1 = pd.DataFrame({'time':[pd.Timestamp('2013-01-01'),pd.Timestamp('2013-01-02')],'val':['test1', 'test2'], 'expiry':[pd.Timestamp('2013-01-16'), pd.Timestamp('2013-01-16')]})
Broken output:
> > > pd.merge(df1, df2, on='expiry', how='left')
> > > expiry time val badjfact volmat
> > > 0 2013-01-16 00:00:00 2013-01-01 00:00:00 test1 NaN NaT
> > > 1 2013-01-16 00:00:00 2013-01-02 00:00:00 test2 NaN NaT
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```
pd.Int64Index(range(10)).max()
```
Similarly for `min()`, but `mean()` works.
Pandas version: 0.10.0
System: Windows 8 64 bits
Python 2.7.3 64 bits
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} | 2 | "2013-04-17T14:54:52Z" | "2014-06-24T18:14:36Z" | "2013-04-20T14:32:53Z" | CONTRIBUTOR | null | and fallback to non-fast apply
fixes #3380
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} | 1 | "2013-04-17T18:43:34Z" | "2013-08-16T19:32:47Z" | "2013-08-16T19:32:47Z" | CONTRIBUTOR | null | Thought had fixed this.....but guess not
http://stackoverflow.com/questions/16067144/in-pandas-how-to-use-fillna-to-fill-the-whole-columns-with-string-if-the-column/16067653#16067653
```
In [2]: csv=u"""a,a,,a
b,b,,b
c,c,,c
"""
In [3]: df = pd.read_csv(StringIO(csv), header=None)
In [4]: df
Out[4]:
0 1 2 3
0 a a NaN a
1 b b NaN b
2 c c NaN c
In [5]: df.dtypes
Out[5]:
0 object
1 object
2 float64
3 object
dtype: object
In [6]: df.fillna({ 2: 'foo' })
ValueError: could not convert string to float: foo
```
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} | 2 | "2013-04-17T22:12:28Z" | "2016-10-12T23:03:43Z" | "2013-04-22T14:38:51Z" | MEMBER | null | There's a call to `to_string` in `DataFrame._repr_fits_horizontal` that is **drastically** slowing down `repr`ing of large `DataFrame`s and incidentally makes the option `max_info_rows` useless since that call occurs before `max_info_rows` comes into play.
Is there any way to compute the length of the column `repr` without using the entire frame as a string? Also, for large frames that will create tons of temporary strings that could cause a segfault if one doesn't have swap enabled (that's probably a bit alarmist; I'm trying to be thorough here. :)) At best it will raise a `MemoryError` exception.
What's the thinking here? One could check `max_info_rows` in `DataFrame._repr_fits_horizontal`, but that's more of a stopgap solution than an actual one. Using `cStringIO` is not a viable solution: it only gives a ~29 ms speedup. Also, should there be a vbench for `repr`ing?
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} | 2 | "2013-04-18T07:54:52Z" | "2013-10-02T21:04:41Z" | "2013-09-22T00:36:50Z" | NONE | null | For some future release the margins of a plot could be set using an matplotlib rcparam to make it look better (see [this question](http://stackoverflow.com/q/13485623/1301710) on SO). For mpl version > 1.2.1
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} | 4 | "2013-04-18T11:11:15Z" | "2013-04-21T12:39:15Z" | "2013-04-21T12:39:15Z" | NONE | null | E:\pandas-master>c:\WinPython-32bit-3.3.0.0beta2\python-3.3.0\python.exe setup.p
y build --compiler=mingw32
<snip>
In file included from c:\WinPython-32bit-3.3.0.0beta2\python-3.3.0\lib\site-pack
ages\numpy\core\include/numpy/ndarraytypes.h:1728:0,
from pandas/src/period.h:11,
from pandas/src/period.c:1:
c:\WinPython-32bit-3.3.0.0beta2\python-3.3.0\lib\site-packages\numpy\core\includ
e/numpy/npy_deprecated_api.h:8:9: note: #pragma message: c:\WinPython-32bit-3.3.
0.0beta2\python-3.3.0\lib\site-packages\numpy\core\include/numpy/npy_deprecated_
api.h(8) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_
NO_DEPRECATED_API NPY_1_7_API_VERSION
writing build\temp.win32-3.3\Release\pandas\tslib.def
C:\MinGW32-xy\bin\gcc.exe -mno-cygwin -shared -s build\temp.win32-3.3\Release\pa
ndas\tslib.o build\temp.win32-3.3\Release\pandas\src\datetime\np_datetime.o buil
d\temp.win32-3.3\Release\pandas\src\datetime\np_datetime_strings.o build\temp.wi
n32-3.3\Release\pandas\src\period.o build\temp.win32-3.3\Release\pandas\tslib.de
f -Lc:\WinPython-32bit-3.3.0.0beta2\python-3.3.0\libs -Lc:\WinPython-32bit-3.3.0
.0beta2\python-3.3.0\PCbuild -lpython33 -lmsvcr100 -o build\lib.win32-3.3\pandas
\tslib.pyd
c:/mingw32-xy/bin/../lib/gcc/mingw32/4.5.2/../../../../mingw32/bin/ld.exe: canno
t find -lmsvcr100
collect2: ld returned 1 exit status
error: command 'gcc' failed with exit status 1
In order to build pandas for Python 3.3 under windows, I have to install Visual Studio (VS 2010 works). It would be nice if mingw32 compiler works.
Thanks in advance.
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} | 5 | "2013-04-18T11:45:03Z" | "2013-09-22T00:28:51Z" | "2013-09-22T00:28:51Z" | CONTRIBUTOR | null | from ML
https://groups.google.com/forum/?fromgroups=#!topic/pydata/fzPGjVrj9lU
```
df = pd.DataFrame.from_items([('a', [1,2]), ('b', ([3,4]))])
df.index = pd.MultiIndex.from_tuples([('A', 'B'),('C', 'D')],
names=['k1', 'k2'])
# A dataframe, with a multiindex:
>>> df
a b
k1 k2
A B 1 3
C D 2 4
```
`.ix[('A','B')]` succeeds
`.ix['A','B']` succeeds as the `A, B` is tried as a tuple (`.ix` tries hard here)
propose that we change `.loc` to disallow the 2nd case and instead raise a KeyError
its more 'strict'
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} | 19 | "2013-04-18T17:01:17Z" | "2014-01-03T20:44:29Z" | "2013-12-20T15:55:34Z" | NONE | null | Perhaps I've opened a dupe issue in the past, can't find it.
`pd.options.display.max_seq_items` should not be None by default.
It's easy to hang the terminal by repr()ing with some mal-chosen data.
#3426
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} | 2 | "2013-04-18T17:10:14Z" | "2013-12-20T02:20:55Z" | "2013-12-20T02:20:55Z" | CONTRIBUTOR | null | run through file and find the positions that are blank on every line
use the first position if there is a block
useful for small files and/or copy-pasting small DataFrame console output and then reconstructing via read_fwf
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} | 298 | "2013-04-18T17:44:05Z" | "2016-10-12T23:03:43Z" | "2013-09-16T18:59:14Z" | CONTRIBUTOR | null | Provide a top-level `eval` function, something like:
`pd.eval(function_or_string,method=None, **kwargs)`
to support things like:
1) out-of-core computation (locally) (see #3202)
2) string evaluation which does not use python lazy evaluation
(so pandas can process effiiently)
`pd.eval('df + df2',method='numexpr')` (or maybe default to numexpr)
see also:
http://stackoverflow.com/questions/16527491/python-perform-operation-in-string
3) possible out-of-pandas evaluation
`pd.eval('df + df2',method='picloud')`
http://www.picloud.com/ (though they seem to have really old versions of pandas),
but I think they handle it anyhow
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} | 5 | "2013-04-18T19:13:10Z" | "2016-09-29T22:44:20Z" | "2016-09-29T22:30:18Z" | CONTRIBUTOR | null | http://carray.pytables.org/docs/manual/
obviously these also offer persistence as well
but this is a another dependence
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} | 20 | "2013-04-18T20:37:48Z" | "2014-06-22T23:48:34Z" | "2013-04-22T14:38:01Z" | CONTRIBUTOR | null | My apologies for the mess i created on the dataframe repr. Not only did i get the non-interactive behavior wrong, i also introduced a performance issue in the interactive mode.
This PR should make my wrong, right again. In non-interactive mode i re-enabled concise formats and made sure that auto-terminal-size detection is not used (little test added), also performance issue reported in #3337 and #3373 is fixed (added a benchmark for it).
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https://api.github.com/repos/pandas-dev/pandas/issues/3396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3396/events | https://github.com/pandas-dev/pandas/issues/3396 | 13,376,566 | MDU6SXNzdWUxMzM3NjU2Ng== | 3,396 | 11.0 problems with hd5store table remove method | {
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} | [] | closed | false | null | [] | null | 6 | "2013-04-18T22:22:44Z" | "2013-04-22T13:36:22Z" | "2013-04-22T13:36:22Z" | NONE | null | I have a hd5store that accumulates data by batch. However the batches overlap and I want to just add the new rows..
The data is in a table with a major index of event timestamp. I can assume the new batch has complete data for the first day so all I need to do is to remove all rows with a timestamp >= min_new_date.
I am using python 2.7, pandas 0.11.0.dev-b41dc91 on Windows 7 64bit and lget the following error however I tried to pass in the date In this example I hard code the date string to simplify the example.
dstore = pyt.HDFStore(hdfile)
dstore
Out[39]:
<class 'pandas.io.pytables.HDFStore'>
File path: Y:\HAVI_MONITORING\Log_Scan_Files\ROPLogHistComp2.h5
/files series (shape->[1])
/logdata frame_table (typ->appendable_multi,nrows->1148964,ncols->13,indexers->[index],dc->[FileId,Start,Wob,Start_date,Type_1])
/meta frame (shape->[1,1])
/stores frame_table (typ->appendable,nrows->1123,ncols->20,indexers->[index])
## dstore.remove('logdata','major_axis>=20130327')
Exception Traceback (most recent call last)
<ipython-input-40-9cc7b5a31bc1> in <module>()
----> 1 dstore.remove('logdata','major_axis>=20130327')
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in remove(self, key, where, start, stop)
546 if not s.is_table:
547 raise Exception('can only remove with where on objects written as tables')
--> 548 return s.delete(where = where, start=start, stop=stop)
549
550 def append(self, key, value, columns=None, **kwargs):
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in delete(self, where, *_kwargs)
2748 # create the selection
2749 table = self.table
-> 2750 self.selection = Selection(self, where, *_kwargs)
2751 values = self.selection.select_coords()
2752
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in **init**(self, table, where, start, stop, **kwargs)
3275 self.coordinates = where.values
3276 else:
-> 3277 self.terms = self.generate(where)
3278
3279 # create the numexpr & the filter
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in generate(self, where)
3304
3305 queryables = self.table.queryables()
-> 3306 return [Term(c, queryables=queryables) for c in where]
3307
3308 def select(self):
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in **init**(self, field, op, value, queryables)
3132
3133 if len(self.q):
-> 3134 self.eval()
3135
3136 def **str**(self):
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in eval(self)
3158
3159 if not self.is_valid:
-> 3160 raise Exception("query term is not valid [%s]" % str(self))
3161
3162 # convert values if we are in the table
Exception: query term is not valid [field->major_axis,op->>=,value->['20130327']]
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in **init**(self, table, where, start, stop, **kwargs)
3275 self.coordinates = where.values
3276 else:
-> 3277 self.terms = self.generate(where)
3278
3279 # create the numexpr & the filter
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in generate(self, where)
3304
3305 queryables = self.table.queryables()
-> 3306 return [Term(c, queryables=queryables) for c in where]
3307
3308 def select(self):
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in **init**(self, field, op, value, queryables)
3132
3133 if len(self.q):
-> 3134 self.eval()
3135
3136 def **str**(self):
C:\Python27\lib\site-packages\pandas\io\pytables.pyc in eval(self)
3158
3159 if not self.is_valid:
-> 3160 raise Exception("query term is not valid [%s]" % str(self))
3161
3162 # convert values if we are in the table
Exception: query term is not valid [field->major_axis,op->>=,value->['20130327']]
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https://api.github.com/repos/pandas-dev/pandas/issues/3397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3397/events | https://github.com/pandas-dev/pandas/pull/3397 | 13,380,836 | MDExOlB1bGxSZXF1ZXN0NTI1MDY1Mg== | 3,397 | ENH: speed up testing when --processes arg is given to test_fast.sh | {
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} | [] | closed | false | null | [] | null | 9 | "2013-04-19T00:20:24Z" | "2014-06-29T16:35:33Z" | "2013-05-03T03:18:02Z" | MEMBER | null | I know this is a stupid one liner but...this allows the pytables fixtures to be shared, but not split (because PyTables is not reentrant [only reading is thread-safe]), during a test run with multiple processors. I get a 3x speed up (75 secs to 25 secs) just by adding this single class variable.
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} | 4 | "2013-04-19T07:25:20Z" | "2013-04-23T00:17:26Z" | "2013-04-22T23:58:21Z" | CONTRIBUTOR | null | ``` python
In [69]: data = """\
....: A,B,C
....: foo,,
....: ,bar,
....: ,,baz
....: """
In [70]: pd.read_csv(StringIO(data))
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
...
CParserError: Error tokenizing data. C error: EOF inside string starting at line 2
```
.
``` python
In [79]: data = """\
....: A,B
....: foo,
....: ,bar
....: """
In [80]: pd.read_csv(StringIO(data))
Out[80]:
A B
0 foo NaN
1 bar("") NaN
```
This is fine
``` python
In [67]: data = """\
....: A,B,C
....: ,,foo
....: ,bar,
....: baz,,
....: """
In [68]: pd.read_csv(StringIO(data))
Out[68]:
A B C
0 NaN NaN foo
1 NaN bar NaN
2 baz NaN NaN
```
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} | 9 | "2013-04-19T09:32:59Z" | "2014-06-14T17:35:39Z" | "2013-04-20T14:32:40Z" | NONE | null | This in part addresses #3340.
I added a few comments in the doc that point users ad using the pandas
at, iat, loc, iloc, etc. methods and included an example similar to
the one exposed in #3340 that addresses some of the reference / value
intricaies encountered with pandas and numpy objects.
To make things clear I selected a rather verbose style.
Let me know if this is over the top ;-)
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https://api.github.com/repos/pandas-dev/pandas/issues/3400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3400/events | https://github.com/pandas-dev/pandas/issues/3400 | 13,398,358 | MDU6SXNzdWUxMzM5ODM1OA== | 3,400 | strange `read_table` error message | {
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 3 | "2013-04-19T12:10:48Z" | "2013-09-30T13:15:22Z" | "2013-09-30T13:15:15Z" | CONTRIBUTOR | null | Hi, I have a ASCII file I want to read with `read_table`. I put the file here: https://gist.github.com/andreas-h/5419916.
I want to read this file like this:
```
read_table("gistfile1.txt", sep="\s*")
```
However I get this error:
```
ValueError: Expected 6 fields in line 38, saw 7
```
Okay, line 38 looks different. It starts with an asterisk `*`. So I try to define the asterisk as a comment:
```
read_table("gistfile1.txt", sep="\s*", comment="*")
```
Still, the error remains the same.
Now, if I try to limit the number of rows to 466, it works:
```
read_table("gistfile1.txt", sep="\s*", comment="*", nrows=466)
```
If I set the limit to anything larger than 466, it fails with the ValueError about line 38:
```
read_table("gistfile1.txt", sep="\s*", comment="*", nrows=467)
[...]
ValueError: Expected 6 fields in line 38, saw 7
```
This seems to be a bug, but I cannot figure out what's the problem.
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