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closes #4102.
closes #4194.
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} | 4 | 2013-07-02T16:23:21Z | 2013-09-27T21:32:37Z | 2013-09-27T21:32:37Z | MEMBER | null | closes #4103.
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https://api.github.com/repos/pandas-dev/pandas/issues/4106 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4106/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4106/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4106/events | https://github.com/pandas-dev/pandas/pull/4106 | 16,279,033 | MDExOlB1bGxSZXF1ZXN0NjY4NDQ0NA== | 4,106 | ENH: add dropna argument to pivot_table | {
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} | [] | closed | false | null | [] | null | 17 | 2013-07-02T18:16:57Z | 2014-07-16T08:17:15Z | 2013-07-10T13:20:05Z | CONTRIBUTOR | null | fixes #3820
```
a = np.array(['foo', 'foo', 'foo', 'bar', 'bar', 'foo', 'foo'], dtype=object)
b = np.array(['one', 'one', 'two', 'one', 'two', 'two', 'two'], dtype=object)
c = np.array(['dull', 'dull', 'dull', 'dull', 'dull', 'shiny', 'shiny'], dtype=object)
In [11]: pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'], drop_na=False)
Out[11]:
b one two
c dull shiny dull shiny
a
bar 1 0 1 0
foo 2 0 1 2
```
Also same argument for `pivot_table`.
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} | 2 | 2013-07-02T22:47:36Z | 2013-07-03T00:04:38Z | 2013-07-03T00:04:38Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/17432944/python-pandas-error-when-doing-groupby-counts
The below example doesn't make sense, but perhaps the error message could be clearer...
```
df = pd.DataFrame(np.random.randint(0,2,(4,4)),
columns=['a', 'b', 'c', 'd'])
In [11]: df
Out[11]:
a b c d gr
0 0 1 1 0 1
1 1 1 1 0 1
2 0 1 1 0 1
3 1 1 0 1 0
In [12]: df.groupby(['b', 'c']).count()
Out[12]:
a b c d gr
b c
1 0 1 1 1 1 1
1 3 3 3 3 3
df['gr'] = df.groupby(['b', 'c']).count()
---------------------------------------------------------------------------
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
```
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} | 0 | 2013-07-02T23:31:31Z | 2014-07-16T08:17:18Z | 2013-07-03T00:04:38Z | CONTRIBUTOR | null | closes #4107
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} | 1 | 2013-07-03T02:08:07Z | 2014-03-30T15:14:50Z | 2014-03-30T15:14:50Z | NONE | null | prob related to #4069
import pandas as pd
from pandas.tseries.offsets import *
help(BQuarterEnd)
Help on class BQuarterEnd in module pandas.tseries.offsets:
class BQuarterEnd(DateOffset, CacheableOffset)
| DateOffset increments between business Quarter dates
| startingMonth = 1 corresponds to dates like 1/31/2007, 4/30/2007, ...
| startingMonth = 2 corresponds to dates like 2/28/2007, 5/31/2007, ...
| startingMonth = 3 corresponds to dates like 3/30/2007, 6/29/2007, ...
|
| Method resolution order:
| BQuarterEnd
| DateOffset
| CacheableOffset
| **builtin**.object
|
| Methods defined here:
|
| **init**(self, n=1, **kwds)
import datetime as dt
d = dt.datetime(2008, 8, 18)
dw = d+ BQuarterEnd(1)
dw
Out[10]: datetime.datetime(2008, 9, 30, 0, 0)
dw = d+ BQuarterEnd(1,2)
---
TypeError Traceback (most recent call last)
<ipython-input-8-2f6514a26e56> in <module>()
----> 1 dw = d+ BQuarterEnd(1,2)
TypeError: **init**() takes at most 2 arguments (3 given)
I am looking for Business quarter end which end APR, and MAY deflaut is JUN, or those three month cycles...
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} | 1 | 2013-07-03T02:08:25Z | 2014-01-29T20:26:50Z | 2014-01-29T20:26:50Z | NONE | null | From http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.sql.write_frame.html :
```
Parameters: (...)
if_exists: {‘fail’, ‘replace’, ‘append’}, default ‘fail’ :
fail: If table exists, do nothing. replace: If table exists, drop it, recreate it,
and insert data. append: If table exists, insert data. Create if does not exist.
```
Everything works fine as long as the table does not exist.
If table already exists :
``` Python
sql.write_frame(sp5002, name="sp500", con=conn, if_exists='replace')
```
-> "sqlite3.OperationalError: no such table: sp500"
Reason is that there is no new "create" statement after the drop statement:
``` Python
create = None
if exists and if_exists == 'replace':
create = "DROP TABLE %s" % name
elif not exists:
create = get_schema(frame, name, flavor)
```
In my opinion it should be changed to something like: (?)
``` Python
create = None
if exists and if_exists == 'replace':
create = "DROP TABLE %s" % name + "\n"+get_schema(frame, name, flavor)
elif not exists:
create = get_schema(frame, name, flavor)
```
If i manually work around that, everything works as expected:
``` Python
exists = sql.table_exists('sp500', conn, 'sqlite')
if not exists:
sql.write_frame(sp5002, name="sp500", con=conn, if_exists='replace')
else:
create = "DROP TABLE sp500"
cur = conn.cursor()
cur.execute(create)
cur.close()
sql.write_frame(sp5002, name="sp500", con=conn, if_exists='replace')
```
#2971
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} | 18 | 2013-07-03T15:35:54Z | 2016-10-12T23:03:57Z | 2013-07-03T18:59:42Z | CONTRIBUTOR | null | the `DataFrame.replace` seems to be broken in 0.12 development version
this code:
```
df = pd.DataFrame({'Type':['Q','T','Q','Q','T'], 'tmp':2})
print df
print df.replace({'Type': {'Q': 0, 'T': 1}})
```
Return correct result in 0.11.0:
```
Type tmp
0 Q 2
1 T 2
2 Q 2
3 Q 2
4 T 2
5 T 2
Type tmp
0 0 2
1 1 2
2 0 2
3 0 2
4 1 2
5 1 2
```
and in 0.12.0.dev-795004d it does not work:
```
Type tmp
0 Q 2
1 T 2
2 Q 2
3 Q 2
4 T 2
5 T 2
Type tmp
0 0 2
1 1 2
2 0 2
3 1 2
4 0 2
5 1 2
```
Series.replace it works well, so the workaround for this bug is the following:
```
df['Type'] = df['Type'].replace({'Q': 0, 'T': 1})
```
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} | 0 | 2013-07-03T15:38:24Z | 2014-02-13T23:12:31Z | 2014-02-13T23:12:31Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/17451843/copying-a-single-index-dataframe-into-a-multiindex-dataframe/17452237#17452237
```
IIn [3]: import itertools
In [4]: inner = ('a','b')
In [5]: outer = ((10,20), (1,2))
In [6]: cols = ('one','two','three','four')
In [7]: sngl = pd.DataFrame(np.random.randn(2,4), index=inner, columns=cols)
IIn [8]: index_tups = list(itertools.product(*(outer + (inner,))))
In [9]: index_mult = pd.MultiIndex.from_tuples(index_tups)
In [10]: mult = pd.DataFrame(index=index_mult, columns=cols)
```
and the solution
```
In [48]: nm = mult.reset_index().set_index('level_2')
In [49]: nm
Out[49]:
level_0 level_1 one two three four
level_2
a 10 1 NaN NaN NaN NaN
b 10 1 NaN NaN NaN NaN
a 10 2 NaN NaN NaN NaN
b 10 2 NaN NaN NaN NaN
a 20 1 NaN NaN NaN NaN
b 20 1 NaN NaN NaN NaN
a 20 2 NaN NaN NaN NaN
b 20 2 NaN NaN NaN NaN
```
A bug here????
This should probably work with a series on the rhs; this might be a buglet
```
In [50]: nm.loc['a',sngl.columns] = sngl.loc['a'].values
In [51]: nm
Out[51]:
level_0 level_1 one two three four
level_2
a 10 1 0.3738456 -0.2261926 -1.205177 0.08448757
b 10 1 NaN NaN NaN NaN
a 10 2 0.3738456 -0.2261926 -1.205177 0.08448757
b 10 2 NaN NaN NaN NaN
a 20 1 0.3738456 -0.2261926 -1.205177 0.08448757
b 20 1 NaN NaN NaN NaN
a 20 2 0.3738456 -0.2261926 -1.205177 0.08448757
b 20 2 NaN NaN NaN NaN
In [52]: nm.reset_index().set_index(['level_0','level_1','level_2'])
Out[52]:
one two three four
level_0 level_1 level_2
10 1 a 0.3738456 -0.2261926 -1.205177 0.08448757
b NaN NaN NaN NaN
2 a 0.3738456 -0.2261926 -1.205177 0.08448757
b NaN NaN NaN NaN
20 1 a 0.3738456 -0.2261926 -1.205177 0.08448757
b NaN NaN NaN NaN
2 a 0.3738456 -0.2261926 -1.205177 0.08448757
b NaN NaN NaN NaN
```
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} | 2 | 2013-07-03T16:08:37Z | 2014-07-16T08:17:27Z | 2013-07-03T18:59:43Z | MEMBER | null | closes #4115.
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} | 9 | 2013-07-03T16:22:02Z | 2013-09-27T03:25:34Z | 2013-09-27T03:25:34Z | MEMBER | null | Currently `DataFrame` and `Series` do slightly different things when their respective `replace` methods are called and accept slightly different arguments. This should be rectified in 0.13.
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} | 0 | 2013-07-03T21:05:14Z | 2013-07-03T23:43:15Z | 2013-07-03T23:43:15Z | CONTRIBUTOR | null | docstring seem to suggest that this ought to convert to NaN:
```
In [10]: s = pd.Series([1, 'na', 3 ,4])
In [11]: s.convert_objects(convert_numeric=True)
Out[11]:
0 1
1 na
2 3
3 4
dtype: object
```
Docstring:
```
convert_numeric : boolean, default True
if True attempt to coerce to numbers (including strings),
non-convertibles get NaN
```
However, does work if the non-strings are stringified first
```
In [142]: pd.Series([1, 'na', 3 ,4]).astype(str).convert_objects(convert_numeric=True)
Out[142]:
0 1
1 NaN
2 3
3 4
dtype: float64
```
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} | 0 | 2013-07-03T21:37:43Z | 2014-07-16T08:17:29Z | 2013-07-03T23:43:15Z | CONTRIBUTOR | null | closes #4119
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} | 7 | 2013-07-03T23:06:45Z | 2013-07-03T23:14:19Z | 2013-07-03T23:14:19Z | CONTRIBUTOR | null | just failed under 2.7 & 3.3
unrelated to my PR
https://travis-ci.org/jreback/pandas/jobs/8716024
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} | 9 | 2013-07-04T01:46:08Z | 2013-07-04T16:05:03Z | 2013-07-04T15:11:52Z | CONTRIBUTOR | null | getting test failures on the windows builds. this is from py27 64-bit:
```
======================================================================
ERROR: test_replace_convert (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test64-py27\pandas\tests\test_frame.py", line 6869, in test_replace_convert
assert_series_equal(expec, res)
File "C:\workspace\pandas-windows-test64-py27\pandas\util\testing.py", line 197, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "C:\workspace\pandas-windows-test64-py27\pandas\util\testing.py", line 141, in assert_almost_equal
assert_almost_equal(a[i], b[i], check_less_precise)
File "C:\workspace\pandas-windows-test64-py27\pandas\util\testing.py", line 146, in assert_almost_equal
if isnull(a):
File "C:\workspace\pandas-windows-test64-py27\pandas\core\common.py", line 60, in isnull
return _isnull(obj)
File "C:\workspace\pandas-windows-test64-py27\pandas\core\common.py", line 74, in _isnull_new
return _isnull_ndarraylike(obj)
File "C:\workspace\pandas-windows-test64-py27\pandas\core\common.py", line 135, in _isnull_ndarraylike
values = np.asarray(obj)
File "c:\python27\lib\site-packages\numpy\core\numeric.py", line 235, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: invalid __array_struct__
======================================================================
FAIL: test_insert_column_bug_4032 (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test64-py27\pandas\tests\test_frame.py", line 10084, in test_insert_column_bug_4032
assert_frame_equal(result,expected)
File "C:\workspace\pandas-windows-test64-py27\pandas\util\testing.py", line 238, in assert_frame_equal
check_less_precise=check_less_precise)
File "C:\workspace\pandas-windows-test64-py27\pandas\util\testing.py", line 199, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
```
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} | 1 | 2013-07-04T14:48:14Z | 2015-09-09T21:41:02Z | 2015-09-09T21:41:01Z | CONTRIBUTOR | null | ```
In [57]: pi = pd.period_range('2002-01','2003-12',freq='M')
In [58]: pi
Out[58]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: M
[2002-01, ..., 2003-12]
length: 24
In [59]: p = pd.Panel(randn(24,5,10),items=pi)
In [60]: p
Out[60]:
<class 'pandas.core.panel.Panel'>
Dimensions: 24 (items) x 5 (major_axis) x 10 (minor_axis)
Items axis: 2002-01 to 2003-12
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 9
```
This is ok
```
In [61]: p.ix[list(pi)]
Out[61]:
<class 'pandas.core.panel.Panel'>
Dimensions: 24 (items) x 5 (major_axis) x 10 (minor_axis)
Items axis: 2002-01 to 2003-12
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 9
```
This raises?
```
In [62]: p.ix[pi]
DateParseError: day is out of range for month
```
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} | 0 | 2013-07-04T15:05:42Z | 2016-06-08T12:36:32Z | 2016-06-08T11:04:50Z | CONTRIBUTOR | null | Change `unique` to return an Index/MultiIndex as needed (rather than an ndarray)
```
In [81]: tuples = [(0, 100, 1000),(0, 100, 1001),(0, 100, 1002), (1, 101, 1001)]
In [82]: index_3levels=pd.MultiIndex.from_tuples(tuples,names=["l1","l2","l3"])
In [83]: index_3levels
Out[83]:
MultiIndex
[(0, 100, 1000), (0, 100, 1001), (0, 100, 1002), (1, 101, 1001)]
In [84]: index_3levels.droplevel('l3')
Out[84]:
MultiIndex
[(0, 100), (0, 100), (0, 100), (1, 101)]
```
This becomes more intuitive (or maybe add `uniquify` keyword to droplevel?
```
In [85]: MultiIndex.from_tuples(index_3levels.droplevel('l3').unique())
Out[85]:
MultiIndex
[(0, 100), (1, 101)]
```
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} | [] | closed | false | null | [] | null | 6 | 2013-07-04T16:33:39Z | 2014-07-16T08:17:34Z | 2013-07-05T13:01:31Z | CONTRIBUTOR | null | Noticed a typo in the documentation of `write_frame`.
Method def has `con` while documentation had `conn`.
`def write_frame(frame, name, con, flavor='sqlite', if_exists='fail', **kwargs):`
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} | [] | closed | false | null | [] | null | 2 | 2013-07-04T23:24:22Z | 2014-07-16T08:17:35Z | 2013-07-04T23:34:56Z | NONE | null | Roughly from 40 minutes to 13 minutes.
Before: https://travis-ci.org/pydata/pandas/builds/8738816
After: https://travis-ci.org/y-p/pandas/builds/8749214
The existing build cache system for 2to3 and cythonized caching can knock this
down much more, but it's more fragile and part of the reason the previous attempt
ultimately failed. Really need to do that in a jenkins server somewhere , if at all.
There's no opt-in/whitelist anymore. faster for everyone.
The building of the dependencies (a rare occurence) is automated in a provisioning
script for vagrant under ci/speedpack. should just be 'vagrant up' and step away for an hour.
The package files are hosted on a private openshift server but if a bus hits me it's all here for
someone to plunk down a static file webserver somewhere and tweak the url.
In case this goes bad and I'm not around to notice, there's one line to comment in
ci/install.sh. cc @jreback @cpcloud
enjoy.
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} | [] | closed | false | null | [] | null | 2 | 2013-07-05T10:14:44Z | 2013-07-05T10:54:12Z | 2013-07-05T10:54:12Z | NONE | null | to_dict is an ideal option for exporting data to json format. Unfortunately, python "json" (json.dumps) package doesn't accept keys as integers, it expects strings only and throws TypeError when it receives anything else. This is trivial to workaround, however in terms of speed, it'd better to have a choice to either have string keys or integers.
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} | 2 | 2013-07-05T13:08:56Z | 2020-10-05T00:10:12Z | null | CONTRIBUTOR | null | related #2813, #4964 (somewhat)
At the moment rolling_apply (et al) the passed function has to return a value (unlike apply which can return a Series and overall return a DataFrame).
If you could pass it a Series you could, for example, do this:
```
from pandas.core.algorithms import value_counts
s = Series([1, 1, 2, 3, 1, 2, 3, 2, 1])
pd.rolling_apply(s, 3, value_counts)
```
from the [mailing list](https://groups.google.com/forum/#!topic/pydata/I7YET3egFtY).
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} | 3 | 2013-07-05T13:17:04Z | 2013-07-07T04:40:23Z | 2013-07-06T04:17:34Z | CONTRIBUTOR | null | The parse method in the ExcelFile class does not pass the argument _keep_default_na_ to the TextParser from pandas.io.parsers. There is no clean way to override the default na values when parsing excel files with the current code.
My specific problem involves "NA" as a typical value for "North America" in excel files.
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} | 13 | 2013-07-05T13:27:52Z | 2013-07-06T01:17:48Z | 2013-07-05T22:38:54Z | CONTRIBUTOR | null | Just to note that this build failed, which is strange since the commit #4127 seems to only change one character in a docstring, I'm assuming it's a travis issue?
https://travis-ci.org/pydata/pandas/jobs/8765391
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} | 4 | 2013-07-05T15:10:22Z | 2014-01-26T18:58:24Z | 2014-01-26T18:58:24Z | CONTRIBUTOR | null | When I have a Panel with a PeriodIndex with `freq="M"` and I use `mypanel.to_excel()`, then the PeriodIndex gets represented as `YYYY-MM-DD` in the output Excel file. I would expect it to be rather `YYYY-MM`.
Is this a bug or a feature?
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} | 0 | 2013-07-05T15:47:13Z | 2013-10-03T00:31:53Z | 2013-08-13T22:19:43Z | CONTRIBUTOR | null | from stack: http://stackoverflow.com/questions/17491446/wrong-result-from-timedelta-operation/17492653#17492653
```
In [101]: rng = pd.date_range('2013', '2014')
In [102]: rng
Out[102]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-07-05 00:00:00, ..., 2014-07-05 00:00:00]
Length: 366, Freq: D, Timezone: None
In [103]: s = pd.Series(rng)
```
These work
```
In [105]: rng - pd.offsets.Hour(1)
Out[105]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-07-04 23:00:00, ..., 2014-07-04 23:00:00]
Length: 366, Freq: D, Timezone: None
In [106]: s - np.timedelta64(100000000)
```
but the other way around doesn't.
```
In [107]: rng - np.timedelta64(100000000)
NotImplementedError:
In [108]: s - pd.offsets.Hour(1)
ValueError: cannot operate on a series with out a rhs of a series/ndarray of type datetime64[ns] or a timedelta
```
cc #3009.
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} | 3 | 2013-07-05T16:02:40Z | 2013-08-13T22:19:43Z | 2013-08-13T22:19:43Z | CONTRIBUTOR | null | Buggy timedelta arithmetic with `np.timedelta64` in np 1.7.1
```
In [41]: s = Series([Timestamp('20130301'),Timestamp('20130228 23:00:00'),Timestamp('20130228 22:00:00'),Timestamp('20130228 21:00:00')])
In [42]: s
Out[42]:
0 2013-03-01 00:00:00
1 2013-02-28 23:00:00
2 2013-02-28 22:00:00
3 2013-02-28 21:00:00
dtype: datetime64[ns]
In [43]: s + timedelta(hours=1)
Out[43]:
0 2013-03-01 01:00:00
1 2013-03-01 00:00:00
2 2013-02-28 23:00:00
3 2013-02-28 22:00:00
dtype: datetime64[ns]
```
Should be the same as above
```
In [45]: s + np.timedelta64(1,'h')
Out[45]:
0 2013-03-01 00:00:00.000000001
1 2013-02-28 23:00:00.000000001
2 2013-02-28 22:00:00.000000001
3 2013-02-28 21:00:00.000000001
dtype: datetime64[ns]
```
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} | 136 | 2013-07-05T18:00:00Z | 2014-06-17T19:42:26Z | 2013-10-09T00:28:28Z | NONE | null | This module adds Google BigQuery functionality to the pandas project. The work is based on the existing sql.py and ga.py, providing methods to query a BigQuery database and read the results into a pandas DataFrame.
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} | 64 | 2013-07-05T22:53:17Z | 2016-10-12T23:03:58Z | 2013-07-08T04:15:07Z | CONTRIBUTOR | null | numpy 1.6.1 supposed to be installed here ?
https://travis-ci.org/pydata/pandas/jobs/8782276
statsmodels not installing
https://travis-ci.org/pydata/pandas/jobs/8782274
#4128
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```
In [1]: df = pd.DataFrame({'a': {0: 1.0676829999999999, 1: -1.3214049999999999, 2: -0.80733299999999997}, 'b': {0: -1.110463, 1: 0.36891499999999999, 2: 0.082979999999999998}, 'c': {0: 0.20866999999999999, 1: -1.055342, 2: -0.87336100000000005}})
In [2]: df.columns.name = 'foo'
In [3]: df
Out[3]:
foo a b c
0 1.067683 -1.110463 0.208670
1 -1.321405 0.368915 -1.055342
2 -0.807333 0.082980 -0.873361
In [4]: pd.melt(df) # before this would have columns ['variable', 'value']
Out[4]:
foo value
0 a 1.067683
1 a -1.321405
2 a -0.807333
3 b -1.110463
4 b 0.368915
5 b 0.082980
6 c 0.208670
7 c -1.055342
8 c -0.873361
Also, cleaned up all the tests (and added some more) in test_reshape.
```
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} | 7 | 2013-07-06T11:11:41Z | 2013-07-06T13:21:40Z | 2013-07-06T13:21:40Z | CONTRIBUTOR | null | Suppose I want to acces a column in df2 (perhaps there is a near way, but I also expect these to work):
```
In [11]: df
Out[11]:
h1 main h3 sub h5
0 a A 1 A1 1
1 b B 2 B1 2
2 c B 3 A1 3
3 d A 4 B2 4
4 e A 5 B2 5
5 f B 6 A2 6
In [12]: df2 = df.set_index(['main', 'sub']).T.sort_index(1)
In [13]: df2
Out[13]:
main A B
sub A1 B2 B2 A1 A2 B1
h1 a d e c f b
h3 1 4 5 3 6 2
h5 1 4 5 3 6 2
```
I want to access the column `('A', 'A1')`:
```
In [14]: df2.iloc[:, 0] # cheating with iloc
In [15]: df2.T.loc[('A', 'A1'), :].iloc[0] # hacky!
In [16]: df2.iloc[:, df2.columns.get_loc(('A', 'A1')).start] # very hacky!
In [17]: df2[df2.columns[:1]] # returns DataFrame
```
I had assumed/hoped this would work:
```
In [18]: df2[('A', 'A1')]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-c307c7bb3bb8> in <module>()
----> 1 df2[('A', 'A1')]
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in __getitem__(self, key)
1997 return self._getitem_frame(key)
1998 elif isinstance(self.columns, MultiIndex):
-> 1999 return self._getitem_multilevel(key)
2000 else:
2001 # get column
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in _getitem_multilevel(self, key)
2036 if isinstance(loc, (slice, np.ndarray)):
2037 new_columns = self.columns[loc]
-> 2038 result_columns = _maybe_droplevels(new_columns, key)
2039 if self._is_mixed_type:
2040 result = self.reindex(columns=new_columns)
/Users/234BroadWalk/pandas/pandas/core/indexing.pyc in _maybe_droplevels(index, key)
1103 if isinstance(key, tuple):
1104 for _ in key:
-> 1105 index = index.droplevel(0)
1106 else:
1107 index = index.droplevel(0)
AttributeError: 'Index' object has no attribute 'droplevel'
In [19]: df2[['A', 'A1']] # interestingly, slightly different error here
KeyError: "['A1'] not in index"
```
Also this way is buggy (loses the index)... which is _weird_, separated this part of the issue as #4146:
```
In [21]: df2['A']['A1'] # in master but not in 0.11.0
Out[21]:
0
0 a
1 1
2 1
```
_pop uses this in it's implementation, so atm it's not possible to pop a MultiIndex._
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} | 0 | 2013-07-06T11:39:54Z | 2013-07-06T13:28:09Z | 2013-07-06T13:27:19Z | CONTRIBUTOR | null | This appears to be a regression since 0.11 in handling duplicates in MultiIndex columns:
```
In [11]: df
Out[11]:
h1 main h3 sub h5
0 a A 1 A1 1
1 b B 2 B1 2
2 c B 3 A1 3
3 d A 4 B2 4
4 e A 5 B2 5
5 f B 6 A2 6
In [12]: df2 = df.set_index(['main', 'sub']).T.sort_index(1)
In [13]: df2
Out[13]:
main A B
sub A1 B2 B2 A1 A2 B1
h1 a d e c f b
h3 1 4 5 3 6 2
h5 1 4 5 3 6 2
```
If we grab out successively we get an unexpected result for the non-duplicate:
```
In [14]: df2['A']
Out[14]:
sub A1 B2 B2
h1 a d e
h3 1 4 5
h5 1 4 5
In [15]: df2['A']['B2']
Out[15]:
sub B2 B2
h1 d e
h3 4 5
h5 4 5
In [16]: df2['A']['A1'] # this worked in 0.11
Out[16]:
0
0 a
1 1
2 1
```
```
In [21]: df2['A']['A1'] # pandas 0.11
Out[21]:
h1 a
h3 1
h5 1
Name: A1, dtype: object
```
_FWIW never like how this can return different a type..._
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} | 26 | 2013-07-06T12:37:58Z | 2020-09-22T00:04:40Z | null | CONTRIBUTOR | null | related #4279
related #5967
Min doesn't seem to work as expected with NaNs and non-numeric data:
```
In [1]: s = pd.Series(['alpha', np.nan, 'charlie', 'delta'])
In [2]: s
Out[2]:
0 alpha
1 NaN
2 charlie
3 delta
dtype: object
In [3]: s.min() # by default docstring suggests this should skipnan
Out[3]: inf
```
The hack/workaround is to exclude them, perhaps we should special case this in the code:
```
In [4]: s[s.notnull()].min()
Out[4]: 'alpha'
```
_From discussion on [this pandas SO question](http://stackoverflow.com/a/17497460/1240268). and I also posted a corresponding numpy issue:
https://github.com/numpy/numpy/issues/3508_.
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} | 3 | 2013-07-06T13:03:12Z | 2014-06-15T09:47:51Z | 2013-07-06T13:21:40Z | CONTRIBUTOR | null | closes #4145, #4146
```
In [1]: data = """h1 main h3 sub h5
...: 0 a A 1 A1 1
...: 1 b B 2 B1 2
...: 2 c B 3 A1 3
...: 3 d A 4 B2 4
...: 4 e A 5 B2 5
...: 5 f B 6 A2 6"""
In [2]: df = pd.read_csv(StringIO(data),sep='\s+',index_col=0)
In [3]: df2 = df.set_index(['main', 'sub']).T.sort_index(1)
In [4]: df2.loc[:,('A','A1')]
Out[4]:
main A
sub A1
h1 a
h3 1
h5 1
In [5]: df2[('A','A1')]
Out[5]:
main A
sub A1
h1 a
h3 1
h5 1
In [6]: df2['A']['A1']
Out[6]:
A1
h1 a
h3 1
h5 1
```
This should NOT work, as this is tryingo select 2 different columns ('A' is ok, but 'A1' fails which is correct)
```
In [7]: df2[['A','A1']]
KeyError: "['A1'] not in index"
```
This is ok though
```
In [8]: df2[[('A','A1')]]
Out[8]:
main A
sub A1
h1 a
h3 1
h5 1
```
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} | 62 | 2013-07-06T17:08:19Z | 2014-06-12T13:34:21Z | 2013-07-12T13:16:57Z | CONTRIBUTOR | null | No idea if there's actually a market for this, but anyhow:
```
In [1]: df = pd.DataFrame([[ 1.067683, -1.110463, 0.20867 ], [-1.321405, 0.368915, -1.055342], [-0.807333, 0.08298 , -0.873361]])
In [2]: df.columns = [list('ABC'), list('abc')]
In [3]: df.columns.names = ['CAP', 'low']
In [4]: df
Out[4]:
CAP A B C
low a b c
0 1.067683 -1.110463 0.208670
1 -1.321405 0.368915 -1.055342
2 -0.807333 0.082980 -0.873361
In [5]: pd.melt(df, col_level=0)
Out[5]:
CAP value
0 A 1.067683
1 A -1.321405
2 A -0.807333
3 B -1.110463
4 B 0.368915
5 B 0.082980
6 C 0.208670
7 C -1.055342
8 C -0.873361
In [6]: pd.melt(df,)
Out[6]:
CAP low value
0 A a 1.067683
1 A a -1.321405
2 A a -0.807333
3 B b -1.110463
4 B b 0.368915
5 B b 0.082980
6 C c 0.208670
7 C c -1.055342
8 C c -0.873361
In [7]: df.columns.names = [None, None]
In [8]: pd.melt(df,)
Out[8]:
variable_0 variable_1 value
0 A a 1.067683
1 A a -1.321405
2 A a -0.807333
3 B b -1.110463
4 B b 0.368915
5 B b 0.082980
6 C c 0.208670
7 C c -1.055342
8 C c -0.873361
```
Also includes fix for `get_level_values` and name attribute.
cc #4144
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} | [] | closed | false | null | [] | null | 1 | 2013-07-07T01:42:44Z | 2013-07-07T13:41:34Z | 2013-07-07T13:41:34Z | CONTRIBUTOR | null | additional commits after @cpcloud eval branch
add some support to replace Term expressions in HDFStore with a direct expression parsing
e.g.
`store.select('columns=A & index>=df.index[0:4]')`
in theory should work
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} | 3 | 2013-07-07T04:07:15Z | 2013-07-09T16:59:56Z | 2013-07-09T13:10:36Z | CONTRIBUTOR | null | ``` python
import datetime as dt
import pandas as pd
val = '01-Apr-2011 00:00:01.978'
print 'pandas version:',pd.__version__
print 'Value to parse:',val
format = '%d-%b-%Y %H:%M:%S.%f'
print 'datetime.strptime :',dt.datetime.strptime(val, format)
print 'to_datetime, w/out format:',pd.to_datetime(val)
print 'to_datetime, w/ format :', pd.to_datetime(val, format=format)
pandas version: 0.12.0.dev-1101391
Value to parse: 01-Apr-2011 00:00:01.978
datetime.strptime : 2011-04-01 00:00:01.978000
to_datetime, w/out format: 2011-04-01 00:00:01.978000
to_datetime, w/ format : 2011-03-31 23:24:13.516352
```
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} | 13 | 2013-07-07T04:38:21Z | 2014-07-16T08:17:58Z | 2013-07-08T04:15:07Z | MEMBER | null | closes #4142.
should be able to finish up tomorrow, just need to put things on the server and
test
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} | 1 | 2013-07-07T04:40:23Z | 2014-07-16T08:18:02Z | 2013-07-10T13:09:33Z | CONTRIBUTOR | null | I simply updated the docs to reflect that `xlrd` handles reading of both `.xls` and `.xlsx` files, and I updated docstrings affected functions by #4131 so that their docstrings stand on their own.
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} | 84 | 2013-07-07T22:28:00Z | 2014-06-22T09:43:11Z | 2013-07-08T18:20:41Z | CONTRIBUTOR | null | extension of @cpcloud #4037
allows natural syntax for queries, with in-line variables allowed
some examples
`"ts>=Timestamp('2012-02-01') & users=['a','b','c']"`
`['A > 0']`
```
dates=pd.date_range('20130101',periods=5)
'index>dates'
```
Todos:
- [ ] update docs / need new examples
- [ ] API changes (disallow dict, and separated expressions)
- [ ] tests for `|` and `~` operators, and invalid filter expressions
- [ ] code clean up, maybe create new `Expr` base class to opt in/out of allowed operations
(e.g. `Numexpr` doesn't want to allow certain operations, but `PyTables` needs them,
so maybe define in the base class for simplicity, and just not allow them (checked in `visit`),
which right now doesn't allow a `generic_visitor`)
- [ ] can `ops.Value` be equiv of `ops.Constant`?
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} | [] | closed | false | null | [] | null | 2 | 2013-07-08T03:13:07Z | 2013-07-08T22:42:54Z | 2013-07-08T22:42:54Z | NONE | null | Hello,
I am wondering if the following is considered to be a bug.
I have two Series a and b. They are the same.
However, after merging with a third Series c, the equality no longer hold.
```
a = {"open": 1, "high": 2, "low": 3, "close": 4, "UpBand": 4, "MidBand":3, "LowBand":5}
b = {"low": 3, "close": 4, "open": 1, "high": 2, "LowBand": 5, "MidBand":3, "UpBand": 4}
c = {"haha": 5, "hello": 2}
t1 = pd.Series(a)
t3 = pd.Series(c)
t2 = pd.Series(b)
t13 = pd.concat([t1, t3])
LowBand 5
MidBand 3
UpBand 4
close 4
high 2
low 3
open 1
haha 5
hello 2
dtype: int64
t32 = pd.concat([t3, t2])
haha 5
hello 2
LowBand 5
MidBand 3
UpBand 4
close 4
high 2
low 3
open 1
dtype: int64
t1 == t2
LowBand True
MidBand True
UpBand True
close True
high True
low True
open True
dtype: bool
t13 == t32
LowBand True
MidBand False
UpBand False
close False
high False
low False
open False
haha False
hello False
```
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} | 22 | 2013-07-08T08:02:00Z | 2013-07-24T20:05:54Z | 2013-07-24T20:05:54Z | CONTRIBUTOR | null | See here:
http://pandas.pydata.org/pandas-docs/dev/io.html#clipboard
and scroll to the clipboard section.
Line [1215] with read_clipboard() failed.
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} | 3 | 2013-07-08T09:05:18Z | 2013-09-28T19:33:55Z | 2013-09-28T19:33:55Z | CONTRIBUTOR | null | `pd.__version__`
'0.12.0.dev-6a0b9ec'
``` python
exdf = pd.DataFrame([["aoe",12,3.14],["qjk",34,5.67]])
exdf.columns = list('abc')
#exdf = exdf.reindex(columns=reversed(list('abc')))
def a(inp):
return '{:>3s},'.format(inp)
def b(inp):
return '{:2d},'.format(inp)
def c(inp):
return '{:3.2f},'.format(inp)
print repr(exdf.to_string(formatters={'a':a,'b':b,'c':c},
index=False,header=False))
```
' aoe, 12, 3.14,\n qjk, 34, 5.67,'
^^extra space!
Note, when you reindex the columns to be the other way around by removing the # in front of the line `exdf = exdf.reindex ...` then you get:
'3.14, 12, aoe,\n5.67, 34, qjk,'
^^no extra space!
where one can easily see, that usually, as it is correct, the first element in the line does NOT get a space. Therefore I consider this a formatting bug for (string) objects.
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} | 7 | 2013-07-08T11:56:12Z | 2013-07-11T11:13:48Z | 2013-07-10T18:59:26Z | MEMBER | null | I noticed that the width of the docs (the text area) is set to a certain maximum in the dev/0.12 version, which I think is a good idea! (certain for wide screens). However, I think it is a little bit too small, as illustrated with the figure below:
You can see that I have to use the horizontal scrollbar to see the whole code snippet (while it is well within the 80 char limit).
This is partly due to the too small width of the text area, but also partly due to a wrong use of indentation in bulleted lists. Lists don't need any indentation, see http://sphinx-doc.org/rest.html. But almost everywhere (at least in the whatsnew of v0.12.0.txt) indentation is used, with the consequence that the lists in the html are over-indented as you can see in the picture.
Possible actions to take:
- Increase the maximum width a little bit? I think code snippets should certainly be shown in full width without scrollbar.
An example of code snippet (without being in a bulleted list) which needs a scrollbar (on my screen): http://pandas.pydata.org/pandas-docs/dev/whatsnew.html#dtypes
- Remove the unnecessary indentation in lists, maybe to start with in the whatsnew v0.12.0.txt (and it can be discussed if it also has to be adapted in older files). If you think this is OK, I can submit a PR for this.
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} | 25 | 2013-07-08T13:52:55Z | 2021-10-29T01:58:26Z | null | NONE | null | xref #14139 for empty MI
Hi everybody,
in the current version renaming of MultiIndex DataFrames does not work. Lets take the following example:
```
import datetime as DT
import pandas as pd
df = pd.DataFrame({
'Branch' : 'A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Joe Mark Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,9,1,13,0),
DT.datetime(2013,9,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,3,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]})
```
and the following query:
```
test_df = df[df['Buyer'].isin(['Carl', 'Mark'])].set_index('Buyer', append=True)[['Date']].unstack(['Buyer'])
```
Now, the following renaming does not work
```
test_df.rename(columns={('Date', 'Carl'): 'Carl'}, inplace=True)
```
Thanks in advance
Andy
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} | 3 | 2013-07-08T17:10:59Z | 2014-02-14T03:24:56Z | 2014-02-14T03:24:56Z | NONE | null | related SO question
http://stackoverflow.com/questions/19636370/dataframe-groupbytimegrouper-d-invalid-length-for-values-or-for-binner/19636874?noredirect=1#comment29155715_19636874
Hi everybody,
today I spend the whole afternoon figuring out that TimeGrouper only works properly when using on a sorted(!) DateTimeIndex.
If the DateTimeIndex is not sorted, TimeGrouper throws no error (!) but produces corrupt results.
I would suggest either to do a sort() call within TimeGrouper or modify it to throw an error message. The fact that it silently produces corrupt results when the DateTimeIndex is not sorted is really disturbing.
Sorry, that I cannot add an example for the bug. All simple examples I created worked fine, it seems that this only leads to corrupt results with DataFrames of size 700 and above.
Best regards
Andy
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} | 30 | 2013-07-08T18:16:45Z | 2013-09-17T20:53:44Z | 2013-07-08T20:11:27Z | MEMBER | null | closes #3393.
cc @jreback
# `eval` Roadmap
## PyTables
allows natural syntax for queries, with in-line variables allowed
some examples
`"ts>=Timestamp('2012-02-01') & users=['a','b','c']"`
`['A > 0']`
```
dates=pd.date_range('20130101',periods=5)
'index>dates'
```
Todos:
- [ ] update docs / need new examples
- [ ] API changes (disallow dict, show deprecations for separated expressions)
- [ ] tests for `|` and `~` operators, and invalid filter expressions
- [ ] code clean up, maybe create new `Expr` base class to opt in/out of allowed operations
(e.g. `Numexpr` doesn't want to allow certain operations, but `PyTables` needs them,
so maybe define in the base class for simplicity, and just not allow them (checked in `visit`),
which right now doesn't allow a `generic_visitor`)
- [ ] can `ops.Value` be equiv of `ops.Constant`?
## Documentation
- [ ] enhancing performance section
- [x] `eval` docstring
- [ ] Warn users that this is _not_ a magic bullet
- E.g., `x = 3; y = 2; pd.eval('x // y', engine='python')` is 1000 times slower than the same operation in actual Python.
## Engines
- [x] python engine for testing (basically a restricted eval, should make sure nothing dangerous a la `eval('os.system("sudo rm -rf /")')`) is possible here
- [x] numexpr engine
Should python be the default engine for small frames?
## Functions
- [x] toplevel `isexpr`
- [x] move `eval` to top-level pandas
## Error Handling
- [x] syntax error handling
- [x] name error handling
## Alignment
- Algorithm
1. flatten the tree of terms
2. resolve the variables, special casing only numpy arrays/scalars/single unary operations (probably room for more abstraction here)
3. align by joining on the indices using the current broadcasting behavior
4. update the original namespace with the newly aligned version of the underlying numpy array
5. reconstruct the final object with the aforementioned joined indices and type
- [ ] punt to `'python'` engine when a `Series` and `DataFrame` both have `DatetimeIndexes` (I would rather not do this and just enforce the to-be-deprecated index-aligning-if-both-indices-are-datetimeindex behavior)
- [ ] `PeriodIndex`es don't work (well, they won't pass my tests) because of a stack overflow bug when joining a `DatetimeIndex` and a `PeriodIndex` (that bug is slated for 0.13, but a design decision needs to be made)
- [ ] alignment for `NDFrame` (is this really necessary?)
- [x] unary alignment
- [x] binary alignment
## Scope
- [x] deal with global scoping issues (not sure if this is tested well enough...which means probably not)
- [x] allow `Expr` objects to be passed to `eval` (`Expr` will pull in the local and global variables from the calling stack frame)
## Miscellaneous
- [ ] replace current `evaluate` function
- [ ] `Expr` in `NDFrame.__getitem__` cc @jreback
- [x] reconstruct the final object to return the expected output type, e.g., `df + df2` should return a `DataFrame` if `df` and `df2` are both `DataFrame` objects.
- [x] change `Expression` to `Expr`
- [x] add `truediv` keyword to `Expr`, default to `True`
## Operators
- [x] `%`
- [x] `**`
- [x] unary ops don't play well when they are operands of binary ops
## Tests
- [ ] `DatetimeIndex` and `PeriodIndex` objects are untested because of a few join and alignment issues.
- [ ] rewrite a few tests in the python evaluator to make sure the `//` operator works as it should (it works, but there may be edge cases that I haven't though of yet)
- [ ] `//` `numexpr` only supports scalar evaluation here: variables are not allowed so this will always eval in python space (half-assed it a bit in the tests here need to fix that)
- [x] truediv keyword tests
- [x] add tests for nans
- [x] fix `**` associativity issue in testing
## Near Future
- allow `and`, `or` and `not` aliases for the operators `&`, `|` and `~`, respectively
- attribute accessing e.g., `df.A` syntax
- reductions (`sum` and `prod`)
- math ops (sin, cos, tan, sqrt, etc.)
## Far Future
- non-python indexing (or just punt to python)
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} | 176 | 2013-07-08T19:54:20Z | 2014-07-23T22:53:35Z | 2014-02-08T03:26:32Z | CONTRIBUTOR | null | **UPDATE**: this is continued in #6292 after the refactor based on sqlalchemy.
---
This is a central issue to track sql support (migrated from the mysql support ticket - #2482 and #1662).
- [x] sqlite
- [x] mysql (#2482)
- [ ] oracle (not tested, partially working)
- [ ] postgres, (partial via #4574) (not tested, some parts possibly working, [SO user suggests it "works"],(http://stackoverflow.com/questions/17156084/unpacking-a-sql-select-into-a-pandas-dataframe/17156233#17156233))
- [ ] ODBC
- _any others?_
I think we are agreed we will be moving to use SQLAlchemy, in the hope of comprehensive cross-platform support:
- [x] SQLAlchemy (#2717, #191)
Todo:
- [x] travis should not skip tests (e.g. mysql #4177)
- [ ] add `flavor` argument to `read_sql`
- [ ] handling no row returns (#3745)
- [ ] Specify chunksize (and iterator) for `read_frame` (#2908)
- [ ] read and write the index (and multi-index)
- [ ] mangle_dup_columns for read_frame (#2738)
- [x] bug in write_frame replace (#4110, #2971, PR #4304, #6164)
- [ ] option in `config` to specify default engine/connection for SQL (maybe?)
- [ ] refactor SQLAlchemy/sqliite tests (@hayd was thinking different flavors could inherit tests from a baseclass, so tests are only flavor explicit if obviously flavor explicit...)
Better Dtype coercion:
- [ ] infer dtypes more carefully (discussed in #2482)
- [ ] NaN/NaT conversions (#2754, #4199, PR #4200)
- [ ] `datetime.date` conversions https://groups.google.com/forum/#!topic/pydata/KEBKxj4cWNI
- [ ] support encoding #5008
Peformance
- [ ] Vbenches! (for various flavors even?)
cc @garaud @mangecoeur @danielballan @changhiskhan
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} | 477 | 2013-07-08T20:32:22Z | 2014-07-14T06:34:32Z | 2013-09-16T18:59:14Z | MEMBER | null | closes #3393
closes #2560
cc @jreback
# `eval` Roadmap
## PyTables
allows natural syntax for queries, with in-line variables allowed
some examples
`"ts>=Timestamp('2012-02-01') & users=['a','b','c']"`
`['A > 0']`
`'(index>df.index[3] & index<=df.index[6]) | string="bar"'`
```
dates=pd.date_range('20130101',periods=5)
'index>dates'
```
Todos:
- [x] Release Notes
- [x] v0.13.txt examples (and link to docs)
- [x] update docs / need new examples
- [x] API changes (disallow dict, show deprecations for separated expressions)
- [x] tests for `|` and `~` operators
- [x] tests for invalid filter expressions
- [x] code clean up, maybe create new `Expr` base class to opt in/out of allowed operations
(e.g. `Numexpr` doesn't want to allow certain operations, but `PyTables` needs them,
so maybe define in the base class for simplicity, and just not allow them (checked in `visit`),
which right now doesn't allow a `generic_visitor`)
- [x] can `ops.Value` be equiv of `ops.Constant`?
## `query` method
- [x] a few more doc examples
- [x] add a section in `query` docstring to show use of `__getitem__` and then add to `indexing.rst`
- [x] `query` docstring
## `DataFrame.eval` method
- [x] implement query in terms of this
- [x] tests
## Documentation
- [x] **RELEASE NOTES**
- [x] v0.13.txt examples & link to docs
- [x] document `in` and `not in`
- [x] enhancing performance section
- [x] `eval` docstring
- [x] Warn users that this is _not_ a magic bullet
- E.g., `x = 3; y = 2; pd.eval('x // y', engine='python')` is many times slower than the same operation in actual Python.
## Engines
- [x] `python` engine for testing (basically a restricted eval, should make sure nothing dangerous a la `eval('os.system("sudo rm -rf /")')`) is possible here
- [x] `numexpr` engine
## Parsers
- [x] `python` parser is currrently not failing on complex bool ops involving subscripting like it should (potentially could `eval` the compiled AST instead of dispatching to function calls)
- [x] preparse should handle subscripting
- [x] `pandas` parser should handle subscripting, i.e., `df.query(...) == pd.eval('df[...]')`
- [x] `pandas` parser
- [x] `python` parser
- [x] `pytables` engine and parser (internal)
Should python be the default engine for small frames?
## Functions
- [x] toplevel `isexpr`
- [x] move `eval` to top-level pandas
## Error Handling
- [x] syntax error handling
- [x] name error handling
- [x] ugly stack overflow when an expression is not in frame...issue with variable resolution in `expr.py`. should fail fast.
## Alignment
- Algorithm
1. flatten the tree of terms
2. resolve the variables, special casing only numpy arrays/scalars/single unary operations (probably room for more abstraction here)
3. align by joining on the indices using the current broadcasting behavior
4. update the original namespace with the newly aligned version of the underlying numpy array
5. reconstruct the final object with the aforementioned joined indices and type
- [x] unary alignment
- [x] binary alignment
## Scope
- [x] add `@a` syntax for referencing local variables with the same name as a column
- [x] deal with global scoping issues (not sure if this is tested well enough...which means probably not)
- [x] allow `Expr` objects to be passed to `eval` (`Expr` will pull in the local and global variables from the calling stack frame)
## Miscellaneous
- [x] support for `MultiIndex` queries
- [x] **remove instance testing methods once series ndframe is merged**
- [x] `str` ops (implemented using `isin`)
- [x] basic `Timestamp` and `datetime` functionality
- [x] vbench!
- [x] single variable or constant evaluation e.g., `pd.eval('1')` or `pd.eval('df')`
- [x] reconstruct the final object to return the expected output type, e.g., `df + df2` should return a `DataFrame` if `df` and `df2` are both `DataFrame` objects.
- [x] change `Expression` to `Expr`
- [x] add `truediv` keyword to `Expr`, default to `True`
- [x] allow `and`, `or` and `not` aliases for the operators `&`, `|` and `~`, respectively
- [x] chained comparisons
- [x] attribute access syntax e.g., `df.A`
## Operators
- [x] `%`
- [x] `**`
- [x] unary ops don't play well when they are operands of binary ops
## Tests
- [ ] `PeriodIndex` objects are untested at large (a few scattered tests for basic things)
- [x] add tests for strange column names e.g., `df['@awesome_domain']`, `df['df["$10"]']`
- [x] Python 3 handles complex scalars differently, so test that
- [x] **revert equality testing in `align.py` once #4379 is merged**
- [x] boolean ops fail with a `DataFrame` with a `PeriodIndex` in the columns
- [x] test alignment of `DatetimeIndex` objects
- [x] rewrite a few tests in the python evaluator to make sure the `//` operator works as it should (it works, but there may be edge cases that I haven't though of yet)
- [x] `//` `numexpr` only supports scalar evaluation here: variables are not allowed so this will always eval in python space (half-assed it a bit in the tests here need to fix that)
- [x] truediv keyword tests
- [x] add tests for nans
- [x] fix `**` associativity issue in testing
## Syntax (Easier)
- [x] handle `in`, `not in` ( ~~was thinking~~ implemented as `a in b` -> `b.isin(a)` and `a not in b` -> `not a in b` -> `~b.isin(a)`)
## Syntax (More difficult)
- reductions (`sum` and `prod`)
- math ops (sin, cos, tan, sqrt, etc.), i.e., whatever `numexpr` supports
- ~~function calls~~ (we're not building a Python interpreter here :wink:)
## Optimizations
There's a quite a bit of recursion going on here, where maybe something stack-based might be more efficient. For example, an expression like `df['f in g and a < b < (c ** 2 + b ** 2 - a ** 3) and d in b not in e']` will make 3 recursive calls to eval (not including the top level call). One for the first `in` expression and two for the `in`/`not in` chain. A stack-based implementation of the parser (instead of recursively visiting nodes) could possible shave off a few milliseconds. Not sure if this is worth it though.
## Others
- ~~alignment for `NDFrame`~~ #4336
- ~~replace current `evaluate` function~~
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https://api.github.com/repos/pandas-dev/pandas/issues/4165 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4165/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4165/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4165/events | https://github.com/pandas-dev/pandas/pull/4165 | 16,493,360 | MDExOlB1bGxSZXF1ZXN0Njc4OTYzNA== | 4,165 | DOC: increased width of text area (#4159) | {
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} | 25 | 2013-07-08T20:51:57Z | 2014-06-17T19:31:27Z | 2013-07-10T18:55:26Z | MEMBER | null | See #4159
This adds a minimum and maximum width of the text area (instead of one fixed value of, at the moment 600px), and I also increased the maximum value (from 600 to 840).
I took the values of the standard sphinx docs which are 780-1080 (minus 240 for the sidebar, in the standard sphinx docs the width is defined for the whole body, here only for the main text area)
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https://api.github.com/repos/pandas-dev/pandas/issues/4166 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4166/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4166/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4166/events | https://github.com/pandas-dev/pandas/pull/4166 | 16,496,694 | MDExOlB1bGxSZXF1ZXN0Njc5MTQ1Nw== | 4,166 | BUG: wrong parsing of microseconds with format arg (#4152) | {
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} | [] | closed | false | null | [] | null | 16 | 2013-07-08T21:59:27Z | 2014-07-16T08:18:25Z | 2013-07-09T13:10:36Z | MEMBER | null | Closes #4152.
`fraction` was already in microseconds, so no need to multiply it with 1000000, which caused faulty parsing of dates with microseconds.
With this commit I get:
```
pandas version: 0.12.0.dev
Value to parse: 01-Apr-2011 00:00:01.978
datetime.strptime : 2011-04-01 00:00:01.978000
to_datetime, w/out format: 2011-04-01 00:00:01.978000
to_datetime, w/ format : 2011-04-01 00:00:01.978000
```
for
```
import datetime as dt
import pandas as pd
val = '01-Apr-2011 00:00:01.978'
print 'pandas version:',pd.__version__
print 'Value to parse:',val
format = '%d-%b-%Y %H:%M:%S.%f'
print 'datetime.strptime :',dt.datetime.strptime(val, format)
print 'to_datetime, w/out format:',pd.to_datetime(val)
print 'to_datetime, w/ format :', pd.to_datetime(val, format=format)
```
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} | 5 | 2013-07-09T12:22:15Z | 2017-07-09T02:43:10Z | 2017-07-09T02:43:06Z | CONTRIBUTOR | null | When hitting a bad url it shows content from [main page](http://pandas.pydata.org/) but loses css.
e.g. http://pandas.pydata.org/pandas-docs/dev/foo.html
I think these pages link to the old (non-existent) `"_static/pydata.css"` rather than `"_static/nature.css"` (does this come from [here](https://github.com/pydata/pandas-website/blob/4431565525f6002710f68817ac46a910adf67227/conf.py#L109) or [here](https://github.com/pydata/pandas-website/blob/4431565525f6002710f68817ac46a910adf67227/_themes/pydata/theme.conf#L3)?). :S
Also, not sure how/where this behaviour is configured (or if it can be), but could be nice if it could show a alert message to say was a bad url, but perhaps that's not possible.
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} | 18 | 2013-07-09T13:33:48Z | 2016-10-12T23:03:59Z | 2013-08-07T19:17:18Z | CONTRIBUTOR | null | cumsum seems to require skipna=False otherwise it sulks here. (Not investigate which others are also affected, cumprod is though).
```
In [10]: b = pd.Series([False, False, False, True, True, False, False])
In [11]: b
Out[11]:
0 False
1 False
2 False
3 True
4 True
5 False
6 False
dtype: bool
In [12]: b.cumsum()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-f3f684a93525> in <module>()
----> 1 b.cumsum()
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/series.pyc in cumsum(self, axis, dtype, out, skipna)
1626
1627 if do_mask:
-> 1628 np.putmask(result, mask, pa.NA)
1629
1630 return Series(result, index=self.index)
ValueError: cannot convert float NaN to integer
In [13]: b.cumsum(skipna=False)
Out[13]:
0 0
1 0
2 0
3 1
4 2
5 2
6 2
dtype: int64
```
If it has nans or you int or object it works as expected:
```
In [21]: b.astype(int).cumsum()
In [22]: b.astype(object).cumsum() # False at the beginning is expected
In [23]: b.astype(int).astype(object).cumsum()
```
Also, if you try and inset an nan it doesn't work nor raise (!):
```
In [31]: b.loc[0] = np.nan
In [32]: b
Out[32]:
0 True
1 False
2 False
3 True
4 True
5 False
6 False
dtype: bool
```
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} | 11 | 2013-07-09T14:24:28Z | 2014-01-27T19:36:14Z | 2014-01-27T19:36:14Z | CONTRIBUTOR | null | This user on SO was trying to read an xlsm file (apparently it doesn't work):
http://stackoverflow.com/questions/17548669/reading-excel-to-a-python-data-frame-starting-from-row-5-and-including-headers
apparently it's possible with openpyxl.
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} | 1 | 2013-07-09T14:33:05Z | 2013-07-26T13:40:19Z | 2013-07-26T13:40:19Z | MEMBER | null | There's a duplicate plot created when calling `Series.hist` as well as a duplicate plot when calling `groupby.plot` that I think would be fixed by resolving this issue.
#4113 #413 #2168
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} | 2 | 2013-07-09T14:46:11Z | 2013-07-09T16:24:44Z | 2013-07-09T16:24:44Z | MEMBER | null | https://travis-ci.org/pydata/pandas/jobs/8889101
the test errors because two different reads of the same data yield different values...i'm not sure how to test for this since no matter what they should be the same....? thoughts?
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Doesn't address the issue, but warns users about it in the docstring (it's already got a warning in the docs).
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} | 2 | 2013-07-09T15:25:25Z | 2014-07-16T08:18:32Z | 2013-07-09T16:24:44Z | MEMBER | null | closes #4174.
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"description": "__repr__ ... | closed | false | null | [] | null | 1 | 2013-07-09T16:47:44Z | 2013-07-09T17:04:23Z | 2013-07-09T17:02:56Z | MEMBER | null | ```
FAIL: pandas.tseries.tests.test_timeseries:TestTimeSeries.test_to_datetime_format_microsecond
gvim -f +836 pandas/tseries/tests/test_timeseries.py # test_to_datetime_format_microsecond
self.assertEqual(result, exp)
AssertionError: Timestamp('2011-03-31 23:24:13.516352', tz=None) != datetime.datetime(2011, 4, 1, 0, 0, 1, 978000)
1 test, 1 failure, 0 errors in 0.0s
```
(i changed to `assertEqual`)
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} | 14 | 2013-07-09T17:24:54Z | 2014-06-14T07:25:56Z | 2013-08-27T12:18:58Z | CONTRIBUTOR | null | As discussed [here](http://stackoverflow.com/questions/17552997/how-to-update-a-subset-of-a-multiindexed-pandas-dataframe), xs usually remove the level which you are accessing. This commit allows you to explicitly say you don't want that (and want to keep the same levels in the result).
```
In [4]: df = DataFrame({'day': {0: 'sat', 1: 'sun'},
'flavour': {0: 'strawberry', 1: 'strawberry'},
'sales': {0: 10, 1: 12},
'year': {0: 2008, 1: 2008}}).set_index(['year','flavour','day'])
In [5]: df
Out[5]:
sales
year flavour day
2008 strawberry sat 10
sun 12
In [6]: df.xs('sat', level='day')
Out[6]:
sales
year flavour
2008 strawberry 10
In [7]: df.xs('sat', level='day', drop_level=False)
Out[7]:
sales
year flavour day
2008 strawberry sat 10
In [8]: df.xs([2008, 'sat'], level=['year', 'day'], drop_level=False)
Out[8]:
sales
year flavour day
2008 strawberry sat 10
```
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} | 8 | 2013-07-09T17:51:28Z | 2018-07-06T20:21:00Z | 2017-07-11T09:59:31Z | CONTRIBUTOR | null | Should the following work? On revision 07403ca.
```
weights = np.random.randn(4,4)
panel = pd.util.testing.makePanel(nper=2).to_frame()
panel.groupby(level=0).apply(lambda x : np.dot(weights, x))
```
I realize I can do
```
panel.groupby(level=0).apply(lambda x : pd.DataFrame(np.dot(weights, x), index=x.index, columns=x.columns))
```
Would it make sense to just assume that the result should be wrapped like this if it's conformable to the other level of the MultiIndex? Too much of a special case?
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} | 12 | 2013-07-09T20:46:53Z | 2013-07-09T23:59:45Z | 2013-07-09T23:59:45Z | MEMBER | null | i love a good game of whack-a-mole.
https://travis-ci.org/pydata/pandas/jobs/8900734
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} | 13 | 2013-07-09T21:01:52Z | 2015-03-03T01:27:02Z | 2015-03-03T01:27:02Z | CONTRIBUTOR | null | At the moment it's a common source of confusion as to what returns a view vs a copy. The [docs are fairly light on this atm](http://pandas.pydata.org/pandas-docs/dev/indexing.html#indexing-view-versus-copy).
Need to expand this section with lots of example, and update with what now returns a view.
I've said before to @jreback I'd do this but tbh, am not fully confident on the ins and outs so perhaps we can post a few examples here to get started.
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} | 0 | 2013-07-09T23:31:56Z | 2014-07-16T08:18:39Z | 2013-07-09T23:59:45Z | MEMBER | null | closes #4182.
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} | 1 | 2013-07-09T23:41:39Z | 2018-07-06T20:21:00Z | 2017-07-11T09:56:43Z | CONTRIBUTOR | null | see related: https://github.com/pydata/pandas/pull/4184, #4324
```
In [2]: pd.Panel(dict(A = np.nan))
AttributeError: 'float' object has no attribute 'shape'
```
```
In [26]: a = pd.Series(np.random.randn(4))
In [27]: b = pd.Series(np.random.randn(4), index=np.arange(1, 5))
In [28]: pd.Panel({'a': a, 'b': b})
IndexError: tuple index out of range
```
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} | 3 | 2013-07-10T01:36:44Z | 2014-07-16T08:18:41Z | 2013-07-10T18:56:00Z | CONTRIBUTOR | null | As pointed out by @lodagro [here](https://github.com/pydata/pandas/commit/a8d15bd4c6dae2c65ed4aa4b2f76c029b6358113#commitcomment-3521549) and [here](https://github.com/pydata/pandas/commit/a8d15bd4c6dae2c65ed4aa4b2f76c029b6358113#commitcomment-3521581) some of the ujson tests fail on 32-bit due to precision errors.
The changes below amend the tests so they pass.
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} | 9 | 2013-07-10T11:11:43Z | 2013-09-10T19:03:57Z | 2013-07-13T08:21:49Z | CONTRIBUTOR | null | ```
In [3]: m = pd.MultiIndex.from_arrays([[1,2], [3,4]])
In [4]: m
Out[4]:
MultiIndex
[(1, 3), (2, 4)]
In [5]: pd.Series(m)
[1] 786 segmentation fault ipython
pd.Series(m.values) has same fault (list of tuples)
```
Probably this should just be disallowed.
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```
m = pd.MultiIndex.from_arrays([[1,2], [3,4]])
In [11]: pd.DataFrame(m)
Out[11]:
Empty DataFrame
Columns: [0]
Index: []
```
Unfortuantely m.values doesn't work "as expected":
```
In [20]: m.values
Out[20]: array([(1, 3), (2, 4)], dtype=object)
In [21]: pd.DataFrame(m.values)
Out[21]:
0
0 (1, 3)
1 (2, 4)
In [22]: m.shape
Out[22]: (0,)
```
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} | 11 | 2013-07-10T12:53:33Z | 2013-07-12T00:05:12Z | 2013-07-11T13:17:51Z | CONTRIBUTOR | null | Currently in 0.12.
```
In [3]: DataFrame(randn(10,2)).to_hdf('test_storer.h5','df')
In [4]: DataFrame(randn(10,2)).to_hdf('test_table.h5','df',table=True)
```
Selecting from a table is good with or without a where
```
In [6]: pd.read_hdf('test_table.h5','df',where='index>5')
Out[6]:
0 1
6 0.296947 -1.876055
7 -0.200516 -0.641670
8 -0.177828 0.877286
9 0.836903 -1.626247
```
This raises if where is not None, the theory being that passing a non-None where was a mistake.
However, then you have to a-prior know if you are dealing with a `Storer` or a `Table`
```
In [7]: pd.read_hdf('test_storer.h5','df',where='index>5')
TypeError: cannot pass a where specification when reading a Storer
```
make this a warning instead?
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} | 6 | 2013-07-10T13:11:52Z | 2014-07-07T12:44:52Z | 2013-07-13T08:21:49Z | CONTRIBUTOR | null | fixes #4187 (at least the segfault part).
Constructing Series with a MultiIndex raises NotImplementedError, the option (to implement this) is to return `.to_series()` ?
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} | 16 | 2013-07-10T14:24:48Z | 2017-10-10T04:49:21Z | 2017-10-10T04:49:21Z | CONTRIBUTOR | null | just too confusion and not consistent
related: #3418, #3171
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} | 9 | 2013-07-10T14:52:44Z | 2013-07-10T18:52:48Z | 2013-07-10T18:52:48Z | CONTRIBUTOR | null | Take the simple example below:
```
s = pd.Series([1.0, 2.0], dtype=float)
s[[True, False]] = np.array([2.0])
```
The expectation would be that the first item in the series will be set to 2. Unfortunately due to a bug in broadcasting, pandas will set the first item in the series to 2*len(s). The culprit appears to be the below code (series.py:757 - where() method definition):
```
if len(other) == 1:
other = np.array(other[0]*len(ser))
```
`other` in this case is the length 1 array we need to broadcast. I believe the code made the assumption that other will be perhaps a list of lists? As it stands, this code will take the single element out of the array, multiply it by the length of the series and then carry out the update.
I propose that we fix this by using np.repeat which does the correct thing (pending testing).
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} | 9 | 2013-07-10T15:25:04Z | 2019-10-09T04:50:43Z | 2019-10-09T04:50:43Z | NONE | null | I think it would be nice to be able to scroll through a DataFrame by calling whatever scroll function and then holding down the up or down arrow to scroll through the different rows in a series or dataframe and the left and right arrows to scroll across various columns. Currently, it's a little cumbersome to type in different index ranges if you want to quickly look at different parts of a dataframe ala df[0:60], then df[60:120], etc.
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} | 8 | 2013-07-10T19:32:55Z | 2016-10-12T23:03:59Z | 2014-05-14T00:45:22Z | CONTRIBUTOR | null | This of course can be done directly, but adding the keyword `quantiles` that accepts a number or list (to create multiple fields), seems like a nice idea
and maybe deprecate `percentile_width` / replace with `quantiles=[50]`
like this:
http://stackoverflow.com/questions/17578115/pass-percentiles-to-pandas-agg-function
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} | 5 | 2013-07-10T23:22:24Z | 2020-03-31T06:49:24Z | 2020-03-31T03:46:40Z | CONTRIBUTOR | null | related: http://stackoverflow.com/questions/21329425/resampling-a-pandas-dataframe-with-loffset-introduces-an-additional-offset-of-an
Hey pandas team. Sorry to have gone MIA the past week, super busy with work. I promise (and look forward to) contributing more soon. :)
Still, I wanted to note that I came across what I believe to a be a bug in `resample()` when trying to change the interval of the binning with `closed='left'`. I know that there have been a few changes to the `resample()` API since Wes' book, however, I don't believe they changed this functionality, but I have been wrong before :)
Bug can be reproduced using the example from Wes' book, generating 12 mins of data like:
``` python
In [3]: rng = pd.date_range('1/1/2000', periods=12, freq='T')
In [4]: ts = pd.Series(np.arange(12), index=rng)
In [5]: ts
Out[5]:
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
2000-01-01 00:09:00 9
2000-01-01 00:10:00 10
2000-01-01 00:11:00 11
Freq: T, dtype: int64
```
we can do a simple resample to 5 mins like:
``` python
In [6]: ts.resample('5min', how='sum')
Out[6]:
2000-01-01 00:00:00 10
2000-01-01 00:05:00 35
2000-01-01 00:10:00 21
Freq: 5T, dtype: int64
```
For my use, I need this resampling to be 'backwards looking' so that the summations at each resampled timestamp include the previous 4 minutes. Documentation (and Wes' book) suggest this is achieved by binning with `closed='left'`, however, this results in the same output as above:
``` python
In [7]: ts.resample('5min', how='sum', closed='left')
Out[8]:
2000-01-01 00:00:00 10
2000-01-01 00:05:00 35
2000-01-01 00:10:00 21
Freq: 5T, dtype: int64
```
I was looking for the following result (note that the first timestamp is at `00:05:00` and with hanging data dropped):
``` python
2000-01-01 00:05:00 10
2000-01-01 00:10:00 35
Freq: 5T, dtype: int64
```
I am able to generate this by combining `loffset='5min'` and then slicing into the resultant Series to remove the:
``` python
In [10]: ts.resample('5min', how='sum', closed='left', loffset='5min')[:-1]
Out[10]:
2000-01-01 00:05:00 10
2000-01-01 00:10:00 35
Freq: 5T, dtype: int64
```
but this is hardly ideal as it's not known in advance if time series ends with a timestamp that resolves equally to the final timestamp of the resampling procedure!
Apologies if I am missing something—any thoughts, help or guidance is welcomed!
Thanks so much.
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} | 2 | 2013-07-11T00:12:13Z | 2013-11-21T16:14:00Z | 2013-07-12T00:03:55Z | MEMBER | null | is this kind of use cases reflected in our vbenchmarks? suppose @jreback is the one to ask here. more evidence need to rewrite the internals of pandas objects in C to reduce indexing overhead, among other things
http://stackoverflow.com/questions/17538902/is-there-a-way-to-speed-up-this-pandas-function
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- But, nan value can not be treated well by MySQLdb, it will raise an exception:
``` python
OperationalError: (1054, "Unknown column 'nan' in 'field list'")
```
- I've tried to convert all nan value to None, but it turns out to be impossible, fillna function treat param value=None as "the caller don't pass a value" not "the nan value should be None"
- I think add some hacks to convert nan to None in io.sql._write_mysql can fix this.
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} | 21 | 2013-07-11T07:59:57Z | 2014-06-13T17:17:53Z | 2013-09-28T19:45:25Z | NONE | null | For issue #4199
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