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https://api.github.com/repos/pandas-dev/pandas/issues/3101 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3101/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3101/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3101/events | https://github.com/pandas-dev/pandas/pull/3101 | 12,208,965 | MDExOlB1bGxSZXF1ZXN0NDcwNjU4NA== | 3,101 | API experiment: lambda grouper based on sliding window | {
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way of splitting frames into multiple frames based on
predicate functions.
Not optimized, just trying it out.
- The number of args in the predicate function determines the width
of the sliding window
- The df.groupby() `offset` kwd determines window offset
relative to current element.
- The df.groupby() `fill` kwd determines the value provided
to the predicate instead of "phantom" locations when the window
slides past the edge of of array.
The predicate function also gets a `_` variable injected into it's
context, which intially 0, and is updated after each call with the value
just returned. That is, `_` holds the "group label of the previous item.
By updating the value as we go along we can always compute the
label for the "next" group. Somewhat like `reduce`
#3066 was prompted by this [SO question](http://stackoverflow.com/questions/13353233/best-way-to-split-a-dataframe-given-an-edge/15449992#15449992)
Note how concise doing that is, using this idiom (much more complex
splitting is easy as well):
``` python
In [1]: def prnt_dfs(g):
...: for d in zip(*list(g))[-1]:
...: print d
...: print ""
...:
In [23]: ix = ['A','A','A','B','A','A','B','A','A']
...: df = pd.DataFrame(np.random.randn(9),index=ix)
...: prnt_dfs(df.groupby(lambda p,c: _+int(p=='B'),offset=-1))
0
A -0.214422
A 1.373191
A -1.209503
B -0.258765
0
A 1.014395
A -0.381113
B 0.707706
0
A -2.038331
A 1.719545
```
``` python
In [2]: df = pd.DataFrame(range(4),index=['X0','X1','B','X3'])
...:
# start new df if current is B
In [3]: g=(df.groupby(lambda c,n: _+int(c == 'B')))
...: prnt_dfs(g)
...:
0
X0 0
X1 1
0
B 2
X3 3
# start new df if next is B
In [4]: g=(df.groupby(lambda c,n: _+int(n == 'B')))
...: prnt_dfs(g)
...:
0
X0 0
0
X1 1
B 2
X3 3
# start new df if prev was B
In [5]: g=(df.groupby(lambda p,c: _+int(p == 'B'),offset=-1))
...: prnt_dfs(g)
...:
0
X0 0
X1 1
B 2
0
X3 3
# start new df if prevPrev was B
In [25]: g=(df.groupby(lambda pp,p,c: _+int(pp == 'B'),offset=-1))
...: prnt_dfs(g)
0
A 0.174714
A -0.761156
A 0.260196
B 0.381065
A -2.015324
0
A 0.387559
B 0.800947
A -1.007098
0
A -0.217266
In [38]: df = pd.DataFrame(range(10),index=[1,2,5,3,7,8,1,1,3,1])
...: g=(df.groupby(lambda p,c,n,nn: _+int(c+p<=3 or nn >5 ),offset=1,fill=0))
...: prnt_dfs(g)
0
1 0
0
2 1
0
5 2
0
3 3
7 4
8 5
1 6
0
1 7
3 8
1 9
```
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} | 30 | "2013-03-20T01:58:30Z" | "2014-06-15T04:34:20Z" | "2013-04-01T15:23:25Z" | NONE | null | @jreback, does this look ok? travis is still running.
#2786
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} | 5 | "2013-03-20T12:36:42Z" | "2013-03-20T14:44:44Z" | "2013-03-20T14:44:44Z" | CONTRIBUTOR | null | avoids leaving temp files around if they are interrupted (during dev),
passes thru exceptions
idiom is:
```
with ensure_clean(path=None) as path:
.....
```
rather than
```
try:
....
finally:
os.remove(path)
```
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} | 1 | "2013-03-20T16:15:04Z" | "2014-07-07T07:08:31Z" | "2014-07-07T07:08:31Z" | CONTRIBUTOR | null | there are doc references in basics.rst and dsinstro.rst (called console display)
for dealing with `set_option`
eliminate the section in dsintro.rst and expand/consolidate into basics.rst
(where it is now)
IPython completion docs are also in 2 places, dsintro.rst and indexing.rst
combine/consolidate? (to basics?)
also provide a pointer in the 10min.rst
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} | 5 | "2013-03-20T16:26:39Z" | "2013-03-25T19:48:58Z" | "2013-03-25T19:48:58Z" | MEMBER | null | ```
pd.DataFrame(np.arange(12).reshape((4, 3)), columns=['foo', 'bar', 'baz'],
index=pd.date_range('2000-01-01', periods=3))
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-80-77df75afb13d> in <module>()
1 pd.DataFrame(np.arange(12).reshape((4, 3)), columns=['foo', 'bar', 'baz'],
----> 2 index=pd.date_range('2000-01-01', periods=3))
/Users/wesm/code/pandas/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
408 else:
409 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 410 copy=copy)
411 elif isinstance(data, list):
412 if len(data) > 0:
/Users/wesm/code/pandas/pandas/core/frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy)
555
556 block = make_block(values.T, columns, columns)
--> 557 return BlockManager([block], [columns, index])
558
559 def _wrap_array(self, arr, axes, copy=False):
/Users/wesm/code/pandas/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check)
826
827 if do_integrity_check:
--> 828 self._verify_integrity()
829
830 self._consolidate_check()
/Users/wesm/code/pandas/pandas/core/internals.pyc in _verify_integrity(self)
916 "items")
917 if block.values.shape[1:] != mgr_shape[1:]:
--> 918 raise AssertionError('Block shape incompatible with manager')
919 tot_items = sum(len(x.items) for x in self.blocks)
920 if len(self.items) != tot_items:
AssertionError: Block shape incompatible with manager
```
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} | 0 | "2013-03-20T17:57:04Z" | "2013-03-20T18:27:10Z" | "2013-03-20T18:27:10Z" | CONTRIBUTOR | null | ok
```
In [34]: df = pd.DataFrame({ 'time' : pd.date_range('20130102',periods=5),
'time2' : pd.date_range('20130105',periods=5) })
In [35]: df['off1'] = df['time2']-df['time']
In [36]: df.dtypes
Out[36]:
time datetime64[ns]
time2 datetime64[ns]
off1 timedelta64[ns]
dtype: object
```
dtypes of both existing are changed to `us`
```
In [37]: df['off2'] = df['time']-df['time2']
In [38]: df.dtypes
Out[38]:
time datetime64[ns]
time2 datetime64[ns]
off1 timedelta64[us]
off2 timedelta64[us]
dtype: object
```
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} | 0 | "2013-03-20T18:26:17Z" | "2013-03-20T19:08:24Z" | "2013-03-20T19:08:24Z" | CONTRIBUTOR | null | consolidation of multiple timedelta64[ns] blocks was changing their dtype to `us` for a strange numpy reason (needed a view)
closes #3106
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} | 0 | "2013-03-20T19:34:45Z" | "2013-04-12T04:45:25Z" | "2013-04-12T04:45:25Z" | CONTRIBUTOR | null | There seems to be an intermittent issue which prevents plotting Series/DataFrames with dtype `object`. See traceback below. This manifests for me when reading data from files but not when manually creating dataframes from the command line. It can be worked around by explicitly casting to float64. The presence of NANs does not appear to have an effect.
``` python
>>> df = pd.read_csv('data.csv') # CSV data file with some "NAN" entries
>>> df['chan1'].dtype
dtype('object')
>>> df['chan1'].plot() # fails
>>> df['chan1'].dropna().plot() # fails
>>> df['chan1'].apply(float).plot() # works
>>> df['chan1'].dropna().apply(float).plot() # works
```
This works though:
``` python
df = pd.DataFrame([3, 1, 4, np.nan, 5, 9], dtype=object) # works
df = pd.DataFrame([3, 1, 4, "NAN", 5, 9], dtype=object) # works
df = pd.DataFrame([3, 1, 4, 1, 5, 9], dtype=object) # works
```
Here is the traceback; it is the same as encountered in #478 and presumably in #1818. Note it doesn't include the error message added in 9186cbc
``` python
>>> df = pd.read_csv(r'C:\test\stats.dat')
>>> df['Ts_Avg'].plot()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\pandas\tools\plotting.py", line 1534, in plot_series
plot_obj.generate()
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\pandas\tools\plotting.py", line 735, in generate
self._make_plot()
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\pandas\tools\plotting.py", line 1106, in _make_plot
newline = plotf(*args, **kwds)[0]
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\matplotlib\axes.py", line 3996, in plot
for line in self._get_lines(*args, **kwargs):
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\matplotlib\axes.py", line 330, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\matplotlib\axes.py", line 289, in _plot_args
linestyle, marker, color = _process_plot_format(tup[-1])
File "C:\WinPython-64bit-2.7.3.3\python-2.7.3.amd64\lib\site-packages\matplotlib\axes.py", line 96, in _process_plot_format
if fmt.find('--')>=0:
AttributeError: 'numpy.ndarray' object has no attribute 'find'
```
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} | 1 | "2013-03-20T20:50:37Z" | "2013-03-21T15:14:26Z" | "2013-03-21T15:14:26Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15533093/pandas-access-axis-by-user-defined-name/15534409#15534409
not that this is user friendly, but
```
pd.DataFrame._AXIS_ALIASES['myaxis'1] = 0
pd.DataFrame._AXIS_ALIASES['myaxis2'] = 1
df.sum(axis='myaxis1')
```
should work but doesn't
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https://api.github.com/repos/pandas-dev/pandas/issues/3110 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3110/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3110/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3110/events | https://github.com/pandas-dev/pandas/pull/3110 | 12,253,792 | MDExOlB1bGxSZXF1ZXN0NDcyOTQ1Mg== | 3,110 | BUG: GH3109 fixed issues where passing an axis of 'columns' would fail | {
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} | 12 | "2013-03-20T23:30:00Z" | "2014-06-22T12:52:31Z" | "2013-03-26T14:55:26Z" | CONTRIBUTOR | null | BUG: some operations were expecting an axis number, this fix allows passing the regular axis name as well, e.g.
`df.sum(axis='columns')` will now work
_note: narrowed the scope of this PR to just a bug fix_
```
In [3]: df = pd.DataFrame(np.random.rand(5,2),columns=['A','B'])
In [4]: df.sum(axis=1)
Out[4]:
0 1.150325
1 0.789142
2 0.581486
3 0.864212
4 0.731511
In [5]: df.sum(axis='columns')
---------------------------------------------------------------------------
Exception: Must have 0<= axis <= 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3111 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3111/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3111/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3111/events | https://github.com/pandas-dev/pandas/issues/3111 | 12,256,148 | MDU6SXNzdWUxMjI1NjE0OA== | 3,111 | Panel.to_frame handles np.nans incorrectly. | {
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``` python
df1 = pd.DataFrame({'hi':[0, None, 1]})
df2 = pd.DataFrame({'hi':[None, 0, 2]})
small = pd.Panel({'df1':df1, 'df2':df2})
small.to_frame()
# df1 df2
#major minor
#2 hi 1 2
```
It drops row1 and row2 for both sub-frames.
http://nbviewer.ipython.org/5209728
for a more fleshed out example.
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https://api.github.com/repos/pandas-dev/pandas/issues/3112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3112/events | https://github.com/pandas-dev/pandas/pull/3112 | 12,266,607 | MDExOlB1bGxSZXF1ZXN0NDczNTU3Nw== | 3,112 | ENH: add option display.mpl_style GH3075 | {
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} | 9 | "2013-03-21T09:45:25Z" | "2014-06-28T10:18:34Z" | "2013-03-23T00:40:41Z" | NONE | null | 
#3075
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https://api.github.com/repos/pandas-dev/pandas/issues/3113 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3113/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3113/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3113/events | https://github.com/pandas-dev/pandas/issues/3113 | 12,269,988 | MDU6SXNzdWUxMjI2OTk4OA== | 3,113 | Possible test_plotting regression in 0.11 | {
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} | 10 | "2013-03-21T11:31:08Z" | "2013-03-23T00:40:11Z" | "2013-03-23T00:37:36Z" | NONE | null | I'm getting failures on master. Travis full_deps should have caught this,
maybe something is screwy about my system. passes on 10.1.
check it out before releasing 0.11, fix travis if needs be.
``` python
nosetests ./pandas/tseries/tests/test_plotting.py
.....................EE..EEEEE........EE...........E.
======================================================================
ERROR: test_tsplot (pandas.tseries.tests.test_plotting.TestTSPlot)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/user1/src/pandas/pandas/tseries/tests/test_plotting.py", line 97, in test_tsplot
_check_plot_works(f, s.index.freq, ax=ax, series=s)
File "/home/user1/src/pandas/pandas/tseries/tests/test_plotting.py", line 967, in _check_plot_works
with ensure_path() as path:
NameError: global name 'ensure_path' is not defined
```
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} | 31 | "2013-03-21T11:35:50Z" | "2018-07-06T22:57:17Z" | "2018-07-06T22:57:06Z" | CONTRIBUTOR | null | to allow easy testing of c-contig/f-contig and long frames etc
see #3089
http://mail.python.org/pipermail/pandas-dev/2013-March/000008.html
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cumbersome currently.
Also think of a way to make modification less likely to create merge conflicts.
The current last line in the bugfix section sees more contention then any other line
in the codebase.
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} | 2 | "2013-03-21T14:29:40Z" | "2013-08-06T15:11:39Z" | "2013-08-06T15:11:39Z" | NONE | null | I am trying to append an empty dataframe to another empty one. This fails if at least one of them has a column defined. Below is the code snippet to generate the error.
```
import numpy as np
import pandas as pd
pd.__version__
#'0.10.1'
np.__version__
#'1.7.0'
a = pd.DataFrame()
b = pd.DataFrame()
a.append(b) # works
a = pd.DataFrame(columns=['asdf'])
b = pd.DataFrame(columns=['asdf'])
a.append(b) # fails
a = pd.DataFrame(columns=['asdf'])
b = pd.DataFrame()
a.append(b) # fails
a = pd.DataFrame()
b = pd.DataFrame(columns=['asdf'])
a.append(b) # fails
```
Exception is: ValueError: need at least one array to concatenate
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} | 19 | "2013-03-21T14:32:52Z" | "2016-10-12T23:03:38Z" | "2013-10-14T01:52:20Z" | CONTRIBUTOR | null | Test case
``` python
In [21]: df = pandas.DataFrame({"foo": [1,2,3]})
In [22]: df["foo"] = df["foo"].astype("int32")
In [23]: df.to_excel("foo.xls")
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-23-a8d956752923> in <module>()
----> 1 df.to_excel("foo.xls")
/usr/lib64/python2.7/site-packages/pandas-0.11.0.dev_4e0465d-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in to_excel(self, excel_writer, sheet_name, na_rep, float_format, cols, header, index, index_label, startrow, startcol)
1406 formatted_cells = formatter.get_formatted_cells()
1407 excel_writer.write_cells(formatted_cells, sheet_name,
-> 1408 startrow=startrow, startcol=startcol)
1409 if need_save:
1410 excel_writer.save()
/usr/lib64/python2.7/site-packages/pandas-0.11.0.dev_4e0465d-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in write_cells(self, cells, sheet_name, startrow, startcol)
2194 self._writecells_xlsx(cells, sheet_name, startrow, startcol)
2195 else:
-> 2196 self._writecells_xls(cells, sheet_name, startrow, startcol)
2197
2198 def _writecells_xlsx(self, cells, sheet_name, startrow, startcol):
/usr/lib64/python2.7/site-packages/pandas-0.11.0.dev_4e0465d-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _writecells_xls(self, cells, sheet_name, startrow, startcol)
2270 wks.write(startrow + cell.row,
2271 startcol + cell.col,
-> 2272 val, style)
/usr/lib/python2.7/site-packages/xlwt/Worksheet.pyc in write(self, r, c, label, style)
1030
1031 def write(self, r, c, label="", style=Style.default_style):
-> 1032 self.row(r).write(c, label, style)
1033
1034 def write_rich_text(self, r, c, rich_text_list, style=Style.default_style):
/usr/lib/python2.7/site-packages/xlwt/Row.pyc in write(self, col, label, style)
257 self.__rich_text_helper(col, label, style, style_index)
258 else:
--> 259 raise Exception("Unexpected data type %r" % type(label))
260
261 def set_cell_rich_text(self, col, rich_text_list, style=Style.default_style):
Exception: Unexpected data type <type 'numpy.int32'>
```
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} | 6 | "2013-03-21T15:09:27Z" | "2016-11-10T08:07:25Z" | "2016-11-10T08:07:18Z" | NONE | null | though clear from the examples, an explicit hint that set operations will re-sort your indices in ascending order would be helpful (section 7.3.1 "Set operations on Index objects"). i had indicies like "QK_1 ... QK_9 QK_10" and afterwards they got sorted as "QK_1 QK_10 QK_2...".
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} | 0 | "2013-03-21T17:50:03Z" | "2013-03-21T19:37:21Z" | "2013-03-21T19:37:21Z" | CONTRIBUTOR | null | not correctctly recreating string columns so that could test the min_itemsize if its passed on subsequent appends, see:
http://stackoverflow.com/questions/15488809/how-to-trouble-shoot-hdfstore-exception-cannot-find-the-correct-atom-type?noredirect=1#comment22032050_15488809
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} | 1 | "2013-03-21T18:17:33Z" | "2013-12-19T22:46:05Z" | "2013-12-19T22:46:05Z" | NONE | null | xref https://github.com/pydata/pandas/issues/3089#issuecomment-15255866
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} | 35 | "2013-03-21T20:37:49Z" | "2014-06-13T15:15:36Z" | "2013-03-22T16:40:46Z" | CONTRIBUTOR | null | As per #3089, for now, I'm putting in a "magic" lower limit for memmove of 256 bytes (32 bytes was the case in the affected tests), which seems to be reasonable from local testing (::dubious::), unless someone on [StackOverflow](http://stackoverflow.com/questions/15554565/forcing-gcc-to-perform-loop-unswitching-of-memcpy-runtime-size-checks) gives me a better idea of what to do.
Also, realized that only the stride in the dimension of the copy matters (i.e. the entire array doesn't have to be contiguous, only the copied subarrays do), so I relaxed that requirement (non-contiguous cases don't seem to be tested in our performance regressions, since they're pretty shallow unfortunately, but they do happen often in practice...this should be addressed by #3114).
Here are the vbench results on the low (improved) end (<90%):
```
series_drop_duplicates_string 0.7474 1.0182 0.7340
frame_multi_and_st 763.4459 1008.7359 0.7568
reindex_frame_level_align 1.5111 1.9937 0.7579
reindex_frame_level_reindex 1.5304 1.9711 0.7764
panel_from_dict_equiv_indexes 34.4702 42.6086 0.8090
groupby_frame_cython_many_columns 6.4044 7.4999 0.8539
read_csv_comment2 40.2233 45.7705 0.8788
groupby_series_simple_cython 7.5779 8.4962 0.8919
```
and the high (regressed) end (>105%, as there were no cases of >110%):
```
frame_ctor_nested_dict 108.7902 103.3220 1.0529
stats_rolling_mean 2.8128 2.6283 1.0702
```
I suspect the last results are just noise.
This is 32-bit Linux GCC 4.6.3, mileage may vary (still haven't set up at 64-bit environment), if anyone else could test this commit too that would be great.
---
**EDIT**
repeat run results:
```
reindex_frame_level_reindex 1.4861 1.9335 0.7686
reindex_frame_level_align 1.5578 1.9846 0.7849
panel_from_dict_equiv_indexes 35.0858 42.0521 0.8343
groupby_frame_cython_many_columns 6.5504 7.8101 0.8387
read_csv_comment2 40.2616 46.1578 0.8723
groupby_last_float32 7.7265 8.6995 0.8882
```
and
```
frame_reindex_columns 0.4452 0.4237 1.0507
append_frame_single_homogenous 0.4923 0.4669 1.0543
reshape_pivot_time_series 292.3670 277.2169 1.0547
join_dataframe_integer_key 2.8652 2.6741 1.0714
```
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} | 4 | "2013-03-21T21:56:03Z" | "2017-06-14T10:03:44Z" | "2017-06-14T10:03:44Z" | CONTRIBUTOR | null | propose an option, `sample` or `keeprows` (#14285)
maybe taking a callable, for really just something like `sample=10`
to return you every 10th row
if it takes a callbale couuld use something like: `lambda x: x % 10`
easy way to get a `sample` of the csv file
useful to avoid specifying `skiprows` with a big list
and to solve an issue like this:
http://stackoverflow.com/questions/15555005/get-inferred-dataframe-types-iteratively-using-chunksize
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} | 6 | "2013-03-22T00:51:56Z" | "2014-06-16T09:11:54Z" | "2013-03-22T14:33:05Z" | CONTRIBUTOR | null | closes #2437
closes #2787
closes #3070
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} | 2 | "2013-03-22T13:42:55Z" | "2013-09-08T03:27:22Z" | "2013-09-08T03:27:22Z" | CONTRIBUTOR | null | In Wes' "Python for Data Analysis" book it says on the bottom of page 256 that:
``` python
df.groupby('key1')['data1']
df.groupby('key1')[['data2']]
```
are syntactic sugar for:
``` python
df['data1'].groupby(df['key1'])
df[['data2']].groupby(df['key1'])
```
I find that the first form works but the second one doesn't, in particular the
restriction on the data columns doesn't occurr in the second form. Here is some
example code:
``` python
In [33]: import pandas as pd
In [34]: df = pd.DataFrame(dict(key1=[0]*3+[1]*3, data1=range(6), data2=range(3)*2))
In [35]: print df
data1 data2 key1
0 0 0 0
1 1 1 0
2 2 2 0
3 3 0 1
4 4 1 1
5 5 2 1
In [36]: expected = df['data1'].groupby(df['key1']).count()
In [37]: print expected
key1
0 3
1 3
In [38]: actual = df.groupby('key1')['data1'].count()
In [39]: print actual
key1
0 3
1 3
In [40]: expected2 = df[['data2']].groupby(df['key1']).count()
In [41]: print expected2
data2
key1
0 3
1 3
In [42]: actual2 = df.groupby('key1')[['data2']].count()
In [43]: print actual2
data1 data2 key1
key1
0 3 3 3
1 3 3 3
In [44]: print pd.__version__
0.10.1
```
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```
======================================================================
FAIL: test_first_last_nth_dtypes (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/stephen/Projects/pandas/pandas/tests/test_groupby.py", line 176, in test_first_last_nth_dtypes
assert_frame_equal(first, expected, check_names=False)
File "/home/stephen/Projects/pandas/pandas/util/testing.py", line 217, in assert_frame_equal
check_less_precise=check_less_precise)
File "/home/stephen/Projects/pandas/pandas/util/testing.py", line 185, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
======================================================================
FAIL: test_iloc_getitem_array (pandas.tests.test_indexing.TestIndexing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/stephen/Projects/pandas/pandas/tests/test_indexing.py", line 345, in test_iloc_getitem_array
self.check_result('array like', 'iloc', s.index, 'ix', { 0 : [2,4,6], 1 : [3,6,9], 2: [4,8,12] }, typs = ['ints'])
File "/home/stephen/Projects/pandas/pandas/tests/test_indexing.py", line 249, in check_result
_eq(t, o, a, obj, key1, k2)
File "/home/stephen/Projects/pandas/pandas/tests/test_indexing.py", line 206, in _eq
raise AssertionError(_print('type error'))
AssertionError: None
```
I can look into this and resolve this myself if no one else does, but just wanted to throw it out there first (knee deep in other stuff atm...)
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} | 10 | "2013-03-23T15:58:53Z" | "2014-06-13T02:17:51Z" | "2013-03-25T21:24:56Z" | CONTRIBUTOR | null | closes #2121
Two items were causing slowness
1) using apply for each group (which in this case is equivalent to directly calling
the function on the group). the function that is testing is `fillna` which we have defined as a function of a data frame so its ok here to use the direct function call
I create a slow_path/fast_path with the first group determining the path, not sure this is super robust, but it is a significant source of slowness
2) at the end of the groupby the concetated object has a reindex_like, this is way slow, replacing by sort_index is much faster (this is a multi-index), again not of the robustness, but all tests pass
This is a comparision of the bench/bench_transform.py (supplied in #2121)
The apply_by_group DOES include the sort_index (which is necessary for correctness)
```
In [2]: %timeit apply_by_group(grouped, f_fillna)
1 loops, best of 3: 2.11 s per loop
In [3]: %timeit grouped.transform(f_fillna)
1 loops, best of 3: 2.14 s per loop
```
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} | 37 | "2013-03-23T17:11:01Z" | "2016-09-29T22:44:20Z" | "2016-09-29T22:25:44Z" | CONTRIBUTOR | null | Split off from #3114 ...
It doesn't seem (from my testing) that numpy guarantees 128-bit alignment the start of an array (which is odd, since `malloc` usually does that no matter what, from my testing), and it certainly doesn't 128-bit align each row/column since they're always contiguous.
Apparently it can be done manually via a method like described below:
http://mail.scipy.org/pipermail/scipy-user/2009-March/020283.html
(This just aligns the start of the array, but if you overallocate more and play with strides you can do the same thing to every c-contig row or f-contig column)
The downside is that this has to be done everywhere where allocation currently happens...even with a helper function to do it it's going to be somewhat messy.
Also, to avoid too much overhead, I think this should only be done when each row/column is at least 256 bits (32 bytes) naturally, so the absolute worst case space penalty is 33% + 96 bits constant (on a 32-bit machine) or 20% + 64 bits constant (on a 64-bit machine)...
We can bump that up a bit higher to 512 bits if that still seems like too much, but it means that DataFrames with 5-7 float64/int64 columns could be pathologically misaligned. (So basically, this won't help at all until at least 9 columns are present...)
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} | 1 | "2013-03-23T17:18:27Z" | "2013-03-23T18:21:12Z" | "2013-03-23T18:21:07Z" | CONTRIBUTOR | null | Added a link to the reference example later in the document
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as np.array(timedelta64[ns] series) converts to int64 dtype for some weird reason
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} | 5 | "2013-03-23T18:33:06Z" | "2018-07-06T22:55:58Z" | "2018-07-06T22:55:58Z" | MEMBER | null | http://stackoverflow.com/questions/15570099/pandas-pivot-tables-row-subtotals/15574875#15574875
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} | 2 | "2013-03-23T19:09:30Z" | "2013-10-21T20:22:39Z" | "2013-10-21T20:22:29Z" | MEMBER | null | Currently one of the main things slowing down testing. Too much Python
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} | 12 | "2013-03-23T20:11:18Z" | "2013-09-20T18:10:19Z" | "2013-09-20T18:10:19Z" | CONTRIBUTOR | null | I find that I am sometimes working between python2 and 3 installs and using pickle for passing data is problematic. I am having a look at adding some simple functions like:
pandas.loadnpz
pandas.obj.savenpz (where obj would be DataFrame, Series, Panel etc ...)
Any opinions on this?
Is it already there and I haven't found it?
Is there a natural (efficient) place to do this? It seems the save/load are fairly generic and are attached to all PandasObjects. Maybe at the level of NDFrame would make most sense?
Also, supposing there is interest (or at least not objection) to this, is there any way to add a test for this under the current framework since the full functionality would involve the space of both python2.\* and python3.\* pandas.
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} | 0 | "2013-03-23T22:09:03Z" | "2014-06-19T17:29:39Z" | "2013-03-24T03:07:37Z" | CONTRIBUTOR | null | closes #2763
e.g. was returning float64 on int64 input when possible to preserve
in first/last
more general cases were fixed in prior PRs (e.g. #3041)
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https://api.github.com/repos/pandas-dev/pandas/issues/3153 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3153/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3153/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3153/events | https://github.com/pandas-dev/pandas/pull/3153 | 12,368,351 | MDExOlB1bGxSZXF1ZXN0NDc4MDY0NQ== | 3,153 | DOC: more cookbook examples | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3154 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3154/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3154/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3154/events | https://github.com/pandas-dev/pandas/pull/3154 | 12,368,772 | MDExOlB1bGxSZXF1ZXN0NDc4MDc2NA== | 3,154 | ENH: added numexpr support for where operations | {
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} | 2 | "2013-03-24T02:47:06Z" | "2014-06-17T09:44:18Z" | "2013-03-25T21:24:30Z" | CONTRIBUTOR | null | Added support for numexpr to do where operations, so this will have an
effect on most boolean indexing operations themselves
(inital commit would speedup ops like `df>0`)
this PR is in effect speeding up `np.where`
`df[(df>0)&(df2>0)]` (100k x 100 columns)
`no_ne` is no numexpr at all
`st` is single threaded
This operation is restricted to int64/float64 dtypes
(others would be upcasted, which we could deal with, but
not now)
note: _the above operation could (and should) be much faster
if done as a single operation, but for now this is basically
4 calls to numexpr (3 boolean, then the where), but that's
for another day_
```
----------------------------------------------------------------
Test name | target[ms] | base[ms] | ratio
----------------------------------------------------------------
frame_multi_and 128.5169 385.2890 0.3336
frame_multi_and_st 215.7741 417.0649 0.5174
frame_multi_and_no_ne 397.3501 401.0870 0.9907
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3155 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3155/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3155/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3155/events | https://github.com/pandas-dev/pandas/issues/3155 | 12,369,793 | MDU6SXNzdWUxMjM2OTc5Mw== | 3,155 | rolling_corr not in [-1,1] when using 'center' flag | {
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} | 3 | "2013-03-24T05:21:07Z" | "2013-03-26T11:01:55Z" | "2013-03-26T11:01:55Z" | CONTRIBUTOR | null | The function `rolling_corr` returns values with absolute value greater than one when using the 'center' flag. The following code will reproduce (with high probability):
```
df = pandas.DataFrame(numpy.random.rand(30,2))
pandas.rolling_corr(df[0],df[1],5,center=True)
```
It seems like passing center to `rolling_count` and `rolling_mean` in `rolling_corr` would be in order?
Also, unrelated to this, calling `rolling_corr(5,6,10)` will hit the recursion limit because `_flex_binary_moment` keeps switching the first two arguments around.
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https://api.github.com/repos/pandas-dev/pandas/issues/3156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3156/events | https://github.com/pandas-dev/pandas/issues/3156 | 12,373,518 | MDU6SXNzdWUxMjM3MzUxOA== | 3,156 | Tips and tricks for pandas devs | {
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} | 14 | "2013-03-24T13:03:56Z" | "2014-01-10T12:18:44Z" | "2014-01-10T12:18:32Z" | NONE | null | Working on pandas for a while now, there's a bunch of tools and tricks
I use, here's a list to help pandas devs slip into the zone:
##### Use ipdb rather then pdb with nose: --ipdb --ipdb-fail
https://github.com/flavioamieiro/nose-ipdb
Because tab-completion is not optional.
##### Re-running only failed tests
`nosetests --with-id --failed` will rerun only the tests which failed last
time you ran nosetests --with-id. If you use test_fast.sh
```
test_fast.sh --failed
```
will do what you expect after you had some tests fail
##### Better integration of github and git commandline flow
[hub](https://github.com/defunkt/hub) a wrapper around git, with github
sugar. first and foremost:
```
hub checkout https://github.com/pydata/pandas/pull/1134
```
adds a remote, fetches it, creates a branch for it, and generally puts your right there.
**Note:** see comment below for a way to do this with pure git, if you don't
mind thousands of remote branches.
##### GH issues from the command line
[ghi](https://github.com/stephencelis/ghi)
open/manipulate gh issues from the command line.
I use it to open issues when I hit a bug and want to quickly
open a reminder to fix, without breaking my focus.
##### Testing across python version locally
[tox](https://pypi.python.org/pypi/tox) let's you run the test suites across all python versions using virtualenvs.
Everything is setup in the repo, just install and run.
[detox](https://pypi.python.org/pypi/detox) parallelizes tox.
##### Faster pandas builds/testing
Note: the build cache was baked into setup.py from roughly 0.9.1. as of 0.11.0
it's been factored out into `scripts/use_build_cache.py`, which rewrites setup.py
to use the build cache. The script has been tested as far back as 0.7.0.
Putting the following in your .bashrc
``` bash
# Use the pandas build cache
export BUILD_CACHE_DIR="$HOME/tmp/.pandas_build_cache/"
if [ ! -e $BUILD_CACHE_DIR ]; then
mkdir -p $BUILD_CACHE_DIR ;
fi
echo $BUILD_CACHE_DIR > [pandas repo root dir]/.build_cache_dir
function cdev {
# any recent commit should do
git checkout c69e3aa scripts/use_build_cache.py vb_suite/test_perf.py
scripts/use_build_cache.py $1 # rewire setup.py with build_cache
if [ x"$VIRTUAL_ENV" == x"" ]; then
_SUDO="sudo"
fi
sudo chown $USER -R .;
$_SUDO python ./setup.py clean;
$_SUDO python ./setup.py develop;
sudo chown $USER -R .;
echo "Restoring setup.py"
git checkout setup.py # restore setup.py
}
```
`c69e3aa` can be any recent commit, needs to be bumped if there are updates
to the script.
The pandas build cache code, caches cythonization, compilation and
2to3 artifacts for reuse in subsequent builds.
To compile, use "git reset --hard" to get the commit you're after, then use `cdev`
to build pandas. setup.py will reuse what it can to speed this up.
Note that setup.py gets overwritten, but also restored when the build completes.
With a warm cache, moving to a given commit takes just a few seconds rather then
then the several minutes of a full compile.
You may also run `scripts/use_build_cache.py` prior to launching tox to speed up tetsing.
##### Use ccache
The build cache just described caches things on a very coarse level, if there's
any change to .pyx (cython) files, all the files will be recythonized and rebuilt.
Using ccache (an apt-get+envar away on most distors these days) can speed
up the compilation part by caching the gcc compilation results. Yes, this overlaps
with the caching from the previous section, only it also caches the cythonized
c files.
##### Benchmarking commits
test_perf.sh let's you compare the performance of one commit against
another or benchmark the current HEAD.
It produces a table of results suitable for posting in a PR, and can serialize
the results dataframe into a pickle file, for analysis in pandas.
It can print summary stats over mutliple runs and all sorts of things.
see `test_perf.sh --help`,
##### Easily generate dataframes of different kinds
`mkdf` let's you easily fabricate dataframes of varying dimensions
and arbitrary data:
``` python
from pandas.util.testing import makeCustomDataframe as mkdf
In [12]: mkdf(3,2)
Out[12]:
C0 C_l0_g0 C_l0_g1
R0
R_l0_g0 R0C0 R0C1
R_l0_g1 R1C0 R1C1
R_l0_g2 R2C0 R2C1
# or even...
In [11]: mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=3,data_gen_f=lambda r,c: r*2+c)
Out[11]:
C0 C_l0_g0 C_l0_g1 C_l0_g2
C1 C_l1_g0 C_l1_g1 C_l1_g2
C2 C_l2_g0 C_l2_g1 C_l2_g2
R0 R1
R_l0_g0 R_l1_g0 0 1 2
R_l0_g1 R_l1_g1 2 3 4
R_l0_g2 R_l1_g2 4 5 6
R_l0_g3 R_l1_g3 6 7 8
R_l0_g4 R_l1_g4 8 9 10
# or even
In [19]: mkdf(8,3,r_idx_nlevels=3,r_ndupe_l=[4,2])
Out[19]:
C0 C_l0_g0 C_l0_g1 C_l0_g2
R0 R1 R2
R_l0_g0 R_l1_g0 R_l2_g0 R0C0 R0C1 R0C2
R_l2_g1 R1C0 R1C1 R1C2
R_l1_g1 R_l2_g2 R2C0 R2C1 R2C2
R_l2_g3 R3C0 R3C1 R3C2
R_l0_g1 R_l1_g2 R_l2_g4 R4C0 R4C1 R4C2
R_l2_g5 R5C0 R5C1 R5C2
R_l1_g3 R_l2_g6 R6C0 R6C1 R6C2
R_l2_g7 R7C0 R7C1 R7C2
```
##### ipython startup file
your ipython installation has ~/.ipython/profile_default/startup directory,
put your imports, monkey-patches and utility function there and have them
always available.
##### Speel checking github issues
issues can quickly become stream of conciousness thing once
you start doing a lot of them, if you'd like an easy way to get red squigglies
when your comment contains silly mistaces, you might consider installing
[After the deadline](http://afterthedeadline.com/), available as an extension for firefox and chrome.
##### Handy git commands
There are too many git tricks to cover, but the following are both useful and less commonly known:
Generate a new Hash for the current commit, without any other changes to repo state.
`git commit --amend -C HEAD`
Report author of given commit hash:
```
function gauthor {
git show --format='%an <%ae>' $@ | head -n 1
}
```
and properly assign authorship of a commit:
```
git commit --author="$(gauthor foohash)"
```
where _foohash_ is any previous commit authored by that contributor.
To locate the merge commit that introduced a commit into the branch:
https://github.com/jianli/git-get-merge
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} | [] | closed | false | null | [] | null | 1 | "2013-03-24T17:29:46Z" | "2013-03-24T17:59:51Z" | "2013-03-24T17:59:51Z" | NONE | null | ``` python
In [11]: mom.rolling_mean(s,4,min_periods=3)
Out[11]:
0 NaN
1 NaN
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
dtype: float64
#but
In [12]: mom.rolling_mean(s,4,min_periods=4)
Out[12]:
0 NaN
1 NaN
2 NaN
3 1
4 1
5 NaN
6 NaN
7 NaN
8 NaN
9 1
dtype: float64
```
one more discovered during #2953
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} | 50 | "2013-03-24T18:11:15Z" | "2013-04-09T23:03:32Z" | "2013-04-07T03:13:14Z" | CONTRIBUTOR | null | Example:
```
frame_reindex_axis1 3.1934 2.0423 1.5636
frame_reindex_axis0 1.4083 0.4687 3.0049
Target [b42175f] : dummy commit
Base [da54321] : SCR: format tweaks to test_perf
```
with the following:
```
stephen@trantor:~/Projects/pandas$ git diff da54321 b42175f
diff --git a/TODO.rst b/TODO.rst
index fc87174..97aa56f 100644
--- a/TODO.rst
+++ b/TODO.rst
@@ -1,6 +1,6 @@
DOCS 0.7.0
----------
-- ??? no sort in groupby
+- ???? no sort in groupby
- DONE concat with dict
- Gotchas re: integer indexing
```
These random outliers are making it hard to figure out what's really happening (the optimization I wanted to test seems to improve things by 25%-40% in a C testbed, but I can't get clean results.)
These tests aren't doing anything fancy either, so if they're affected, it's possible the entire suite is affected as well (just less dramatically...)
Possibly some combination of #3129, #3146 (cache and alignment) are related, or maybe some other source of non-determinism in Python and numpy. I can't reproduce this instability in C by trying to proxy it either by randomly adjusting byte alignment, but I haven't tried screwing around with memory paging yet.
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} | [] | closed | false | null | [] | null | 1 | "2013-03-24T20:36:16Z" | "2013-03-24T20:49:38Z" | "2013-03-24T20:49:36Z" | CONTRIBUTOR | null | Allow for more configurability to making scatter plots. Most of the other plotting functions already do this, just giving .scatter_plot() the same power. Works for both single scatters and grouped scatters.
I didn't make a test because anything that would be tested would be matplotlib specific, but I'd be happy to do so if you'd like (test_graphics.py: TestDataFramePlots.test_scatter is a good spot).
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However, I looked on the mailing list and didn't see any guidelines there either, so I think it would be nice to enumerate them here.
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} | 7 | "2013-03-24T21:23:56Z" | "2014-06-30T15:02:10Z" | "2014-02-07T10:29:26Z" | NONE | null | #3155, #2953
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} | 12 | "2013-03-25T00:43:23Z" | "2014-06-14T23:24:34Z" | "2013-04-02T12:46:09Z" | CONTRIBUTOR | null | BUG: #2641 fixes "df.decribe() with boolean column"
Numpy refers to a boolean type as a "numerical type", and both Python
and numpy cast True bools into the value 1 and False bools into the
value 0, so all numpy numerical operations always work.
---
This basically is to solve this issue, which I always found a bit puzzling:
import pandas
df = pandas.DataFrame({
'int1': [1, 2, 3],
'bool1': [False, False, True],
'bool2': [True, True, False],
})
print df.corr()
```
int1
int1 1
```
print df[['bool1', 'bool2']].corr()
```
bool1 bool2
bool1 1 -1
bool2 -1 1
```
After the change:
print df.corr()
```
bool1 bool2 int1
bool1 1.000000 -1.000000 0.866025
bool2 -1.000000 1.000000 -0.866025
int1 0.866025 -0.866025 1.000000
```
This also applies to quite a few other numeric operations on dataframes, which when the dataframe is mixed type defaults to using "is_numeric" to decide which ones to perform the operation on.
I'm not sure how deep the rabbit hole goes for this change and how much stuff it might affect, but all of the tests pass (after of course editing the one that specifically tested this functionality). If there are other potential issues I'd be happy to look into them and make other related fixes.
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} | 14 | "2013-03-25T01:57:36Z" | "2014-01-24T14:35:14Z" | "2013-03-25T12:45:52Z" | CONTRIBUTOR | null | Hit this after updating to '0.11.0.dev-da54321' from master. Haven't had a chance to dig any deeper, other than isolate frame length as a factor.
```
df = pandas.util.testing.makeTimeDataFrame(25000)
df.to_csv("save.csv") # works
df = pandas.util.testing.makeTimeDataFrame(25001)
df.to_csv("save.csv") # throws exception below
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-83-12cc25e3eafd> in <module>()
----> 1 df.to_csv("save.csv")
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_da54321-py2.7-linux-i686.egg/pandas/core/frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, nanRep, encoding, quoting, line_terminator, chunksize, **kwds)
1348 index_label=index_label,
1349 chunksize=chunksize,legacy=kwds.get("legacy",False) )
-> 1350 formatter.save()
1351
1352 def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_da54321-py2.7-linux-i686.egg/pandas/core/format.pyc in save(self)
936
937 else:
--> 938 self._save()
939
940
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_da54321-py2.7-linux-i686.egg/pandas/core/format.pyc in _save(self)
1008 break
1009
-> 1010 self._save_chunk(start_i, end_i)
1011
1012 def _save_chunk(self, start_i, end_i):
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_da54321-py2.7-linux-i686.egg/pandas/core/format.pyc in _save_chunk(self, start_i, end_i)
1029 ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep, float_format=self.float_format)
1030
-> 1031 lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer)
1032
1033 # from collections import namedtuple
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0.dev_da54321-py2.7-linux-i686.egg/pandas/lib.so in pandas.lib.write_csv_rows (pandas/lib.c:13152)()
IndexError: list index out of range
```
Current workaround:
```
df.to_csv("save.csv", legacy=True)
```
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} | 2 | "2013-03-25T02:36:52Z" | "2014-06-12T19:22:09Z" | "2013-04-23T02:10:35Z" | NONE | null | Ssers with xlrd<0.9 will get an error message.
unlike openpyxl, xlrd parse() leaves type inferencing to
TextReader, which looks at the first values only, but this
was already the behaviour for .xls files, so small price to pay.
Removed much code duplication, and xlrd 0.9.0 added py3
support (courtesy of @takluyver, Thanks!), though that's not
tested yet.
Also, closes #1629
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} | 2 | "2013-03-25T09:42:28Z" | "2013-06-23T17:26:13Z" | "2013-03-25T19:46:59Z" | CONTRIBUTOR | null | When trying to rename something in a MultiIndex it doesn't work and flattens the index:
```
df = pd.DataFrame({'Data1': {0: 'present', 1: 'absent'}, 'Data2': {0: 'absent', 1: 'present'}, 'Location': {0: 'loc1', 1: 'loc2'}, 'Name': {0: 'Foo', 1: 'Foo2'}, 'Position': {0: 12345, 1: 67890}}).set_index(['Name', 'Location', 'Position'])
In [2]: df
Out[2]:
Data1 Data2
Name Location Position
Foo loc1 12345 present absent
Foo2 loc2 67890 absent present
In [3]: df.rename({'Foo2': 'Bar'})
Out[3]:
Data1 Data2
(Foo, loc1, 12345) present absent
(Foo2, loc2, 67890) absent present
In [4]: df.rename_axis({'Foo2': 'Bar'}, axis=1)
Out[4]:
Data1 Data2
(Foo, loc1, 12345) present absent
(Foo2, loc2, 67890) absent present
```
_Taken from [this SO question](http://stackoverflow.com/questions/15610819/renaming-one-item-of-a-level-in-pandas-multiindex)._
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} | 1 | "2013-03-25T12:44:47Z" | "2014-07-24T09:41:21Z" | "2013-03-25T13:11:55Z" | CONTRIBUTOR | null | A table will now raise if min_itemsize contains fields which are not queryables
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} | 10 | "2013-03-25T16:13:48Z" | "2014-06-14T06:58:24Z" | "2013-03-28T04:51:48Z" | NONE | null | Accessing out of bounds negative timeseries indices silently return nonsense values.
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} | 7 | "2013-03-25T16:35:57Z" | "2013-03-26T18:25:35Z" | "2013-03-25T19:47:25Z" | CONTRIBUTOR | null | This gives an incorrect error message: "takes **at least 2** arguments ( **2** given)".
_Note: I may be using drop wrong here, but the error message is confusing._
```
import pandas as pd
from StringIO import StringIO
csv = StringIO("""ID,NAME,DATE,VAR1
1,a,03-JAN-2013,69
1,a,04-JAN-2013,77
1,a,05-JAN-2013,75
2,b,03-JAN-2013,69
2,b,04-JAN-2013,75
2,b,05-JAN-2013,72""")
df = pd.read_csv(csv, index_col=['DATE', 'ID'], parse_dates=['DATE'])
df.columns.name = 'Params'
In [11]: a = df.unstack('ID').resample('W-THU').unstack()
In [12]: a.drop(level=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-93-6ee7c2529aa0> in <module>()
----> 1 a.drop(level=0)
TypeError: drop() takes at least 2 arguments (2 given)
In [13]: a.drop(axis=0, level=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-98-081a6501fa61> in <module>()
----> 1 a.drop(axis=0, level=0)
TypeError: drop() takes at least 2 arguments (3 given)
```
_Found when messing about with this [SO question](http://stackoverflow.com/questions/15617496/resampling-a-multi-index-dataframe), which I think may also be a bug..._
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} | 3 | "2013-03-25T17:01:30Z" | "2013-03-31T15:07:42Z" | "2013-03-31T15:07:42Z" | CONTRIBUTOR | null | Stacking this particular MultiIndex column DataFrame throws an error:
```
import pandas as pd
from StringIO import StringIO
csv = StringIO("""ID,NAME,DATE,VAR1
1,a,03-JAN-2013,69
1,a,04-JAN-2013,77
1,a,05-JAN-2013,75
2,b,03-JAN-2013,69
2,b,04-JAN-2013,75
2,b,05-JAN-2013,72""")
df = pd.read_csv(csv, index_col=['DATE', 'ID'], parse_dates=['DATE'])
df.columns.name = 'Params'
In [11]: df.unstack('ID').resample('W-THU')
Out[11]:
Params VAR1
ID 1 2
DATE
2013-01-03 69 69.0
2013-01-10 76 73.5
```
It looks like you ought to be able to stack this over `'ID'`, but it throws an error:
```
In [12]: df.unstack('ID').resample('W-THU').stack('ID')
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-12-e61b475b7bed> in <module>()
----> 1 df.unstack('ID').resample('W-THU').stack('ID')
/Library/Python/2.7/site-packages/pandas/core/frame.pyc in stack(self, level, dropna)
3950 return result
3951 else:
-> 3952 return stack(self, level, dropna=dropna)
3953
3954 def unstack(self, level=-1):
/Library/Python/2.7/site-packages/pandas/core/reshape.pyc in stack(frame, level, dropna)
443
444 if isinstance(frame.columns, MultiIndex):
--> 445 return _stack_multi_columns(frame, level=level, dropna=True)
446 elif isinstance(frame.index, MultiIndex):
447 new_levels = list(frame.index.levels)
/Library/Python/2.7/site-packages/pandas/core/reshape.pyc in _stack_multi_columns(frame, level, dropna)
504 # can make more efficient?
505 if loc.stop - loc.start != levsize:
--> 506 chunk = this.ix[:, this.columns[loc]]
507 chunk.columns = level_vals.take(chunk.columns.labels[-1])
508 value_slice = chunk.reindex(columns=level_vals).values
/Library/Python/2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
32 pass
33
---> 34 return self._getitem_tuple(key)
35 else:
36 return self._getitem_axis(key, axis=0)
/Library/Python/2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
222 continue
223
--> 224 retval = retval.ix._getitem_axis(key, axis=i)
225
226 return retval
/Library/Python/2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
340 raise ValueError('Cannot index with multidimensional key')
341
--> 342 return self._getitem_iterable(key, axis=axis)
343 elif axis == 0:
344 is_int_index = _is_integer_index(labels)
/Library/Python/2.7/site-packages/pandas/core/indexing.pyc in _getitem_iterable(self, key, axis)
406 # this is not the most robust, but...
407 if (isinstance(labels, MultiIndex) and
--> 408 not isinstance(keyarr[0], tuple)):
409 level = 0
410 else:
/Library/Python/2.7/site-packages/pandas/core/index.pyc in __getitem__(self, key)
1745 retval = []
1746 for lev, lab in zip(self.levels, self.labels):
-> 1747 if lab[key] == -1:
1748 retval.append(np.nan)
1749 else:
IndexError: index out of bounds
```
_Taken from this [SO question](http://stackoverflow.com/questions/15617496/resampling-a-multi-index-dataframe)._
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} | 8 | "2013-03-25T17:04:25Z" | "2013-08-18T16:20:41Z" | "2013-08-18T16:20:41Z" | NONE | null | ``` python
In [10]: ix = [-352.737091, 183.575577]
...: df=pd.DataFrame([0,1],index=ix)
...: df.to_csv("/tmp/1.csv")
...: df2=pd.DataFrame.from_csv("/tmp/1.csv")
...: print df
...: print df2
0
-352.737091 0
183.575577 1
0
2105-11-21 22:43:41.128654 0
1936-11-21 22:43:41.128654 1
```
and if you try:
```
In [13]: df2.index.astype('f')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-8d8276604fe4> in <module>()
----> 1 df2.index.astype('f')
/home/user1/src/pandas/pandas/tseries/index.pyc in astype(self, dtype)
624 return self.asi8.copy()
625 else: # pragma: no cover
--> 626 raise ValueError('Cannot cast DatetimeIndex to dtype %s' % dtype)
627
628 @property
ValueError: Cannot cast DatetimeIndex to dtype float32
```
related [SO](http://stackoverflow.com/questions/10591000/specifying-data-type-in-pandas-csv-reader)
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https://api.github.com/repos/pandas-dev/pandas/issues/3173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3173/events | https://github.com/pandas-dev/pandas/pull/3173 | 12,411,325 | MDExOlB1bGxSZXF1ZXN0NDc5ODkwMA== | 3,173 | ENH: assert_X_equal with check_less_precise should apply to index/cols too | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3174/events | https://github.com/pandas-dev/pandas/pull/3174 | 12,413,263 | MDExOlB1bGxSZXF1ZXN0NDc5OTg3OA== | 3,174 | DOC release notes add assert_frame_equal checks index and column names | {
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} | [] | closed | false | null | [] | null | 4 | "2013-03-25T18:21:55Z" | "2013-03-25T19:36:32Z" | "2013-03-25T19:36:32Z" | CONTRIBUTOR | null | Appended to release notes re #2964.
I put it in "API changes" (not sure if that is optimal but I wasn't sure where else it would fit).
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} | 4 | "2013-03-25T19:12:07Z" | "2014-06-12T16:31:08Z" | "2013-03-25T19:42:45Z" | CONTRIBUTOR | null | ### Bug 1: After `rename`, the `name` of the `index` is missing:
```
In [1]: import pandas as pd
In [2]: data = [1, 2]
In [3]: index = pd.Index(['a', 'b'], name='name')
In [4]: df = pd.DataFrame(data, index=index)
In [5]: df
Out[5]:
0
name
a 1
b 2
In [6]: df.rename({'a': 'c'})
Out[6]:
0
c 1
b 2
```
Both `Series` and `DataFrame` has the same problems. The reason is that we didn't set the `name` of the `index` while reconstructing the `DataFrame/Series` by `rename` .
### Bug 2: `rename` cannot work for the `MultiIndex` case, the `index` becomes tuples, the `names` of the index are gone
```
In [1]: import pandas as pd
In [2]: data = [1, 2]
In [3]: index = pd.MultiIndex.from_tuples([('a', 'b'), ('c', 'd')], names=['name1', 'name2'])
In [4]: df = pd.DataFrame(data, index=index)
In [5]: df
Out[5]:
0
name1 name2
a b 1
c d 2
In [6]: df.rename({'a': 'e'})
Out[6]:
0
(a, b) 1
(c, d) 2
```
The missing name issue is same as Bug1. The original `rename` function convert the dict to the mapper function and map each tuple but not each index, and doesn't reconstruct it as `MultiIndex`, so it becomes a `Index` with some tuples.
### Fix
I've fixed the above two bugs, the `rename` will also pass the `name` of the `index` too, the `rename` mapper will go through each index for the `MultiIndex` case now.
I also added some test cases in `test_series.py` and `test_frame.py`
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Thoughts?
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} | 2 | "2013-03-26T18:33:04Z" | "2013-04-08T07:46:53Z" | "2013-04-08T07:46:53Z" | CONTRIBUTOR | null | The pandas.io.parsers module raises a generic exception when a file cannot be
found or opened. This is not consistent with other Python modules which
typically raise the more informative (and specific) IOError. This change should
make the exception handling more consistent and more informative.
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https://api.github.com/repos/pandas-dev/pandas/issues/3182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3182/events | https://github.com/pandas-dev/pandas/issues/3182 | 12,471,321 | MDU6SXNzdWUxMjQ3MTMyMQ== | 3,182 | Create sphinx extension to handle github issue/changeset links | {
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} | 28 | "2013-03-26T21:08:50Z" | "2016-10-12T23:03:38Z" | "2013-06-20T18:10:16Z" | CONTRIBUTOR | null | Add a sphinx extension, such that it is easier to link to github issue or changeset in the rst docs.
Idea comes from [pycon 2013](http://pyvideo.org/video/1703/better-documentation-through-automation-creating) and the [code](https://bitbucket.org/dhellmann/sphinxcontrib-bitbucket) already exists for bitbucket. Maybe also for github somewhere?
This way writing e.g
```
:issue:`GH1234`
```
wil expand to
```
`GH1234 <https://github.com/pydata/pandas/issues/1234>`__
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3183/events | https://github.com/pandas-dev/pandas/pull/3183 | 12,473,999 | MDExOlB1bGxSZXF1ZXN0NDgyOTM3Ng== | 3,183 | WIP,CLN: com.ReprMixin everywhere, cls defines __unicode__ and py2/3 | {
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} | 2 | "2013-03-26T22:17:05Z" | "2013-12-20T15:58:52Z" | "2013-04-23T08:39:13Z" | NONE | null | str,bytes,repr work.
reduces code duplication and boilerplate.
still need to replace
```
class Foo(com.ReprMixin,object)
```
by
```
class Foo(PrintableObject)
```
lots of churn, it'll wait till 0.12.
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https://api.github.com/repos/pandas-dev/pandas/issues/3184 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3184/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3184/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3184/events | https://github.com/pandas-dev/pandas/pull/3184 | 12,495,386 | MDExOlB1bGxSZXF1ZXN0NDgzNTE0NA== | 3,184 | BUG: Append the empty frame with columns, #3121 | {
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} | 3 | "2013-03-27T07:32:36Z" | "2014-06-12T23:15:15Z" | "2013-04-03T08:08:51Z" | CONTRIBUTOR | null | From [issue3121](https://github.com/pydata/pandas/issues/3121)
### Bug
```
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame(columns=['test'])
In [3]: df2 = pd.DataFrame()
In [4]: df1.append(df2)
ValueError: need at least one array to concatenate
```
### Reason
numpy cannot concatenate nothing:
```
In [10]: import numpy as np
In [11]: np.concatenate([])
ValueError: need at least one array to concatenate
```
### Fix
Return an empty numpy array if we find that the arrays to be concatenated are empty
```
# return the empty np array, if nothing to concatenate, #3121
if not to_concat: return np.array([], dtype=object)
```
### Test
Test case `test_append_empty_frame` is added in `test_frame.py`
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} | 1 | "2013-03-27T11:35:52Z" | "2016-09-30T12:35:16Z" | "2016-09-29T22:28:56Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/15652343/how-to-speed-up-pandas-rolling-sum
http://stackoverflow.com/questions/21058333/compute-rolling-maximum-drawdown-of-pandas-series
this numpy recipe might be of iterest to create a strided dimension:
https://github.com/numpy/numpy/pull/31
prob easiest in these cases to create a algos.roll_generic_2d
```
./pandas/core/frame.py: (this is OK, just a fallback if reduce doesn't work)
4109 : result = np.apply_along_axis(func, axis, self.values)
./pandas/core/nanops.py:
201 : return np.apply_along_axis(get_median, axis, values)
235 : result = np.apply_along_axis(__builtin__.min, apply_ax, values)
260 : result = np.apply_along_axis(__builtin__.max, apply_ax, values)
./pandas/core/panel.py:
1246 : result = np.apply_along_axis(func, i, self.values)
./pandas/stats/moments.py:
281 : result = np.apply_along_axis(calc, axis, values)
363 : output = np.apply_along_axis(_ewma, 0, values)
651 : result = np.apply_along_axis(f, axis, values)
```
```
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1000003 8.930 0.000 8.930 0.000 {method 'put' of 'numpy.ndarray' objects}
1000 8.413 0.008 23.225 0.023 shape_base.py:11(apply_along_axis)
1000000 3.612 0.000 3.612 0.000 {pandas.algos.roll_sum}
1000000 0.833 0.000 5.277 0.000 moments.py:277(<lambda>)
1000000 0.628 0.000 4.444 0.000 moments.py:499(call_cython)
2000000 0.551 0.000 0.551 0.000 {method 'tolist' of 'numpy.ndarray' objects}
2 0.517 0.259 0.517 0.259 {pandas.algos.take_2d_axis0_float64_float64}
```
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} | 16 | "2013-03-27T13:22:22Z" | "2019-08-06T21:47:11Z" | "2016-09-29T22:27:47Z" | NONE | null | iotop and a simple-mined c program indicates we're nowhere
near IO-bound in df.to_csv, at about ~10-15x.
It might be possible to speed things up considerably with a fast path
for special cases (numerical only) that don't need fancy quoting and other
bells and whistles provided by the underlying csv python module.
``` c
#include <stdio.h>
#include <stdlib.h>
int main(int argc,char **argv)
{
int i;
FILE *f;
char fmt[] = "%f,%f,%f,%f,%f\n";
while (1) {
f = fopen("out.csv","wb");
for(i=0;i<1000000;i++) {
fprintf(f,fmt, 1.0,2.0,3.0,4.0,5.0);
}
fclose(f);
}
}
```
sustains about 30MB/s on my machine (without even batching writes)
vs ~2-3MB/s for the new (0.11.0) cython df.to_csv().
need to check if it's the stringifying, quoting logic, memory layout, or something
else that constitutes the difference.
Should also yield insights for any future binary serialization format
implemented.
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} | 2 | "2013-03-27T13:53:53Z" | "2016-10-12T23:03:39Z" | "2013-04-01T06:13:36Z" | CONTRIBUTOR | null | Not sure if this is a bug here or not.
```
import pandas as pd
import numpy as np
d = {'noise': np.random.randn(100).cumsum(),
'trend': np.arange(100)}
df = pd.DataFrame(d)
df['resid'] = pd.ols(y=df.noise, x=df.trend).resid
df['resid'].plot(kind='kde', color='k')
```
Will give this traceback:
```
In [14]: Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py", line 927, in draw
func(*args)
File "/usr/local/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/matplotlib/axes.py", line 1999, in draw
a.draw(renderer)
File "/usr/local/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/matplotlib/lines.py", line 494, in draw
gc.set_foreground(self._color)
File "/usr/local/lib/python2.7/site-packages/matplotlib/backends/backend_macosx.py", line 189, in set_foreground
GraphicsContextBase.set_foreground(self, fg, isRGB)
File "/usr/local/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 878, in set_foreground
self._rgb = colors.colorConverter.to_rgba(fg)
File "/usr/local/lib/python2.7/site-packages/matplotlib/colors.py", line 354, in to_rgba
raise ValueError('to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
ValueError: to_rgba: Invalid rgba arg "['k']"
need more than 1 value to unpack
If you suspect this is an IPython bug, please report it at:
https://github.com/ipython/ipython/issues
or send an email to the mailing list at [email protected]
You can print a more detailed traceback right now with "%tb", or use "%debug"
to interactively debug it.
Extra-detailed tracebacks for bug-reporting purposes can be enabled via:
%config Application.verbose_crash=True
```
I haven't add a chance to look through the plotting code yet to see if this should work or not, but it seems like it should.
```
In [14]: pd.__version__
Out[14]: '0.11.0.dev-7d11531'
```
And this happens in IPython both with and without pylab mode. I've tried with other rgba values and they seem to fail too.
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} | 14 | "2013-03-27T14:16:10Z" | "2021-10-25T13:52:42Z" | "2021-10-25T13:52:41Z" | CONTRIBUTOR | null | The plotting functions on DataFrames always have grid=True. This overrides user settings in matplotlibrc.
If you have to created a desired default plot style with the rc file, you want every plot to show with this style unless otherwise specified.
Because pandas overrides this setting you have to manually set the grid option every time.
A better behaviour would be for Pandas not to override any plot styles by default.
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} | 0 | "2013-03-27T15:38:30Z" | "2013-03-27T18:40:35Z" | "2013-03-27T18:40:09Z" | CONTRIBUTOR | null | If a DataFrame has a MultiIndex, the `to_records(index=True)` method gives incorrect results.
A simple example test case is shown here:
http://nbviewer.ipython.org/5255161
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} | 26 | "2013-03-27T15:44:02Z" | "2015-11-16T00:02:02Z" | "2015-11-16T00:02:02Z" | NONE | null | `to_string()`, `to_html()`, `to_latex()`, `formatters`, `float_format`, etc.
related #459
- The existing methods are not as flexible as users sometimes wish,
see #167, #2502, https://github.com/pydata/pandas/pull/2919#issuecomment-14067605, #2942, #2924,#3195, #3196,#3264, #3467, #3197,#4578,#4315, [ml1](https://groups.google.com/forum/?fromgroups=#!topic/pydata/XL2AQd56Zf0)., #6052
- Pandas' core competence is it's data structures and data ops DSL.
the output formats were added for convenience, but don't see much
ongoing work (formatting tables isn't _sexy_).
- Users roll their own using custom code, there's no uniform way to do it
in a way that naturally extends pandas.
Template engines [jinja2, mako] are the accepted way of converting data into documents,
and the web dev community has provided great libraries to do it.
We should piggyback.
_This would be good_:
- Give users the power to scratch their own itch, avoiding the need for pandas devs
do it, or ignoring "fringe" use cases.
- Provides users with a readable baseline , which is easy to modify and adapt to their needs.
- Having a standard way to do it (by customizing a template), users can more easily
have their output blend into python, and they can contribute / manage / source-control
document templates in a clean, self-contained way.
- Escape the "%d" not "{:d}" silliness and format strings in general, which is a relic of
olden 2.5 support days. No more parameter hell necessary to make the existing methods
more expressive. I never _ever_ want to format html tables using function arguments.
- Use a better way to express formatting logic via a DSL, rather then piecing together
python strings.
- Unify the handling of textual representations, not different code, only different template files.
- Can refactor this out of core (but not really, see below)
- Can provide features such as conditional formatting, styling.
_However_,
- Can't really remove existing methods.
- Adding a template engine dependency to pandas is clearly feature-creep (IMO).
_So_,
- Make it a standalone project out of core (or optional dependency?)
- Translate the existing styles into template form.
- Provide hooks in pandas to allow users to optionally install the
library and use it for rendeing
- Let users know it's available for their use.
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} | 2 | "2013-03-27T16:17:34Z" | "2013-04-27T20:27:44Z" | "2013-03-28T11:21:26Z" | CONTRIBUTOR | null | closes #3168
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} | 2 | "2013-03-27T17:30:51Z" | "2013-07-30T23:10:05Z" | "2013-07-30T23:10:05Z" | CONTRIBUTOR | null | The following code:
``` python
import pandas
from io import StringIO
data = u'1,2,3\n4,5,6\n7,8,9'
df = pandas.read_csv(StringIO(data),
dtype={'B': int, 'C':float},
header=None,
names=['A', 'B', 'C'],
converters={'A': str},
)
print(df.dtypes)
print
df = pandas.read_csv(StringIO(data), usecols=[0,2],
dtype={'B': int, 'C':float},
header=None,
names=['A', 'B', 'C'],
converters={'A': str},
)
print(df.dtypes)
print
df = pandas.read_csv(StringIO(data), usecols=[0,1,2],
dtype={'B': int, 'C':float},
header=None,
names=['A', 'B', 'C'],
converters={'A': str},
)
print(df.dtypes)
```
outputs
``` python
A object
B int32
C float64
dtype: object
A object
C float64
dtype: object
A object
B object
C object
dtype: object
```
The last dtype should be the same as the first one. The issue arises when passing in a `converters` dictionary together with a `usecols` that uses all the available columns.
The commit https://github.com/tr11/pandas/commit/1ac3700e72a5861a7d8544a72d77a4d64c71f118 in my fork seems to fix this issue.
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} | 6 | "2013-03-27T19:06:24Z" | "2014-06-26T01:13:10Z" | "2013-03-28T20:19:01Z" | CONTRIBUTOR | null | - ENH: add support for Period formatting (as a native type), add `equals` comparison method
`__eq__` et al added by others
- TST: add period support to makeCustomDataFrame for testing purposes
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``` python
print fl['TurnOnDate'].head()
print 'dtype:'
print fl['TurnOnDate'].dtype
print fl['TurnOnDate'].value_counts().head()
```
Result:
2723 2003-10-01 00:00:00
2841 2011-08-01 00:00:00
2855 2004-12-01 00:00:00
2941 2010-08-01 00:00:00
3019 2005-11-01 00:00:00
Name: TurnOnDate
dtype:
datetime64[ns]
1970-01-23 00:00:00 4
1970-01-19 00:00:00 4
1970-01-21 00:00:00 2
1970-01-22 00:00:00 2
1970-01-20 08:00:00 2
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} | 1 | "2013-03-27T21:16:41Z" | "2018-07-06T22:50:31Z" | "2018-07-06T22:50:25Z" | CONTRIBUTOR | null | pandas.core.format.py:436
if self.show_index_names and self.has_index_names:
Below for pandas.core.format.py:647:
if self.fmt.has_index_names:
I think the second part should look at the show_index_names option.
The problem currently is that you cannot disable the two-row index, and sorttable.js does not work with two-row indexes.
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} | 8 | "2013-03-28T05:11:08Z" | "2020-02-18T12:32:28Z" | "2018-07-06T22:49:14Z" | CONTRIBUTOR | null | The simple library `matrix2latex` has a lot of functionality packed into a single function. I think capturing some of those features in the current `DataFrame.to_latex()` method would make `to_latex()` much more useful.
The flexibility I am talking about is being able to do these types of things:
- pass in arbitrary environment names
- specify the justification of each column (as well as vertical rules between them)
- natural handling for latex symbols like `\infty`
- insert captions and labels into the tabular environment
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The flexibility I am talking about is being able to do these types of things:
- pass in arbitrary environment names
- specify the justification of each column (as well as vertical rules between them)
- natural handling for latex symbols like `\infty`
- insert captions and labels into the tabular environment
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} | [] | closed | false | null | [] | null | 0 | "2013-03-28T05:14:22Z" | "2013-03-28T05:15:17Z" | "2013-03-28T05:15:17Z" | CONTRIBUTOR | null | The simple library `matrix2latex` has a lot of functionality packed into a single function. I think capturing some of those features in the current `DataFrame.to_latex()` method would make `to_latex()` much more useful.
The flexibility I am talking about is being able to do these types of things:
- pass in arbitrary environment names
- specify the justification of each column (as well as vertical rules between them)
- natural handling for latex symbols like `\infty`
- insert captions and labels into the tabular environment
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https://api.github.com/repos/pandas-dev/pandas/issues/3199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3199/events | https://github.com/pandas-dev/pandas/pull/3199 | 12,551,443 | MDExOlB1bGxSZXF1ZXN0NDg1OTEyMg== | 3,199 | BUG MultiIndex sometimes tupled on apply, 2902 | {
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} | [] | closed | false | null | [] | null | 0 | "2013-03-28T07:38:07Z" | "2013-03-28T17:33:50Z" | "2013-03-28T17:33:50Z" | CONTRIBUTOR | null | Fixes #2902.
(broken tests stem from master I branched from, will rebase later to check.)
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https://api.github.com/repos/pandas-dev/pandas/issues/3200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3200/events | https://github.com/pandas-dev/pandas/pull/3200 | 12,553,559 | MDExOlB1bGxSZXF1ZXN0NDg1OTk1Mw== | 3,200 | TST fix broken tests (which were using fancy asserts, broke travis py2.6... | {
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} | [] | closed | false | null | [] | null | 4 | "2013-03-28T09:03:29Z" | "2013-03-28T17:15:16Z" | "2013-03-28T17:15:16Z" | CONTRIBUTOR | null | ... and py3)
Master is not passing travis.
For py2.6: cleared up assertGreater etc. which don't work in py2.6 :(.
For py3: one test is broken in py3 (not sure how to fix):
```
ERROR: test_period_constructor (pandas.tseries.tests.test_period.TestPeriodProperties)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.3_with_system_site_packages/lib/python3.3/site-packages/pandas-0.11.0.dev_4b61349-py3.3-linux-x86_64.egg/pandas/tseries/tests/test_period.py", line 102, in test_period_constructor
self.assert_(i1 != i4)
File "/home/travis/virtualenv/python3.3_with_system_site_packages/lib/python3.3/site-packages/pandas-0.11.0.dev_4b61349-py3.3-linux-x86_64.egg/pandas/tseries/period.py", line 132, in __eq__
raise ValueError("Cannot compare non-conforming periods")
ValueError: Cannot compare non-conforming periods
```
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