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https://api.github.com/repos/pandas-dev/pandas/issues/3101
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3,101
API experiment: lambda grouper based on sliding window
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NONE
null
related #3066, #414, looking for an expressive, flexible 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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#2786: applymap fails with dupe columns, ObjectBlock convert() method bombs
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@jreback, does this look ok? travis is still running. #2786
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TST: added ensure_clean file cleanup context manager for file based test
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"2013-03-20T14:44:44Z"
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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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DOC: consolidate docs for console formatting / options sections
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"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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MDU6SXNzdWUxMjIzOTk3Mg==
3,106
BUG: Multiple timedetal64[ns] operations causes changes in dtypes
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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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BUG: GH 3106, not combining timedelta64[ns] blocks correctly
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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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3,108
Plotting Series/DF of dtype object fails intermittently
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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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BUG/ENH: allow axis name aliasing
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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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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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Panel.to_frame handles np.nans incorrectly.
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1
"2013-03-21T00:57:33Z"
"2013-12-04T00:57:24Z"
"2013-03-21T08:46:26Z"
CONTRIBUTOR
null
It appears that when calling Panel.to_frame() that it will drop any np.nan row. I'm not sure if that's correct behavior, but it will also drop that row for all other sub-frames (Panel items). ``` 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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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
![style](https://f.cloud.github.com/assets/1820866/285040/05e8f29c-920c-11e2-9ba1-1b7fab69a1a6.png) #3075
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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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TST/PERF: make a test input creater instead of np.random.randn mainly for perf testing
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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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BUG: series.plot(kind='bar') should respect pylab color scheme
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"2018-11-16T13:55:54Z"
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BUG: incorrect ensure_clean (was ensure_path)
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Write script to automate additions to RELEASE.rst, what's new with a single command
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launch an editor, add description, specify section, hit save and push. 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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TST: less likelihood of random failing tests
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3,121
appending empty dataframe with columns fails
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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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3,122
ExcelWriter fails with columns with int32 dtype
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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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3,123
documentation improvement: set operations sort the index
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6
"2013-03-21T15:09:27Z"
"2016-11-10T08:07:25Z"
"2016-11-10T08:07:18Z"
NONE
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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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3,124
BUG: GH2817 raise the correct KeyError that the multi-index is not sorted
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0
"2013-03-21T15:45:44Z"
"2014-08-12T14:13:58Z"
"2013-03-22T16:33:55Z"
CONTRIBUTOR
null
closes #2817
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BUG: GH2903 implemented xs for axis=1 with a level specified
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"2013-03-21T16:27:21Z"
"2014-06-30T09:18:29Z"
"2013-03-22T17:34:59Z"
CONTRIBUTOR
null
closes #2903
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12,286,612
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3,126
BUG: HDFStore bug when appending to a table, .typ not recreated on subsequent appends
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"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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3,127
BUG: GH2719, fixed reshape on a Series with invalid input
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CONTRIBUTOR
null
fixes #2719
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3,128
DOC: GH2072 add example and warning on fallback indexing for float indexes.
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CONTRIBUTOR
null
https://github.com/pydata/pandas/issues/2072#issuecomment-9454035
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3,129
Investigate Cache-friendliness of cython code , cachegrind/vtune
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NONE
null
xref https://github.com/pydata/pandas/issues/3089#issuecomment-15255866
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3,130
PERF: Limit memmove to >= 256 bytes, relax contiguity requirements
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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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DOC: GH3105 better error message on ndarray construction
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11
"2013-03-21T20:48:48Z"
"2014-07-31T14:36:10Z"
"2013-03-25T19:48:34Z"
CONTRIBUTOR
null
closes #3105
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3,132
ENH: add an option to read_csv to select a specific rows / sample of the csv file
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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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ENH: extend selection semantics on ordered timeseries to unordered
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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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3,134
groupby() syntactic sugar not working correctly in DataFrame form
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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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3,135
BUG: fix NameError issue in GH2787
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"2013-03-22T14:39:18Z"
CONTRIBUTOR
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closes #2787
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3,136
ENH: GH2437 added selection to an unordered timeseries
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CONTRIBUTOR
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closes #2437
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ENH: GH3070 allow string selection on a DataFrame with a datelike index, to have partial_string semantics (like Series)
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"2013-03-22T14:44:36Z"
"2014-07-10T09:20:30Z"
"2013-03-22T16:20:31Z"
CONTRIBUTOR
null
closes #3070
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3,138
BUG: GH3042 Timestamp now supports classmethod fromordinal similar to datetimes
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"2013-03-22T16:15:06Z"
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CONTRIBUTOR
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closes #3042
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BUG: GH2745 Fix issue with indexing a series with a boolean key a 1-len list on rhs
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CONTRIBUTOR
null
closes #2745
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ENH: read_csv header argument to take a list to reconstruct multi-index
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CONTRIBUTOR
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linkt to #1651
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TST: test for GH2623, object in frame upconverting a datetime64[ns] in another column
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"2013-03-22T22:35:04Z"
"2013-03-23T01:54:17Z"
"2013-03-22T22:43:27Z"
CONTRIBUTOR
null
this is tests only at #2623 is covered by other PRS
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BLD: Nosetests failures on 32-bit (test_first_last_nth_dtypes, test_iloc_getitem_array)
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"2013-03-23T06:27:38Z"
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CONTRIBUTOR
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I'm getting them on two different 32-bit ubuntu machines updated to master (0.11.0.dev-5f2ec15)...I'm guessing these are 32-bit specific since they're `dtype` related: ``` ====================================================================== 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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TST: putmasking changing dtype when not necessary
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PERF: GH2121 groupby transform
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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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PERF, TODO: Ensure 128-bit alignment all rows/columns for SIMD friendliness
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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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DOC: Added error_bad_lines to the summary options for csv
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"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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BUG: GH3094, timedelta64 failing on numpy 1.7.0 (on 2.7)
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"2013-03-23T17:36:49Z"
"2014-08-16T17:44:41Z"
"2013-03-23T17:37:43Z"
CONTRIBUTOR
null
closes #3094 as np.array(timedelta64[ns] series) converts to int64 dtype for some weird reason
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How to make partial sums in pivot tables a little more flexible?
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"2018-07-06T22:55:58Z"
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MEMBER
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http://stackoverflow.com/questions/15570099/pandas-pivot-tables-row-subtotals/15574875#15574875
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PERF/TST: higher performance pandas.util.testing.assert_almost_equal
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Currently one of the main things slowing down testing. Too much Python
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savez method for DataFrame, Series: porting data between python2 and python3
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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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3,152
BUG: GH2763 fixed downcasting of groupby results on SeriesGroupBy
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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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3,153
DOC: more cookbook examples
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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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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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Tips and tricks for pandas devs
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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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3,157
Bug in rolling_mean when minp == win
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``` 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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PERF/TST: Figure out what's going on with vb_suite test result instability (`frame_reindex_*` in particular)
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50
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"2013-04-09T23:03:32Z"
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CONTRIBUTOR
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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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ENH: Pass **kwargs through to matplotlib .scatter()
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"2013-03-24T20:36:16Z"
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CONTRIBUTOR
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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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DOC: Add more detailed description of commit prefixes
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"2013-03-24T20:56:09Z"
"2013-03-24T21:04:38Z"
"2013-03-24T21:04:35Z"
CONTRIBUTOR
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I was unfamiliar with these particular prefixes when I just contributed, so I thought it might be helpful for contributors to have some general guidelines for when to use them. I honestly just guessed at some of them, so if they are wrong please feel free to correct them. 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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WIP: fixes to rolling_window functions with center=True
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"2014-06-30T15:02:10Z"
"2014-02-07T10:29:26Z"
NONE
null
#3155, #2953
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ENH: Declare a BoolBlock as a NumericBlock
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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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to_csv() fail on 0.11.dev
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"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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ENH: Use xlrd >=0.9.0 for both xls/xlsx, sidesteps GH1629
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"2014-06-12T19:22:09Z"
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NONE
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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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Rename in MultiIndex flattens index
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"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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3,166
BUG: GH3163 fixed to_csv with a boundry condition issue at the chunksize break
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"2013-03-25T11:39:15Z"
"2013-03-25T12:45:05Z"
"2013-03-25T12:45:04Z"
CONTRIBUTOR
null
closes #3163
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BUG/CLN: Exception in HDFStore are now ValueError or TypeError
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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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3,168
Index out of bounds error raised for negative indices.
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10
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"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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Drop gives TypeError message for too few arguments when that many are given
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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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Stacking with MultiIndex column DataFrame throws an error
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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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API: Can't override type sniffing in df.from_csv()?
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``` 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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3,172
DOC: minor edits in io.rst / cookbook.rst
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ENH: assert_X_equal with check_less_precise should apply to index/cols too
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DOC release notes add assert_frame_equal checks index and column names
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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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BUG: Fix the rename function for Series and DataFrame, #3165
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CONTRIBUTOR
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### 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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TST: resample test custom_grouper haveing dtype comp error on 32-bit
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CONTRIBUTOR
null
from GH2763 changes
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3,177
DOC: provide an axis alises example in the cookbook.rst
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CONTRIBUTOR
null
dependent on #3110
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3,178
PeriodIndex.tolist should box integers as Period objects with metadata
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KeyError from PeriodIndex.get_loc reported as integer, not Period object
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ENH add time to DatetimeIndex
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"2014-06-19T19:02:16Z"
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CONTRIBUTOR
null
This adds a convenience property (`.time`) to DatetimeIndex, implementation is not very clever, but it means you can extract an array of time objects similar to `.hour` et al. Thoughts?
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3,181
Raise more specific exceptions when parsing fails
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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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MDU6SXNzdWUxMjQ3MTMyMQ==
3,182
Create sphinx extension to handle github issue/changeset links
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"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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3,183
WIP,CLN: com.ReprMixin everywhere, cls defines __unicode__ and py2/3
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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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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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PERF/ENH: np.apply_along_axis -> reduce in moments.py/nanops.py
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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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3,186
PERF: Explore even faster path for df.to_csv
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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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3,187
[BUG] KDE plot and color kwarg
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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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3,188
Grid=True option in plot functions overrides user settings
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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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3,189
DataFrame.to_records(index=True) doesn't work with MultiIndex
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"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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Provide a template-engine based way of rendering pandas data objects
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`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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BUG: GH3168 bug in integer out-of-bounds indexing in Series causing segfault
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"2013-04-27T20:27:44Z"
"2013-03-28T11:21:26Z"
CONTRIBUTOR
null
closes #3168
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Issue with CSV parser when using usecols
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"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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CLN: refactor core/index and tseries/index,period to have their format, to_native_types methods consistent
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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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3,194
value_counts() is changing dates
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Code: ``` 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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HTMLFormatter.to_html() does not look at show_index_names
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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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"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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CONTRIBUTOR
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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 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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BUG MultiIndex sometimes tupled on apply, 2902
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"2013-03-28T07:38:07Z"
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CONTRIBUTOR
null
Fixes #2902. (broken tests stem from master I branched from, will rebase later to check.)
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MDExOlB1bGxSZXF1ZXN0NDg1OTk1Mw==
3,200
TST fix broken tests (which were using fancy asserts, broke travis py2.6...
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"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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