Datasets:

DELith

/

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like 0

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MDU6SXNzdWUyMzE5NjAy

Panel property specifying orientation

closed

CONTRIBUTOR

I have on occasion used the from_dict(orientation) method that you created. It would be useful to have a property of the panel object telling me with which orientation the Panel was created, so that I can handle it differently. Thanks Christian

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MDU6SXNzdWUyMzE5NjU0

Support things other than column names in DataFrame.set_index

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MDU6SXNzdWUyMzIwMTUy

Unstack with an object variable overwrites BlockManager

closed

CONTRIBUTOR

Consider the (long) code snippet here. Unless you remove ccode from the df beforehand, you end up with all object dtype for df after unstack. http://www.pastie.org/2904328

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MDU6SXNzdWUyMzIwNTM5

Add ignore option to unstack or something like that

closed

MEMBER

per convo with @jseabold ``` Well, it's only the ones that are the same that I want to ignore. I'd say that's up to the user to do this right. No checking, just take the first value or something, but that's up to you. df = df.unstack('agefrom', ignore='ccode') df = df.unstack('agefrom', keep='ccode') df = df.unstack('agefrom', constant='ccode') ? ``` primary issue i can think will be what to do w/ hierarchical columns with an ignore argument-- should things get munged automatically? should the key for the ignored column be like `'ccode', ''` or `'ccode', None`?

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MDU6SXNzdWUyMzMxMjMy

GroupBy does not handle non-string column labels passed

closed

MEMBER

cc @dhrod5

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MDU6SXNzdWUyMzMyMjg2

Test / handle other dtypes when passing MaskedArray to DataFrame constructor

closed

MEMBER

fyi @dieterv77

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MDU6SXNzdWUyMzM0Nzk5

Improve memory usage of read_csv, Python objects take up too much

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MDU6SXNzdWUyMzM2NDE3

Index name is not properly pickled/unpickled

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MDU6SXNzdWUyMzM2NzA2

A lot of partial setting stuff doesn't work with .ix, needs testing/work

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MDU6SXNzdWUyMzM2Nzkw

Set up some kind of system for testing legacy pickles

closed

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MDU6SXNzdWUyMzQwMTUw

Panel constructor ignores dtype

closed

CONTRIBUTOR

In [11]: Panel(items=arange(3),major_axis=arange(3),minor_axis=arange(3),dtype=np.object).ix[1].dtypes Out[11]: 0 float64 1 float64 2 float64

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MDU6SXNzdWUyMzQzODQy

Override Index.astype to prevent bad things from happening

closed

MEMBER

For example, this should be possible: `index.astype('i8')`

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MDU6SXNzdWUyMzQ0MzU0

API: Making plot methods more uniform in their options

closed

MEMBER

from @lodagro: Been thinking a bit on uniformering signatures for plot, hist, boxplot and what they do/return. For reference, below an overview what pandas and matplotlib.pyplot have. some things that come to mind - +1 for what you did with boxplot, _by_ and _column_ are handy, this could also be used for other kind of plots. - [ ] bar-plot goes through plot, why not bar() itself. - [ ] maybe pie() to? - [ ] plot offers possibility to control _sharex_ and _sharey_, others don not have this control. - [ ] not all functions use the same subplot layout approach. DataFrame.plot() can plot all lines on a single axis or a nx1 layout. DataFrame.hist() uses nxn layout and DataFrame.boxplot is clever and can do nxm, but the user has no control. Maybe add _nrow_, _ncol_ arguments? Default to None, meaning pandas can control layout, if either one defined pandas should compute the other one. Can get tricky for plot(), need to do something with _subplots_ argument - [ ] usage of **_kwds_, e.g boxplot has it, does not use this. maybe add _subplot_kw_ and _figure_kw_ arguments for dispatching argmuments -- like matplotlib does. - [ ] Series.plot has _style_, DataFrame.plot not -- later could maybe use style/colum (like _formatters_ in to_string)? - [ ] rot not used everywhere - [ ] probably many users of pandas are familiar with matplotlib too, in general align plotting signatures and return objects with matplotlib would be a good thing to do? Maybe if i stare at it a bit longer i may have some more ideas, but this is getting long already. What do you think? # for reference ## Series: ``` plot(self, label=None, kind='line', use_index=True, rot=30, ax=None, style='-', grid=True, **kwds) hist(self, ax=None, grid=True, **kwds) ``` ## DataFrame: ``` boxplot(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, **kwds) plot(self, subplots=False, sharex=True, sharey=False, use_index=True, figsize=None, grid=True, legend=True, rot=30, ax=None, kind='line', **kwds) def hist(self, grid=True, **kwds): ``` ## matplotlib.pyplot ``` figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, **kwargs) fig, ax = subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, **fig_kw) ---> always creates a new figure plot(*args, **kwargs) returns list of matplotlib.lines.Line2D boxplot(x, notch=0, sym='b+', vert=1, whis=1.5, positions=None, widths=None, patch_artist=False, bootstrap=None, hold=None) Returns a dictionary, mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. plt.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, hold=None) Return value: If *autopct* is None, return the tuple (*patches*, *texts*): - *patches* is a sequence of :class:`matplotlib.patches.Wedge` instances - *texts* is a list of the label :class:`matplotlib.text.Text` instances. If *autopct* is *None*, return the tuple (*patches*, *texts*) If *autopct* is not *None*, return the tuple (*patches*, *texts*, *autotexts*) plt.bar(left, height, width=0.8, bottom=None, hold=None, **kwargs) Return value is a list of matplotlib.patches.Rectangle instances ```

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MDU6SXNzdWUyMzQ0NjUx

Conditional split/groupby

closed

MEMBER

per Andreas Hilboll question on mailing list ``` Hi, I have a DataFrame ``DF`` where in one column there's the time (in hours, as float). I'm looking for an easy way of iterating over subsets of ``DF`` which are defined by looking for time gaps of 45 minutes or more and using these gaps as seperator between the different iterators. As a first shot, I would do something like this:: diff = np.diff(DF.time) idx = np.where(diff>.75)[0] DFs = [] for i in idx: DFs.append(DF.ix[from:to] But maybe there's a simpler solution? ```

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MDU6SXNzdWUyMzQ2NzEy

Implement equivalent of cut function from base R

closed

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MDU6SXNzdWUyMzUxOTM3

GroupBy level should pass on level name to result index

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MDU6SXNzdWUyMzU1NTY0

test failures with v0.6.0

closed

NONE

OS: Windows 7 python: Enthought Python Distribution, Python 2.7.2 |EPD 7.1-1 (32-bit) pandas: v0.6.0 , git commit 010a620da89e5babc137b924dd1f632b4f6e6fee Running 'nosetests pandas' yields the following errors: # ## ERROR: testFilterWithDataFrameRHS (pandas.stats.tests.test_ols_filter.TestOLSFilter) Traceback (most recent call last): File "C:\Python27\lib\site-packages\pandas\stats\tests\test_ols_filter.py", line 62, in testFilterWithDataFrameRH S index, valid) = _filter_data(self.TS1, self.DF1) ValueError: too many values to unpack # ## ERROR: testFilterWithDictRHS (pandas.stats.tests.test_ols_filter.TestOLSFilter) Traceback (most recent call last): File "C:\Python27\lib\site-packages\pandas\stats\tests\test_ols_filter.py", line 72, in testFilterWithDictRHS index, valid) = _filter_data(self.TS1, self.DICT1) ValueError: too many values to unpack # ## ERROR: testFilterWithSeriesRHS (pandas.stats.tests.test_ols_filter.TestOLSFilter) Traceback (most recent call last): File "C:\Python27\lib\site-packages\pandas\stats\tests\test_ols_filter.py", line 38, in testFilterWithSeriesRHS index, valid) = _filter_data(self.TS1, {'x1' : self.TS2}) ValueError: too many values to unpack # ## ERROR: testFilterWithSeriesRHS2 (pandas.stats.tests.test_ols_filter.TestOLSFilter) Traceback (most recent call last): File "C:\Python27\lib\site-packages\pandas\stats\tests\test_ols_filter.py", line 45, in testFilterWithSeriesRHS2 index, valid) = _filter_data(self.TS2, {'x1' : self.TS1}) ValueError: too many values to unpack # ## ERROR: testFilterWithSeriesRHS3 (pandas.stats.tests.test_ols_filter.TestOLSFilter) Traceback (most recent call last): File "C:\Python27\lib\site-packages\pandas\stats\tests\test_ols_filter.py", line 52, in testFilterWithSeriesRHS3 index, valid) = _filter_data(self.TS3, {'x1' : self.TS4}) ValueError: too many values to unpack --- Ran 1131 tests in 51.592s FAILED (SKIP=3, errors=5)

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MDU6SXNzdWUyMzYwODU3

strange behavior with int in DF

closed

CONTRIBUTOR

This is something that has been diving me nuts for a couple of hours, so I decided to mention this. When I create a dataframe as a class property and append rows as shown in the code below, the 'int' type of 'id' field is being returned as float when accessed with .ix or .xs method: ``` python from pandas import DataFrame,Index import numpy as np class tst(object): def __init__(self): self._header = ['id','bid','ask','last'] # columns of the _data table self._data = DataFrame() def add(self,symbol): ''' add a row to dataframe ''' data = dict(zip(self._header,[1,np.nan,np.nan,np.nan])) row = DataFrame(data, index = Index([symbol])) self._data = self._data.append(row[self._header]) # append data and set correct column order def __repr__(self): return str(self._data) t = tst() t.add('AAA') t.add('BBB') print t._data['id']['AAA'] # int (like it should)! # this is where it goes wrong: print t._data.ix[0,0] # float! print t._data.xs('AAA') # float again! ```

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MDU6SXNzdWUyMzYyOTY5

DataReader - request to truncate retrieved data to specified dates

closed

NONE

Pandas version 0.6.0 from pandas.io.data import DataReader import datetime import matplotlib.pyplot as plt sp500 = DataReader("SPY", "yahoo", start=datetime.datetime(2005, 1,1)) sp500.info() sp500["Adj Close"].plot() plt.show() <class 'pandas.core.frame.DataFrame'> Index: 4744 entries, 1993-01-29 00:00:00 to 2011-11-25 00:00:00 Data columns: Open 4744 non-null values High 4744 non-null values Low 4744 non-null values Close 4744 non-null values Volume 4744 non-null values Adj Close 4744 non-null values dtypes: int64(1), float64(5) As you can see the index begins at 1993-01-29 despite a user specified start date of 2005-1-1. You can specify any ticker and the start date specified in the DataReader function seems to be ignored. Comment from Wes below: The Yahoo API is very unreliable in my experience-- the code currently returns whatever data is returned by the query, so something must have changed recently there. I guess it should truncate to the passed date range regardless of what is returned.

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MDExOlB1bGxSZXF1ZXN0NTMyNTM3

Change Series default float formatter.

closed

CONTRIBUTOR

Series now uses _pandas.core.common._float_format()_ as default float formatter instead of _str()_. _pandas.core.common._float_format()_ is also default float formatter for DataFrame.

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MDU6SXNzdWUyMzY2MDUx

Buglet in groupby when passing function like len with as_index False

closed

MEMBER

``` import numpy as np import pandas def webuse(data, baseurl='http://www.stata-press.com/data/r11/'): """ Parameters ---------- data : str Name of dataset to fetch. Examples -------- >>> dta = webuse('auto') Notes ----- Make sure baseurl has trailing forward slash. Doesn't do any error checking in response URLs. """ # lazy imports from scikits.statsmodels.iolib import genfromdta from urllib2 import urlopen from urlparse import urljoin from StringIO import StringIO url = urljoin(baseurl, data+'.dta') dta = urlopen(url) dta = StringIO(dta.read()) # make it truly file-like return genfromdta(dta) dta = webuse('auto') df = pandas.DataFrame.from_records(dta) ``` then `df.groupby(['foreign', 'rep78'], as_index=False)['mpg'].agg(len)` raises an exception

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MDU6SXNzdWUyMzcyODk2

names param in DataFrame.from_records should be called columns

closed

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MDU6SXNzdWUyMzczMDI4

DataFrame.count corner case with empty columns

closed

MEMBER

Consider changing behavior of: `DataMatrix(index=range(10)).count(1)` returning zeros should not be very harmful.

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MDExOlB1bGxSZXF1ZXN0NTQyMTE1

Pass Series.hist **kwds on to plotting function.

closed

CONTRIBUTOR

Pass Series.hist() **kwds on to plotting function, as mentioned in the documentation.

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MDExOlB1bGxSZXF1ZXN0NTQ0NDUw

more momery efficient describe() for DataFrame

closed

NONE

reindex() in describe() in DataFrame was making copy of the data. I changed describe() so it processes one column at a time instead of get all columns with reindex(). It seems to be a better implementation (much less memory usage).

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MDU6SXNzdWUyNDA4MTgy

Build errors using llvm-gcc

closed

NONE

After upgrading to Xcode 4.2, I'm unable to build pandas. As with previous attempts, I have to hand-compile some of the .pyx files in pandas/src, but now even with this, the package will not build. Here is what I get: ``` building 'pandas._cppsandbox' extension llvm-gcc-4.2 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -I/Users/fonnescj/Developer/include -pipe -arch x86_64 -I/Library/Python/2.7/site-packages/numpy-2.0.0.dev_eb46577_20111130-py2.7-macosx-10.7-x86_64.egg/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/src/cppsandbox.c -o build/temp.macosx-10.7-x86_64-2.7/pandas/src/cppsandbox.o pandas/src/cppsandbox.c:235:19: error: utility: No such file or directory pandas/src/cppsandbox.c:236:15: error: map: No such file or directory In file included from /Library/Python/2.7/site-packages/numpy-2.0.0.dev_eb46577_20111130-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/ndarraytypes.h:1972, from /Library/Python/2.7/site-packages/numpy-2.0.0.dev_eb46577_20111130-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/ndarrayobject.h:17, from /Library/Python/2.7/site-packages/numpy-2.0.0.dev_eb46577_20111130-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/arrayobject.h:14, from pandas/src/cppsandbox.c:239: /Library/Python/2.7/site-packages/numpy-2.0.0.dev_eb46577_20111130-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API" pandas/src/cppsandbox.c: In function ‘__pyx_pf_10cppsandbox_map_indices’: pandas/src/cppsandbox.c:953: error: expected expression before ‘:’ token pandas/src/cppsandbox.c:985: error: ‘__pyx_v_mapping’ undeclared (first use in this function) pandas/src/cppsandbox.c:985: error: (Each undeclared identifier is reported only once pandas/src/cppsandbox.c:985: error: for each function it appears in.) pandas/src/cppsandbox.c:985: error: ‘std’ undeclared (first use in this function) pandas/src/cppsandbox.c:985: error: expected ‘;’ before ‘:’ token pandas/src/cppsandbox.c:953: warning: label ‘std’ defined but not used error: command 'llvm-gcc-4.2' failed with exit status 1 ``` Not sure if this is idiosyncratic, so I will sit tight until others have tried with the llvm compiler.

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MDExOlB1bGxSZXF1ZXN0NTQ1Njc1

Bugfix for issue 419 and .gitignore fixup

closed

CONTRIBUTOR

Two different things fixed.

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MDU6SXNzdWUyNDEzMjQx

add rank and rank correlation methods

closed

CONTRIBUTOR

I do not know what scope you have in mind for DataFrame methods, but rank and spearman correlation would sure be handy ;)

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MDU6SXNzdWUyNDI4MTAy

Boolean ops with Series should reindex?

closed

MEMBER

Currently requires that the indexes be identical to work at all (otherwise raises exception)

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MDU6SXNzdWUyNDI5Njgy

Enable DataFrame.append to add a single row Series

closed

MEMBER

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MDU6SXNzdWUyNDI5NzY0

.ix[row, col] should preserve type

closed

MEMBER

In the case of float/int data, ints currently getting upcasted to float. @lodagro, @sjev

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MDU6SXNzdWUyNDMwMDk1

Element-wise map does not produce DataFrame in apply

closed

MEMBER

``` compareOp = lambda s1, s2 : map(lambda x, y: x if y is None else (y if x is None else min(x, y)), s1, s2) print m2.apply(lambda x : compareOp(x, s)) ```

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MDU6SXNzdWUyNDMwMTQx

Add section about development / contributing to the docs

closed

MEMBER

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MDU6SXNzdWUyNDMyNDY0

Empty series corner case arith bug

closed

MEMBER

This works in 0.4 and crashes in 0.6: ``` import numpy as np from pandas import Series s1 = Series([], [], dtype=np.int32) s2 = Series({'x' : 0.}) print s1 * s2 ```

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MDExOlB1bGxSZXF1ZXN0NTUzNTg0

Using pandas with PyQt

closed

CONTRIBUTOR

I've written a simple data model for showing pandas DataFrame in a QDataTable. It is all in the sandbox, qtpandas.py. This file can be run stand-alone as an example.

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MDU6SXNzdWUyNDMzMTcw

Implement SparseList and SparseArray support data structures

closed

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MDU6SXNzdWUyNDMzMjc0

Fast as possible get_value/put_value methods for Series/DataFrame/Panel

closed

MEMBER

Should feature minimal overhead. Some work already done here

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MDU6SXNzdWUyNDMzMjk1

Methods for inserting values

closed

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MDU6SXNzdWUyNDMzMzUy

Faster conversion of dict of large SparseSeries -> SparseDataFrame

closed

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MDU6SXNzdWUyNDMzNzUx

DataFrame.delevel infer dtypes better

closed

MEMBER

cc @lodagro ``` MultiIndex seems to store the level data always as dtype('object'). When using DataFrame.delevel() the added columns from the index also have dtype('object'). This prevents from using DataFrame.delevel.corr() to have a look at the correlation between the original DataFrame columns and the index level values. Does anyone have an idea to work around this? See example below: In [1]: import pandas In [2]: import numpy as np In [3]: import itertools In [4]: tuples = [tuple for tuple in itertools.product(['foo', 'bar'], [10, 20], [1.0, 1.1])] In [5]: index = pandas.MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2']) In [6]: df = pandas.DataFrame(np.random.randn(8,3), columns=['A', 'B', 'C'], index=index) In [7]: df Out[7]: A B C prm0 prm1 prm2 foo 10 1.0 0.2074 0.3425 -1.295 1.1 0.3194 0.8114 2.133 foo 20 1.0 -0.1798 -1.162 0.5774 1.1 -0.4635 1.436 1.419 bar 10 1.0 -1.013 0.7605 -1.184 1.1 -0.4716 0.6983 0.5209 bar 20 1.0 -0.87 -0.3788 0.272 1.1 1.018 -0.4496 1.132 In [8]: df.corr() Out[8]: A B C A 1 -0.2445 0.3852 B -0.2445 1 0.08211 C 0.3852 0.08211 1 In [9]: df.delevel().corr() --------------------------------------------------------------------------- ValueError Traceback (most recent call last) ... 2535 cols = self.columns 2536 mat = self.as_matrix(cols).T -> 2537 baseCov = np.cov(mat) 2538 2539 sigma = np.sqrt(np.diag(baseCov)) .../python2.7/site-packages/numpy/lib/function_base.pyc in cov(m, y, rowvar, bias, ddof) 1920 raise ValueError("ddof must be integer") 1921 -> 1922 X = array(m, ndmin=2, dtype=float) 1923 if X.shape[0] == 1: 1924 rowvar = 1 ValueError: setting an array element with a sequence. My guess is that this exception is related to the fact corr can not work with strings. So let`s try it without the strings. In [10]: df.delevel()[['prm1', 'prm2', 'A', 'B', 'C']] Out[10]: prm1 prm2 A B C 0 10 1 0.2074 0.3425 -1.295 1 10 1.1 0.3194 0.8114 2.133 2 20 1 -0.1798 -1.162 0.5774 3 20 1.1 -0.4635 1.436 1.419 4 10 1 -1.013 0.7605 -1.184 5 10 1.1 -0.4716 0.6983 0.5209 6 20 1 -0.87 -0.3788 0.272 7 20 1.1 1.018 -0.4496 1.132 In [11]: df.delevel()[['prm1', 'prm2', 'A', 'B', 'C']].corr() --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [...] TypeError: function not supported for these types, and can't coerce safely to supported types In [12]: df.delevel()['prm1'].values.dtype Out[12]: dtype('object') In [13]: df.delevel()['prm1'] Out[13]: 0 10 1 10 2 20 3 20 4 10 5 10 6 20 7 20 Name: prm1 In [14]: index.levels Out[14]: [Index([bar, foo], dtype=object), Index([10, 20], dtype=object), Index([1.0, 1.1], dtype=object)] ```

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MDU6SXNzdWUyNDM4MTA1

issue with py2exe

closed

CONTRIBUTOR

a package produced by py2exe crashes in panel.py `114: f.__doc__ = f.__doc__ % str(op)` with message: <pre> File "zipextimporter.pyo", line 82, in load_module File "pandas\core\panel.pyo", line 145, in <module> File "pandas\core\panel.pyo", line 690, in Panel File "pandas\core\panel.pyo", line 114, in _panel_arith_method TypeError: unsupported operand type(s) for %: 'NoneType' and 'str' </pre> commenting this line out solves the problem in a workaround way. I guess the `.__doc__` property becomes None somehow .

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MDExOlB1bGxSZXF1ZXN0NTU1OTI4

Minor fix for simpler indexing syntax in array of subplots.

closed

CONTRIBUTOR

`plt.subplots()` returns a numpy array, so it's OK to use the more compact/familiar/efficient `a[i,j]` indexing syntax instead of `a[i][j]`.

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MDU6SXNzdWUyNDQ1MDgw

Add NA handling option to GroupBy

closed

MEMBER

Currently all NA groups are excluded though perhaps this should be a flag passed to groupby

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MDU6SXNzdWUyNDQ1NTUy

More convenient syntax for DataFrame constructor to declare order of columns?

closed

CONTRIBUTOR

I've searched the list, docs and issue list and didn't find this addressed, sorry if it was. It seems to me that right now, if I want to specify the order of my columns in new DataFrame built from existing Series, the only way to do it is to build a dict and pass the columns argument separately, with a redundant spelling of the column names: ``` df = pd.DataFrame(dict(m30_t10=splits_30, m15_t10=splits_15, m30_m15=splits_m), columns=['m30_t10', 'm15_t10','m30_m15']) ``` I'm wondering if a more convenient syntax could be devised for this operation. It seems that list-of-tuples already has different semantics, so the minimal change of the above doesn't work: ``` # This gives an error: df3 = pd.DataFrame([('m30_t10', splits_30), ('m15_t10', splits_15), ('m30_m15', splits_m)]) ``` It's unfortunate, b/c the above is incidentally a valid dict constructor: ``` # This is OK: df3 = pd.DataFrame(dict([('m30_t10', splits_30), ('m15_t10', splits_15), ('m30_m15', splits_m)])) ``` But since a list is an inherently ordered data structure, it would be nice if it was accepted also as a constructor for DataFrame, carrying with it the implicit column order.

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MDU6SXNzdWUyNDUzMDc3

Sparse data structure support for MultiIndex?

closed

MEMBER

Should probably just disable it for now. Unclear though

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MDU6SXNzdWUyNDUzMTAy

Cache DataFrame items in Panel to improve performance

closed

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MDU6SXNzdWUyNDUzNTMy

DataFrames should have a name attribute.

closed

MEMBER

was: should DataFrames have a name attribute? @y-p

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MDU6SXNzdWUyNDU0MjA3

Build data structure similar to BlockManager for sparse objects

closed

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MDU6SXNzdWUyNDU2MzM4

Decorator for copying documentation from one function to another

closed

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MDU6SXNzdWUyNDYxMTQ3

When adding a Series to a DataFrame with a different index, the Series gets turned into all NaNs

closed

CONTRIBUTOR

Case in point: ``` python >>> df RP/Rsum P.value ID A_23_P42353 17.8 0 A_23_P369994 15.91 0 A_33_P3262440 436.7 0.0005 A_32_P199429 18.97 0 A_23_P256724 22.24 0 A_33_P3394689 24.24 0 A_33_P3403117 27.14 0 A_24_P252364 28.56 0 A_23_P99515 31.82 0 A_24_P261750 31.46 0 >>> df.dtypes RP/Rsum float64 P.value float64 >>> ids = pandas.Series(['51513', '9201', np.nan, np.nan, '8794', '6530', '7025', '4897', '84935', '11081']) >>> df["test"] = ids >>> df RP/Rsum P.value test ID A_23_P42353 17.8 0 NaN A_23_P369994 15.91 0 NaN A_33_P3262440 436.7 0.0005 NaN A_32_P199429 18.97 0 NaN A_23_P256724 22.24 0 NaN A_33_P3394689 24.24 0 NaN A_33_P3403117 27.14 0 NaN A_24_P252364 28.56 0 NaN A_23_P99515 31.82 0 NaN A_24_P261750 31.46 0 NaN >>> df.dtypes RP/Rsum float64 P.value float64 test object ``` This also happens with float objects and the like. I am not sure in what the trigger is.

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MDU6SXNzdWUyNDYyOTAx

DataFrame.unstack() issue when unstacking multiple levels.

closed

CONTRIBUTOR

Let`s create a dataframe first. ``` python In [170]: import pandas In [171]: import numpy as np In [172]: import itertools In [173]: index = pandas.MultiIndex.from_tuples([t for t in itertools.product(['foo', 'bar'], [0, 1] , ['a', 'b'])]) In [174]: df = pandas.DataFrame(np.random.randn(8,2), columns=['A', 'B'], index=index) In [175]: df Out[175]: A B foo 0 a -1.627 1.301 b -494.049m 120.371m foo 1 a 955.212m 580.450m b -55.806m 169.247m bar 0 a 266.666m 246.100m b 806.406m 894.707m bar 1 a -1.319 475.675m b 638.371m 409.483m ``` Unstacking a single level works fine. ``` python In [176]: df.unstack(level=[1]) Out[176]: A B 0 1 0 1 bar a 266.666m -1.319 246.100m 475.675m b 806.406m 638.371m 894.707m 409.483m foo a -1.627 955.212m 1.301 580.450m b -494.049m -55.806m 120.371m 169.247m ``` Trying to unstack multiple level does not work as expected. Looks like when handling multiple levels the unstack are applied consecutively, ok for the first unstack, but following ones should have level adapted internally, which does not seem to happen. First example tries to unstack level 0 and 1 but level 0 and 2 get unstacked. Second example, idea is to unstack level 1 and 2 but this gives an error, since after level 1 is unstacked, there is no level 2 any more this became level 1 - see also last example. ``` python In [180]: df.unstack(level=[0,1]) Out[180]: A A B B bar foo bar foo a b a b a b a b 0 266.666m 806.406m -1.627 -494.049m 246.100m 894.707m 1.301 120.371m 1 -1.319 638.371m 955.212m -55.806m 475.675m 409.483m 580.450m 169.247m In [177]: df.unstack(level=[1,2]) --------------------------------------------------------------------------- IndexError Traceback (most recent call last) ... IndexError: pop index out of range In [178]: df.unstack(level=[1,1]) Out[178]: A A B B 0 1 0 1 a b a b a b a b bar 266.666m 806.406m -1.319 638.371m 246.100m 894.707m 475.675m 409.483m foo -1.627 -494.049m 955.212m -55.806m 1.301 120.371m 580.450m 169.247m ```

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MDU6SXNzdWUyNDYzNDE2

UnboundLocalError when setting data to a Panel: "local variable 'mat' referenced before assignment"

closed

CONTRIBUTOR

Hi, the following code produces a UnboundLocalError when executed with pandas 0.6.0 / Python 2.7: ``` import datetime import pandas timeidx = pandas.DateRange(start=datetime.datetime(2009,1,1), end=datetime.datetime(2009,12,31), offset=pandas.datetools.MonthEnd()) lons_coarse = np.linspace(-177.5, 177.5, 72) lats_coarse = np.linspace(-87.5, 87.5, 36) P = pandas.Panel(items=timeidx, major_axis=lons_coarse, minor_axis=lats_coarse) P[datetime.datetime(2009,2,28)] = np.random.randn(72*36).reshape((72,36)) ``` Maybe this is to be expected, but it's definitely not intuitive. Since both `P[datetime.datetime(2009,2,28)]` and `np.random.randn(72*36).reshape((72,36))` have shape `(72,36)`, I would expect this to work. Until it does, what would be the way to do this?

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MDExOlB1bGxSZXF1ZXN0NTYzMzgx

Series/DataFrame, make decision between full and short __repr__ configurable

closed

CONTRIBUTOR

Extending pandas.set_printoptions() with two extra arguments: _max_rows_ and _max_columns_. These two numbers impact `pandas.Series.__repr__()` and `pandas.DataFrame.__repr__()`. Decision between a short representation or a full one is now configurable. Default values are set to the current ones, so the change is transparent. When using DataFrames with large number of columns, i got tired of typing `print df.to_string()` all the time :-) ``` python set_printoptions(precision=None, column_space=None, max_rows=None, max_columns=None) Alter default behavior of DataFrame.toString precision : int Floating point output precision column_space : int Default space for DataFrame columns, defaults to 12 max_rows : int max_columns : int max_rows and max_columns are used in __repr__() methods to decide if to_string() or info() is used to render an object to a string. ```

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MDU6SXNzdWUyNDY0MTc2

Saving Panel with float index to HDFStore fails

closed

CONTRIBUTOR

Hi, the following code produces a `ValueError: unrecognized index type <type 'float'>`: ``` import datetime import pandas timeidx = pandas.DateRange(start=datetime.datetime(2009,1,1), end=datetime.datetime(2009,12,31), offset=pandas.datetools.MonthEnd()) lons_coarse = np.linspace(-177.5, 177.5, 72) lats_coarse = np.linspace(-87.5, 87.5, 36) P = pandas.Panel(items=timeidx, major_axis=lons_coarse, minor_axis=lats_coarse) D = pandas.DataFrame(data=np.random.randn(72*36).reshape((72,36)),index=lons_coarse, columns=lats_coarse) P[datetime.datetime(2009,2,28)] = D H = pandas.HDFStore("test.h5", complevel=9, complib='zlib') H['test'] = P H.close() ```

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MDU6SXNzdWUyNDY1NjQw

Possible API method for retrieving rows by number

closed

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A briefer way than, say, `df.xs(df.index[247])` though this isn't really all that bad

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MDU6SXNzdWUyNDY2NzE4

DateRange union bug caused by bad DateOffset comparison

closed

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Reported by user ``` In [35]: rng1 = DateRange('12/5/2011', '12/5/2011') In [36]: rng2 = DateRange('12/2/2011', '12/5/2011') In [37]: rng1.union(rng2) Out[37]: <class 'pandas.core.daterange.DateRange'> offset: <1 BusinessDay>, tzinfo: None [2011-12-02 00:00:00, ..., 2011-12-05 00:00:00] length: 2 In [38]: rng2.offset = datetools.BDay() In [39]: rng1.union(rng2) Out[39]: Index([2011-12-02 00:00:00, 2011-12-05 00:00:00], dtype=object) ```

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MDU6SXNzdWUyNDY4NjI5

Structured array passed to DataFrame constructor can't have passed columns "selected" from it

closed

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Structured array input should be semantically similar to dict input

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MDU6SXNzdWUyNDc1MjAx

winXP 32-bit build problems

closed

NONE

trouble building latest tip on winxp 32: ``` running build running build_py copying pandas\version.py -> build\lib.win32-2.6\pandas running build_ext skipping 'pandas\src\tseries.c' Cython extension (up-to-date) building 'pandas._tseries' extension C:\Program Files\Microsoft Visual Studio 9.0\VC\BIN\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC:\Python26\lib\site-packages\numpy-1.6.1-py2.6-win32.egg\numpy\core\include -IC:\Python26\include -IC:\Python26\PC /Tcpandas\src\tseries.c /Fobuild\temp.win32-2.6\Release\pandas\src\tseries.obj tseries.c c:\documents and settings\reckoner\desktop\pandas\pandas\src\numpy_helper.h(29) : error C2036: 'void *' : unknown size pandas\src\tseries.c(25741) : warning C4244: 'function' : conversion from '__pyx_t_5numpy_int64_t' to 'Py_ssize_t', possible loss of data pandas\src\tseries.c(27030) : warning C4244: 'function' : conversion from '__pyx_t_5numpy_int64_t' to 'Py_ssize_t', possible loss of data pandas\src\tseries.c(29457) : warning C4244: 'function' : conversion from '__pyx_t_5numpy_int64_t' to 'Py_ssize_t', possible loss of data pandas\src\tseries.c(32203) : warning C4244: 'function' : conversion from '__pyx_t_5numpy_int64_t' to 'Py_ssize_t', possible loss of data ``` any help appreciated! I previously had no trouble building this. I have the latest cython and numpy releases. Thanks!

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MDU6SXNzdWUyNDc1NzM2

DataFrame rendering stylists?

closed

CONTRIBUTOR

Been working a bit on the idea of DataFrame rendering stylists. Would like some feedback on the idea / API. Idea is that on top of the _formatters_ used in `DataFrame.to_string()` and `DataFrame.to_html()`, i would like to add _stylists_. A _stylist_ takes as input a string and returns a string, allowing to alter the string representation of each individual cell of a DataFrame. Each DataFrame element goes first through a _formatter_ and the string result from this goes through a _stylist_. This allows to use e.g ANSI escape sequences to change text, background color of how a DataFrame cell is displayed on screen. Or in the `DataFrame.to_html()` case, a _stylists_ can add html div, class tags - which combined with css change the rendering of a html table cell. This is the description of _stylists_ and new API for `DataFrame.to_string()` and `DataFrame.to_html` ``` python class DataFrameFormatter(object): """ Render a DataFrame self.to_string() : console-friendly tabular output self.to_html() : html table """ def __init__(self, frame, buf=None, columns=None, col_space=None, na_rep='NaN', formatters=None, float_format=None, sparsify=True, index_names=True, stylists=None): """ Parameters ---------- stylists : object when indexed with [row_key][column_key] returns a callable object taking as input a string and outputs a string. When rendering a DataFrame, each cell can be altered individually using stylists, a cell gets formatted first with formatters after which a style can be applied. For example the stylist can add ansi escape sequences to display a cell in a different color, add html class or div tags, ... If stylist[row_key][column_key] does not exist, no styling is done to this particular cell of the DataFrame. """ class DataFrame(): def to_string(self, buf=None, columns=None, colSpace=None, na_rep='NaN', formatters=None, float_format=None, sparsify=True, nanRep=None, index_names=True, stylists=None): def to_html(self, buf=None, columns=None, colSpace=None, na_rep='NaN', formatters=None, float_format=None, sparsify=True, index_names=True, stylists=None): ``` A little demo when using stylists on screen: ``` python import pandas import numpy as np from colorama import Fore, Back, Style df = pandas.DataFrame(np.random.randint(0, 10, (5, 2)), columns=['A', 'B'], index=['a', 'b', 'c', 'd', 'e']) red = lambda x: Back.RED + x + Back.RESET green = lambda x: Back.GREEN + x + Back.RESET yellow = lambda x: Back.YELLOW + x + Back.RESET stylists = {'a': {'A': red, 'B': yellow}, 'b': {'A': green}, 'c': {'B': green}} ``` Results in the following (there should be an image here below):  As you can see, more work is needed. The ANSI escape sequences are taken into account when determining the number of characters needed for each column, this is not needed since they are invisible. Solution is e.g to set column widths before stylists are applied, implying that a stylist can not change the width of a column - seems reasonable. Or maybe this should be taken one step further and find some way to combine the functionality of both _formatters_ and _stylists_ into one (have not thought about how this should look)? Ideas, feedback?

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MDU6SXNzdWUyNDc4Njgw

Master does not compile on windows

closed

CONTRIBUTOR

I ran into issues compiling current master on windows. The problem is with the new pandas/src/numpyhelper.h file There are two issues there: 1. MSVC does not support "inline" in C (see http://msdn.microsoft.com/en-us/library/z8y1yy88.aspx) It does support _inline in C however, which has the intended meaning as far as i can tell. 1. MSVC does not support pointer arithmetic on void *. I fixed that with simple cast. There's a diff below, it only show how to make it compile on windows, and is not portable. I didn't put this in a pull request because i'm not sure how you would like to make changes that are portable. ``` diff --git a/pandas/src/numpy_helper.h b/pandas/src/numpy_helper.h index f5779dd..fe40e8c 100644 --- a/pandas/src/numpy_helper.h +++ b/pandas/src/numpy_helper.h @@ -1,30 +1,30 @@ #include "Python.h" #include "numpy/ndarrayobject.h" -inline int +_inline int is_integer_object(PyObject* obj) { return PyArray_IsIntegerScalar(obj); } -inline int +_inline int is_float_object(PyObject* obj) { return (PyFloat_Check(obj) || PyArray_IsScalar(obj, Floating)); } -inline int +_inline int is_bool_object(PyObject* obj) { return (PyBool_Check(obj) || PyArray_IsScalar(obj, Bool)); } -inline int +_inline int is_string_object(PyObject* obj) { return (PyString_Check(obj) || PyUnicode_Check(obj)); } -inline int +_inline int assign_value_1d(PyArrayObject* ap, Py_ssize_t _i, PyObject* v) { char *item; npy_intp i = (npy_intp) _i; - item = PyArray_DATA(ap) + i * PyArray_STRIDE(ap, 0); + item = (char *) PyArray_DATA(ap) + i * PyArray_STRIDE(ap, 0); return PyArray_DESCR(ap)->f->setitem(v, item, ap); } ```

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MDU6SXNzdWUyNDc5NjU0

Enhance DataFrame.align to work with Series input / index-only option, add axis option

closed

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MDU6SXNzdWUyNDgxNDg3

Enable rolling_corr to take DataFrame + Series

closed

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MDExOlB1bGxSZXF1ZXN0NTcwMzc4

Yahoo quote

closed

CONTRIBUTOR

I've added a get_quote_yahoo function to io.data.py. The function returns a DataFrame of the current yahoo data for a set of symbols

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MDU6SXNzdWUyNDk1ODg4

Insert DataFrame rows with dict / other interface

closed

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want to be able to have a syntactically easy way to insert one or more rows in a DataFrame

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MDU6SXNzdWUyNTAwNTQ4

DataFrame.applymap(func) issue when func returns tuple

closed

CONTRIBUTOR

``` python In [82]: def func_tuple(x): ....: return (x,x) ....: In [83]: series = pandas.Series(['a', 'b', 'c', 'd']) In [84]: df = pandas.DataFrame({'A': series}) In [85]: df Out[85]: A 0 a 1 b 2 c 3 d In [86]: df.applymap(func_tuple) Out[86]: A 0 None 1 None 2 None 3 None ``` This works fine ``` python In [87]: map(func_tuple, series) Out[87]: [('a', 'a'), ('b', 'b'), ('c', 'c'), ('d', 'd')] ``` Let`s try with a list: ``` python In [88]: def func_list(x): ....: return [x,x] In [89]: df.applymap(func_list) Out[89]: A 0 ['a', 'a'] 1 ['b', 'b'] 2 ['c', 'c'] 3 ['d', 'd'] ``` Had a look at the pandas.DataFrame.applymap() code and this behavior is related to numpy. I`m using numpy 1.5.1, i know that for other reason numpy 1.6.1 is preferred. Would more recent numpy version fix this? I tried to install more recent version of numpy, but so far unsuccessful (various compilation errors). ``` python In [103]: np.frompyfunc(func_tuple, 1, 1)(series) Out[103]: 0 NaN 1 NaN 2 NaN 3 NaN In [104]: np.frompyfunc(func_list, 1, 1)(series) Out[104]: 0 ['a', 'a'] 1 ['b', 'b'] 2 ['c', 'c'] 3 ['d', 'd'] ```

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MDU6SXNzdWUyNTA2ODIz

Padding / backfilling for monotonic data

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MDU6SXNzdWUyNTA4NDg2

Memory leak in 0.6.0

closed

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The following code results in huge memory usage. Reported by a user ``` df = DataFrame(index=np.arange(175)) for i in range(8000): df[i] = 5 ```

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MDU6SXNzdWUyNTE4MjY1

Optimize DataFrame.append for block structure

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MDU6SXNzdWUyNTE4MzU3

Need to exclude NA values with dtype=object in stat methods

closed

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reported by @npinger, regression from 0.5.0 I think. import pandas lis = [1, 3, 5, None, 7] index = [1, 2, 3, 4, 5] ser = pandas.core.series.Series(data=lis, index=index) ser.mean() Gives this error: Traceback (most recent call last): File "<console>", line 1, in <module> File ".../pandas/core/series.py", line 650, in mean return nanops.nanmean(self.values, skipna=skipna) File ".../pandas/core/nanops.py", line 30, in nanmean the_mean = values.sum(axis) / float(values.shape[axis]) TypeError: unsupported operand type(s) for +: 'int' and 'NoneType' import pandas import numpy as np lis = [1, 3, 5, np.nan, 7] index = [1, 2, 3, 4, 5] ser = pandas.core.series.Series(data=lis, index=index) ser.mean() (gives no error)

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MDU6SXNzdWUyNTE5NjYy

Use OpenMP for computing pairwise correlations

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MDU6SXNzdWUyNTIwMjEz

ImportError: No module named sparse.api

closed

CONTRIBUTOR

Pulled from github master and did install (removed previous version and build dir), but get ImportError. ``` python In [1]: import pandas --------------------------------------------------------------------------- ImportError Traceback (most recent call last) ... ----> 1 import pandas <...>/lib/python2.7/site-packages/pandas/__init__.py in <module>() 21 22 from pandas.core.api import * ---> 23 from pandas.sparse.api import * 24 from pandas.stats.api import * 25 ImportError: No module named sparse.api In [2]: ``` There is no sparce directory in <...>/lib/python2.7/site-packages/pandas. If i build in place and import from the src directory there is no ImportError. UPDATE: Running git bisect, gives the following commits as first _bad_ commit; ``` b3c12b15e8c8a0f87c317fd1bf8e792805f1c582 is the first bad commit commit b3c12b15e8c8a0f87c317fd1bf8e792805f1c582 Author: Wes McKinney <wesmckinn@gmail.com> Date: Sat Dec 10 16:10:26 2011 -0500 REF: broke apart sparse.py, created sparse subpackage :100644 100644 029e140cb88eed926b2eecf8151fc527ac6401f1 d8993b3d91be5a5b070f9960a0ba0c52e91c2445 M .gitignore :040000 040000 62a8d207935e494713c8cc1db338b3e82ffe7af9 fc94ae9d245f0e9747352379eccb36c3298156f6 M pandas ```

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MDU6SXNzdWUyNTIwOTYz

pandas: python remote engine crashes at object introspection of DataFrame instance Optionen

closed

NONE

Dear all, When i try the following pandas example code from http://pandas.sourceforge.net/dsintro.html in the Python interpreter of PyScripter 2.4.3.0 with python 2.6.7 on win32bit and the remote python engine (using rpyc Version 3.2.0) and pandas 0.6.0 from pandas import * d = {'one' : Series([1., 2., 3.], index=['a', 'b', 'c']), 'two' : Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])} df = DataFrame(d) and try to use the object inspection by typing df. in order to get the available methods and attributes of the DataFrame instance, the python engine crashes. The same happens, when i open the "Variables"-Window in PyScripter after having created the df object. Using the internal python engine of PyScripter causes no crash, but the object introspection does not worrk neither. Using the IDLE Python Interpreter, the object inspection works (after waiting for 2-3 seconds), With instances of the Series class, i had no problems. A similar behaviour has been described in http://code.google.com/p/pyscripter/issues/detail?id=579 the problem descibed there has something to do with the impementation of the **getattribute**() method. In the pandas modules only in pandas\core\groupby.py the **getattribute** method is changed. I guess, that the bug is caused by the way, how the IPython completion mechanism has been implemented in pandas. > > The columns are also connected to the IPython completion mechanism so they can be tab-completed: > > In [5]: df.fo<TAB> > > df.foo1 df.foo2 As i don't know, if this behaviour is caused by a problem in the pandas code, or if the remote python engine of PyScripter has a bug, i will cross-post this bug to the PyScripter mailing list. Best regards, Max Thomas Kluyver wrote: > Nothing comes to mind. I've read through the linked bug report for > PyScripter, and it appears that case was due to attributes presenting > invalid special (double-underscore) attributes, like **class** and **doc**. > I don't think that's a problem with the Series objects we're returning. > > Wishful thinking perhaps, but I don't suppose PyScripter can be persuaded > to crash with a traceback? Otherwise, we're kind of debugging blind. > > Thanks, > Thomas

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MDU6SXNzdWUyNTI3MTU2

Issue modifying data in object arrays in DataFrame

closed

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reported by user ``` import pandas pandas import numpy as np from pandas import DataMatrix Y = DataMatrix( np.random.random( (4,4) ) , index = ('a','b','c','d') , columns = ('e','f','g','h' )) Y Y['e'] = Y['e'].astype('object') Y['g']['c'] = np.NaN Y Y.sum() Y['g'].sum() ``` Appears to be caused by a stale reference to a block

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MDU6SXNzdWUyNTI3MzQz

Add inplace option to DataFrame.consolidate

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MDU6SXNzdWUyNTI4Mjg5

DataFrame constructor bug from single Series with different Index

closed

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reported by a user ``` In [7]: DataFrame(Series(np.arange(10)), index=range(5)) --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) /home/wesm/code/pandas/<ipython-input-7-0a85de6b44f0> in <module>() ----> 1 DataFrame(Series(np.arange(10)), index=range(5)) /home/wesm/code/pandas/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy) 206 else: 207 mgr = self._init_ndarray(data, index, columns, dtype=dtype, --> 208 copy=copy) 209 elif isinstance(data, list): 210 mgr = self._init_ndarray(data, index, columns, dtype=dtype, /home/wesm/code/pandas/pandas/core/frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy) 287 columns = _ensure_index(columns) 288 block = make_block(values.T, columns, columns) --> 289 return BlockManager([block], [columns, index]) 290 291 def _wrap_array(self, arr, axes, copy=False): /home/wesm/code/pandas/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check) 274 275 if do_integrity_check: --> 276 self._verify_integrity() 277 278 @property /home/wesm/code/pandas/pandas/core/internals.pyc in _verify_integrity(self) 353 mgr_shape = self.shape 354 for block in self.blocks: --> 355 assert(block.values.shape[1:] == mgr_shape[1:]) 356 tot_items = sum(len(x.items) for x in self.blocks) 357 assert(len(self.items) == tot_items) AssertionError: ```

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MDU6SXNzdWUyNTI5MTQ1

DataFrame.delevel improvements

closed

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from @lodagro ``` Concerning the method name, DataFrame.delevel() is maybe too close to a MultiIndex. DataFrame.deindex() is more general, both are fine for me. And i have no problem to change either. One little remark, for a MultiIndex default column names are inserted when needed, whereas if no MultiIndex is used, and no name - an exception is raised. Would it not be cleaner to do the same for both, either raise an exception if no names are set or enter default column names? ```

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MDU6SXNzdWUyNTI5ODkw

Enable unstack to create Series (currently raises Exception)

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MDU6SXNzdWUyNTMwMjc5

More helpful error message when DataFrame.plot fails on some column

closed

NONE

I'm trying to plot a DataFrame and it wasn't working, so I tried loading the data set from the read_csv docs. It also failed. I think I'm missing something, but I've been successful with matplotlib before. :) ``` from pandas import read_csv import matplotlib.pyplot as plt junk = read_csv('bar.csv') fig = plt.figure() junk.plot() fig.savefig('/tmp/foo3.png') ``` bar.csv ``` year,indiv,zit,xit 1977,"A",1.2,.6 1977,"B",1.5,.5 1977,"C",1.7,.8 1978,"A",.2,.06 1978,"B",.7,.2 1978,"C",.8,.3 1978,"D",.9,.5 1978,"E",1.4,.9 1979,"C",.2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 ``` I get the folowing error: ``` python /tmp/plot.py Traceback (most recent call last): File "/tmp/plot.py", line 6, in <module> junk.plot() File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/core/frame.py", line 3225, in plot ax.plot(x, y, label=str(col), **kwds) File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/matplotlib/axes.py", line 3783, in plot for line in self._get_lines(*args, **kwargs): File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/matplotlib/axes.py", line 317, in _grab_next_args for seg in self._plot_args(remaining, kwargs): File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/matplotlib/axes.py", line 275, in _plot_args linestyle, marker, color = _process_plot_format(tup[-1]) File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/matplotlib/axes.py", line 93, in _process_plot_format if fmt.find('--')>=0: AttributeError: 'numpy.ndarray' object has no attribute 'find' ```

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