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} | 3 | "2011-11-22T16:20:05Z" | "2014-05-28T23:12:49Z" | "2014-05-28T23:12:49Z" | CONTRIBUTOR | null | 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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} | 2 | "2011-11-22T17:07:04Z" | "2013-07-29T11:43:14Z" | "2011-11-23T06:12:46Z" | CONTRIBUTOR | null | 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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```
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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} | 1 | "2011-11-24T11:27:35Z" | "2011-11-25T05:50:26Z" | "2011-11-25T05:50:26Z" | CONTRIBUTOR | null | 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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} | 2 | "2011-11-24T17:58:54Z" | "2011-12-03T17:51:16Z" | "2011-12-03T17:51:12Z" | MEMBER | null | For example, this should be possible: `index.astype('i8')`
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} | 9 | "2011-11-24T19:21:01Z" | "2018-07-06T19:31:40Z" | "2018-07-06T19:31:39Z" | MEMBER | null | 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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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/20"
} | 1 | "2011-11-24T20:14:15Z" | "2014-06-07T16:57:58Z" | "2013-11-22T09:52:45Z" | MEMBER | null | 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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https://api.github.com/repos/pandas-dev/pandas/issues/417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/417/events | https://github.com/pandas-dev/pandas/issues/417 | 2,355,564 | MDU6SXNzdWUyMzU1NTY0 | 417 | test failures with v0.6.0 | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-26T14:46:45Z" | "2011-11-28T21:11:49Z" | "2011-11-28T21:11:49Z" | NONE | null | 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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} | [] | closed | false | null | [] | null | 6 | "2011-11-27T16:46:13Z" | "2011-12-02T16:18:14Z" | "2011-11-30T21:09:21Z" | CONTRIBUTOR | null | 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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} | 4 | "2011-11-28T00:25:18Z" | "2013-12-04T00:43:49Z" | "2011-12-02T21:03:13Z" | NONE | null | 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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_pandas.core.common._float_format()_ is also default float formatter for DataFrame.
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} | 0 | "2011-11-28T10:45:38Z" | "2011-12-02T22:01:06Z" | "2011-12-02T22:01:06Z" | MEMBER | null | ```
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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} | 0 | "2011-11-28T20:31:16Z" | "2011-11-29T00:10:45Z" | "2011-11-29T00:10:45Z" | MEMBER | null | Consider changing behavior of:
`DataMatrix(index=range(10)).count(1)`
returning zeros should not be very harmful.
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https://api.github.com/repos/pandas-dev/pandas/issues/426 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/426/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/426/comments | https://api.github.com/repos/pandas-dev/pandas/issues/426/events | https://github.com/pandas-dev/pandas/issues/426 | 2,408,182 | MDU6SXNzdWUyNDA4MTgy | 426 | Build errors using llvm-gcc | {
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```
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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} | 2 | "2011-12-02T16:17:55Z" | "2011-12-02T21:07:30Z" | "2011-12-02T21:02:23Z" | MEMBER | null | In the case of float/int data, ints currently getting upcasted to float. @lodagro, @sjev
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} | 0 | "2011-12-02T16:43:57Z" | "2011-12-02T20:46:17Z" | "2011-12-02T20:46:17Z" | MEMBER | null | ```
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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} | 0 | "2011-12-02T20:09:31Z" | "2011-12-02T20:23:30Z" | "2011-12-02T20:23:30Z" | MEMBER | null | 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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```
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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} | 6 | "2011-12-03T17:12:27Z" | "2011-12-08T04:39:42Z" | "2011-12-08T04:39:42Z" | CONTRIBUTOR | null | 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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} | 1 | "2011-12-05T02:28:39Z" | "2019-11-18T17:09:34Z" | "2019-11-18T17:09:33Z" | MEMBER | null | Currently all NA groups are excluded though perhaps this should be a flag passed to groupby
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} | 10 | "2011-12-05T04:14:09Z" | "2011-12-12T05:16:22Z" | "2011-12-12T04:30:56Z" | CONTRIBUTOR | null | 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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} | 1 | "2011-12-05T19:02:41Z" | "2014-08-17T21:24:02Z" | "2014-08-17T21:24:02Z" | MEMBER | null | Should probably just disable it for now. Unclear though
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@y-p
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} | 9 | "2011-12-06T11:25:27Z" | "2011-12-08T04:09:18Z" | "2011-12-07T00:55:55Z" | CONTRIBUTOR | null | 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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} | 1 | "2011-12-06T14:52:41Z" | "2011-12-08T05:06:54Z" | "2011-12-08T05:06:53Z" | CONTRIBUTOR | null | 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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} | 1 | "2011-12-06T15:32:31Z" | "2011-12-08T19:50:19Z" | "2011-12-08T19:50:19Z" | CONTRIBUTOR | null | 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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} | [] | closed | false | null | [] | null | 4 | "2011-12-06T15:48:18Z" | "2014-06-20T00:27:24Z" | "2011-12-08T04:33:25Z" | CONTRIBUTOR | null | 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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} | 2 | "2011-12-06T16:30:34Z" | "2011-12-12T04:09:23Z" | "2011-12-12T04:09:23Z" | CONTRIBUTOR | null | 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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} | 0 | "2011-12-06T20:04:32Z" | "2011-12-08T03:48:17Z" | "2011-12-08T03:48:17Z" | MEMBER | null | 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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} | 1 | "2011-12-06T22:50:14Z" | "2011-12-08T03:40:06Z" | "2011-12-08T03:40:06Z" | MEMBER | null | Structured array input should be semantically similar to dict input
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} | 7 | "2011-12-07T14:40:22Z" | "2011-12-08T23:17:58Z" | "2011-12-07T23:32:35Z" | NONE | null | 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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} | 7 | "2011-12-07T15:29:54Z" | "2019-10-18T15:39:08Z" | "2014-03-11T01:33:18Z" | CONTRIBUTOR | null | 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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} | 3 | "2011-12-07T19:24:05Z" | "2011-12-07T23:32:23Z" | "2011-12-07T23:32:23Z" | CONTRIBUTOR | null | 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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} | 3 | "2011-12-08T23:13:56Z" | "2012-01-14T20:34:08Z" | "2012-01-14T20:34:08Z" | MEMBER | null | want to be able to have a syntactically easy way to insert one or more rows in a DataFrame
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} | 2 | "2011-12-09T11:13:43Z" | "2013-12-04T00:48:07Z" | "2011-12-12T02:58:34Z" | CONTRIBUTOR | null | ``` 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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} | 0 | "2011-12-09T23:30:29Z" | "2011-12-10T20:26:45Z" | "2011-12-10T20:26:45Z" | MEMBER | null | 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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} | 1 | "2011-12-11T23:47:42Z" | "2011-12-12T00:52:38Z" | "2011-12-12T00:52:25Z" | MEMBER | null | 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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} | 2 | "2011-12-12T07:46:39Z" | "2011-12-12T15:58:03Z" | "2011-12-12T15:30:09Z" | CONTRIBUTOR | null | 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 <[email protected]>
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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} | [] | closed | false | null | [] | null | 2 | "2011-12-12T09:38:19Z" | "2012-10-15T18:30:25Z" | "2012-10-15T18:30:25Z" | NONE | null | 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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```
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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```
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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} | 2 | "2011-12-12T21:56:47Z" | "2011-12-29T09:02:10Z" | "2011-12-29T03:21:12Z" | MEMBER | null | 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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} | 3 | "2011-12-12T23:29:44Z" | "2011-12-13T22:28:15Z" | "2011-12-13T22:28:15Z" | NONE | null | 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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```
append_many(cls, frames):
df = None
for f in frames:
df = f if df is None else df.append(f)
return df
```
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} | 0 | "2011-12-13T01:53:11Z" | "2014-02-18T01:42:24Z" | "2014-02-18T01:42:24Z" | MEMBER | null | When a level is already unique, can drop other levels and return an object indexed just by that
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} | 3 | "2011-12-13T13:41:10Z" | "2012-04-23T16:06:47Z" | "2012-04-23T16:06:47Z" | CONTRIBUTOR | null | Suppose I have a daily series ts ending on dec 12th, and that I want to make a monthly series out of it, with ts.asfreq(MonthEnd()).
The last date I will obtain is 2011-11-30, but if I want to use this data to calculate returns I do not want to lose data for December and get a MTD return as last datapoint.
So I was wondering if it makes sense that in this case, ts.asfreq(MonthEnd())[-1] returns NaN with date 2012-12-31, while ts.asfreq(MonthEnd())[-1] returns the value of 2011-12-12 with date 2012-12-31.
Any good reason why this should not be the case?
What do you think?
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} | 1 | "2011-12-13T18:08:37Z" | "2011-12-13T19:36:02Z" | "2011-12-13T19:36:02Z" | MEMBER | null | per @aristotle137
```
In [1216]: l = [[1, 'a'], [2, 'b']]
...: df = DataFrame(data = l, columns = ["num", "str"])
...:
In [1217]: df.dtypes
Out[1217]: num object
str object
```
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} | [] | closed | false | null | [] | null | 3 | "2011-12-13T18:48:39Z" | "2011-12-13T21:55:43Z" | "2011-12-13T21:55:43Z" | NONE | null | I had a few issues during install. I first created a virtual env, then pip installed pandas. It appeared to work but I got the following error when trying to import pandas:
```
Traceback (most recent call last):
File "test.py", line 2, in <module>
from pandas import DataFrame, Series, read_csv
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/__init__.py", line 10, in <module>
import pandas._tseries as lib
ImportError: /home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/_tseries.so: undefined symbol: PyArray_IsIntegerScalar
```
I then went back and read the install docs (yeah, did that backwards) and noticed that my numpy on Ubuntu 10.10 was not at 1.6.1. So I upgraded that and then tried to install pandas from git.
Got the following error:
```
$ python setup.py install
Traceback (most recent call last):
File "setup.py", line 292, in <module>
depends=tseries_depends + ['pandas/src/numpy_helper.h'],
TypeError: unsupported operand type(s) for +: 'NoneType' and 'list'
```
I changed tseries_depends to a `[]` instead of `None` and it progressed furhter but still bailed out:
```
opying pandas/io/tests/test.xls -> build/lib.linux-i686-2.6/pandas/io/tests
running build_ext
Traceback (most recent call last):
File "setup.py", line 355, in <module>
**setuptools_args)
File "/usr/lib/python2.6/distutils/core.py", line 152, in setup
dist.run_commands()
File "/usr/lib/python2.6/distutils/dist.py", line 975, in run_commands
self.run_command(cmd)
File "/usr/lib/python2.6/distutils/dist.py", line 995, in run_command
cmd_obj.run()
File "/usr/lib/python2.6/distutils/command/install.py", line 615, in run
self.run_command('build')
File "/usr/lib/python2.6/distutils/cmd.py", line 333, in run_command
self.distribution.run_command(command)
File "/usr/lib/python2.6/distutils/dist.py", line 995, in run_command
cmd_obj.run()
File "/usr/lib/python2.6/distutils/command/build.py", line 135, in run
self.run_command(cmd_name)
File "/usr/lib/python2.6/distutils/cmd.py", line 333, in run_command
self.distribution.run_command(command)
File "/usr/lib/python2.6/distutils/dist.py", line 995, in run_command
cmd_obj.run()
File "/usr/lib/python2.6/distutils/command/build_ext.py", line 340, in run
self.build_extensions()
File "setup.py", line 239, in build_extensions
self.check_cython_extensions(self.extensions)
File "setup.py", line 236, in check_cython_extensions
""" % src)
Exception: Cython-generated file 'pandas/src/tseries.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
```
So I pip installed cython and I think I'm good now.
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} | 1 | "2011-12-14T09:48:57Z" | "2011-12-15T17:22:57Z" | "2011-12-15T17:22:57Z" | CONTRIBUTOR | null | In [71]: a=DataFrame(randn(10,2),columns=['a','b'])
In [72]: b=DataFrame(randn(10,1),columns=['c']).astype(np.float32)
## In [73]: a.join(b)
TypeError Traceback (most recent call last)
/Users/c.prinoth/<ipython-input-73-3163f462d6f9> in <module>()
----> 1 a.join(b)
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/frame.pyc in join(self, other, on, how, lsuffix, rsuffix)
2614 return self._join_on(other, on, how, lsuffix, rsuffix)
2615 else:
-> 2616 return self._join_index(other, how, lsuffix, rsuffix)
2617
2618 def _join_on(self, other, on, how, lsuffix, rsuffix):
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/frame.pyc in _join_index(self, other, how, lsuffix, rsuffix)
2649
2650 # this will always ensure copied data
-> 2651 merged_data = join_managers(thisdata, otherdata, axis=1, how=how)
2652 return self._constructor(merged_data)
2653
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/internals.pyc in join_managers(left, right, axis, how, copy)
1032 def join_managers(left, right, axis=1, how='left', copy=True):
1033 op = _JoinOperation(left, right, axis=axis, how=how)
-> 1034 return op.get_result(copy=copy)
1035
1036 class _JoinOperation(object):
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/internals.pyc in get_result(self, copy)
1090 if lblk and rblk:
1091 # true merge, do not produce intermediate copy
-> 1092 res_blk = self._merge_blocks(lblk, rblk)
1093 elif lblk:
1094 res_blk = self._reindex_block(lblk, side='left')
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/internals.pyc in _merge_blocks(self, lblk, rblk)
1206 else:
1207 common.take_fast(rblk.values, ridx, None, False,
-> 1208 axis=self.axis, out=out[lk:])
1209
1210 # does not sort
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/common.pyc in take_fast(arr, indexer, mask, needs_masking, axis, out)
247 return take_2d(arr, indexer, out=out, mask=mask,
248 needs_masking=needs_masking,
--> 249 axis=axis)
250
251 result = arr.take(indexer, axis=axis, out=out)
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/common.pyc in take_2d(arr, indexer, out, mask, needs_masking, axis)
232 needs_masking = mask.any()
233
--> 234 result = arr.take(indexer, axis=axis, out=out)
235 result = _maybe_mask(result, mask, needs_masking, axis=axis,
236 out_passed=out is not None)
TypeError: array cannot be safely cast to required type
In [77]: a.dtypes
Out[77]:
a float64
b float64
In [78]: b.dtypes
Out[78]: c float32
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} | 1 | "2011-12-14T16:48:19Z" | "2011-12-15T04:23:28Z" | "2011-12-15T04:23:28Z" | NONE | null | Hi,
It looks like apply_along_axis does not play well with objects that have no length? If I go comment out the numpy 1.6.1 workaround in Python 3.x portion, this goes back to working fine.
Thanks!
For example:
> > > import pandas as ps
> > > x = ps.Series([ps.datetime(2011,4,3)])
> > > x.min()
Traceback (most recent call last):
File "<pyshell#3>", line 1, in <module>
x.min()
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 722, in min
return nanops.nanmin(self.values, skipna=skipna, copy=True)
File "C:\Python27\lib\site-packages\pandas\core\nanops.py", line 118, in nanmin
result = np.apply_along_axis(**builtin**.min, apply_ax, values)
File "C:\Python27\lib\site-packages\numpy\lib\shape_base.py", line 104, in apply_along_axis
outshape[axis] = len(res)
TypeError: object of type 'datetime.datetime' has no len()
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} | 3 | "2011-12-14T18:56:00Z" | "2013-12-04T00:43:45Z" | "2011-12-15T17:38:22Z" | NONE | null | from pandas import Series
s = Series({})
s.to_string()
pandas\core\series.pyc in to_string(self, buf,
na_rep, float_format, nanRep)
460 na_rep = nanRep
461
--> 462 the_repr = self._get_repr(float_format=float_format, na_rep=na_rep)
463 if buf is None:
464 return the_repr
pandas\core\series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
481 string_index = index.format()
482
--> 483 maxlen = max(len(x) for x in string_index)
484 padSpace = min(maxlen, 60)
485
ValueError: max() arg is an empty sequence
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} | 3 | "2011-12-14T18:59:52Z" | "2011-12-15T14:03:33Z" | "2011-12-15T04:40:17Z" | NONE | null | We like to use to_string to print the contents of a Series to a buffer. As of 0.6.1, this has started including "\nLength:..." at the end. Could this be an option? It leaves us with no option but to rewrite to_string if we don't want this included.
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} | 1 | "2011-12-14T19:49:16Z" | "2011-12-15T20:13:46Z" | "2011-12-15T20:13:46Z" | NONE | null | Just playing with pandas. I was using structured array. I tried converting my structured array to pandas. It was OK, until I tried with a field that was itself a (variable length) ndarray.
arr = np.empty ((len(results),), dtype=[(... various fields ommitted,
```
('err', object),
('bursts', int),
('per', float), ...
])
```
Next the array is filled with data.
The field 'err' is then filled in with a ndarray (vector) whose size is variable per instance.
Now convert to df:
from pandas import *
df = DataFrame (arr)
Any attempt to print anything with 'err' field gives an exception
/home/nbecker/hn-8psk/<ipython-input-81-68bace0c3dbe> in <module>()
----> 1 df['err']
/usr/lib/python2.7/site-packages/IPython/core/displayhook.pyc in **call**(self, result)
300 self.start_displayhook()
301 self.write_output_prompt()
--> 302 format_dict = self.compute_format_data(result)
303 self.write_format_data(format_dict)
304 self.update_user_ns(result)
/usr/lib/python2.7/site-packages/IPython/core/displayhook.pyc in compute_format_data(self, result)
213 MIME type representation of the object.
214 """
--> 215 return self.shell.display_formatter.format(result)
216
217 def write_format_data(self, format_dict):
/usr/lib/python2.7/site-packages/IPython/core/formatters.pyc in format(self, obj, include, exclude)
120 continue
121 try:
--> 122 data = formatter(obj)
123 except:
124 # FIXME: log the exception
/usr/lib/python2.7/site-packages/IPython/core/formatters.pyc in **call**(self, obj)
441 type_pprinters=self.type_printers,
442 deferred_pprinters=self.deferred_printers)
--> 443 printer.pretty(obj)
444 printer.flush()
445 return stream.getvalue()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.pyc in pretty(self, obj)
349 if hasattr(obj_class, '_repr_pretty_'):
350 return obj_class._repr_pretty_(obj, self, cycle)
--> 351 return _default_pprint(obj, self, cycle)
352 finally:
353 self.end_group()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
467 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
468 # A user-provided repr.
--> 469 p.text(repr(obj))
470 return
471 p.begin_group(1, '<')
/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in **repr**(self)
435 result = self._get_repr(print_header=True,
436 length=len(self) > 50,
--> 437 name=True)
438 else:
439 result = '%s' % ndarray.__repr__(self)
/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
494 return '%s %s' % (str(k).ljust(padSpace), v)
495
--> 496 it = [_format(idx, v) for idx, v in izip(string_index, vals)]
497
498 if print_header and have_header:
/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in _format(k, v)
488
489 def _format(k, v):
--> 490 if isnull(v):
491 v = na_rep
492 if isinstance(v, (float, np.floating)):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [82]:
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} | 1 | "2011-12-14T23:13:22Z" | "2013-12-04T00:43:45Z" | "2011-12-15T04:05:07Z" | NONE | null | from pandas import DataFrame
DataFrame([],[]) # DataFrame([]) does the same thing
pandas\core\frame.pyc in **init**(self, data, index, columns, dtype, copy)
208 copy=copy)
209 elif isinstance(data, list):
--> 210 if isinstance(data[0], (list, tuple)):
211 data, columns = _list_to_sdict(data, columns)
212 mgr = self._init_dict(data, index, columns, dtype=dtype)
IndexError: list index out of range
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} | 1 | "2011-12-15T08:52:27Z" | "2011-12-15T17:53:24Z" | "2011-12-15T17:53:24Z" | CONTRIBUTOR | null | The following code fails on pandas 0.6.1
```
import numpy as np
import pandas
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = pandas.DataFrame(data, index=idx, columns=col)
H = pandas.HDFStore("test.h5", "w")
H['data'] = DF
H.close()
```
The traceback is as follows:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home2/hilboll/<ipython-input-15-a313de079f58> in <module>()
----> 1 H['data'] = DF
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in __setitem__(self, key, value)
121
122 def __setitem__(self, key, value):
--> 123 self.put(key, value)
124
125 def __len__(self):
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in put(self, key, value, table, append, compression)
259 """
260 self._write_to_group(key, value, table=table, append=append,
--> 261 comp=compression)
262
263 def _get_handler(self, op, kind):
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, table, append, comp)
327 wrapper = lambda value: handler(group, value)
328
--> 329 wrapper(value)
330 group._v_attrs.pandas_type = kind
331
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in <lambda>(value)
325
326 handler = self._get_handler(op='write', kind=kind)
--> 327 wrapper = lambda value: handler(group, value)
328
329 wrapper(value)
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_frame(self, group, df)
336
337 def _write_frame(self, group, df):
--> 338 self._write_block_manager(group, df._data)
339
340 def _read_frame(self, group, where=None):
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_block_manager(self, group, data)
347 group._v_attrs.ndim = data.ndim
348 for i, ax in enumerate(data.axes):
--> 349 self._write_index(group, 'axis%d' % i, ax)
350
351 # Supporting mixed-type DataFrame objects...nontrivial
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_index(self, group, key, index)
439 else:
440 setattr(group._v_attrs, '%s_variety' % key, 'regular')
--> 441 converted, kind, _ = _convert_index(index)
442 self._write_array(group, key, converted)
443 node = getattr(group, key)
/home2/hilboll/.virtualenvs/pydoas/lib/python2.7/site-packages/pandas/io/pytables.pyc in _convert_index(index)
734 return np.asarray(values, dtype=np.float64), 'float', atom
735 else: # pragma: no cover
--> 736 raise ValueError('unrecognized index type %s' % type(values[0]))
737
738 def _read_array(group, key):
ValueError: unrecognized index type <type 'tuple'>
```
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} | 1 | "2011-12-15T14:14:14Z" | "2011-12-15T17:06:47Z" | "2011-12-15T17:06:47Z" | CONTRIBUTOR | null | Since Series.to_string no longer adds length field by default, unittest needs to reflect this.
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} | 1 | "2011-12-15T15:46:25Z" | "2011-12-15T17:04:54Z" | "2011-12-15T17:04:54Z" | CONTRIBUTOR | null | In [1]: from pandas import *
In [2]: df = DataFrame([1,2,3,4], index=['a','b','c','d'])
In [3]: df
Out[3]:
0
a 1
b 2
c 3
d 4
In [4]: df.dtypes
Out[4]: 0 int64
In [5]: df.dtypes[0]
Out[5]: dtype('int64')
In [6]: df.dtypes[1]
**\* glibc detected **\* /home/adam/.virtualenvs/py27/bin/python: free(): corrupted unsorted chunks: 0x00000000025db8e0 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x78a96)[0x7f8751957a96]
/lib/x86_64-linux-gnu/libc.so.6(cfree+0x6c)[0x7f875195bd7c]
/home/adam/.virtualenvs/py27/bin/python[0x4354d8]
/home/adam/.virtualenvs/py27/bin/python[0x447a2c]
/home/adam/.virtualenvs/py27/bin/python[0x4e4fe3]
/home/adam/.virtualenvs/py27/bin/python[0x4e500d]
======= Memory map: ========
00400000-00633000 r-xp 00000000 08:05 5373988 /home/adam/.virtualenvs/py27/bin/python
00832000-00833000 r--p 00232000 08:05 5373988 /home/adam/.virtualenvs/py27/bin/python
00833000-0089c000 rw-p 00233000 08:05 5373988 /home/adam/.virtualenvs/py27/bin/python
0089c000-008ae000 rw-p 00000000 00:00 0
016f0000-0278e000 rw-p 00000000 00:00 0 [heap]
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} | 3 | "2011-12-16T18:35:02Z" | "2011-12-19T13:36:59Z" | "2011-12-18T05:29:59Z" | CONTRIBUTOR | null | I'm trying to build a dataframe from a list of list and I get an "AssertionError" and I have no idea what I did wrong or what I should do differently. A shorted version of the data and code:
pprint(_fields)
[ 'name',
'timescited',
'weight',
'closeness',
'betweenness',
'degree',
'numberofworks',
'pagerank',
'pages',
'constraint',
'citationaverage',
'eigenvector']
pprint(_data2)
[ ['Huselid, Ma', 'Gulati, R', 'Damanpour, F', 'Mcallister, Dj', 'Tsai, Wp'],
[2721, 5251, 1269, 1287, 2834],
[6, 17, 6, 3, 6],
[ 0.0002510562854116362,
0.00025108339541739277,
0.00025105435150127433,
0.00025106898828104144,
0.0002510723039607744],
[ 23311.0,
173596.65383408728,
79279.82282582425,
18701.108026093425,
57261.74716265692],
[4, 11, 9, 3, 7],
[6, 17, 6, 3, 6],
[ 0.0001438440250079284,
0.0003063098736173118,
0.00027071793856870986,
7.839463995668608e-05,
0.00020047003509480145],
[130, 387, 148, 68, 88],
[ 0.28573421556122447,
0.12411566040831735,
0.19959178902739988,
0.3703749360800643,
0.200788126303937],
[453, 308, 211, 429, 472],
[ 5.889013732612275e-08,
0.0005066654664776551,
4.567816673280219e-07,
2.4226314239685797e-06,
4.069482619394397e-07]]
df = DataFrame(_data2, columns=_fields )
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\portabel\Python27\lib\site-packages\pandas\core\frame.py", line 208, in __init__
copy=copy)
File "C:\portabel\Python27\lib\site-packages\pandas\core\frame.py", line 273, in _init_ndarray
block = make_block(values.T, columns, columns)
File "C:\portabel\Python27\lib\site-packages\pandas\core\internals.py", line 211, in make_block
do_integrity_check=do_integrity_check)
File "C:\portabel\Python27\lib\site-packages\pandas\core\internals.py", line 26, in __init__
assert(len(items) == len(values))
AssertionError
It would be nice if pandas could output a more descriptive error message in case some inputs are not what pandas expects.
Thanks a lot for the lib!
Jan
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