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```
from pandas import DataFrame
import matplotlib.pyplot as plt
df = DataFrame({'a': [0, 1], 'b': [1, 0]})
fig = plt.figure()
ax = fig.add_subplot(111)
df.plot(ax=ax, kind='bar')
plt.show()
UnboundLocalError: local variable 'axes' referenced before assignment
```
If the 'ax' parameter is removed, no problem. Line plots with a custom 'ax' work too.
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] | closed | false | null | [] | null | 4 | "2012-01-27T20:20:18Z" | "2012-02-08T23:21:07Z" | "2012-02-08T23:21:07Z" | NONE | null | I'm using pandas version '0.7.0rc1' with Python 2.7.2 on Windows XP.
When writing HDF files, pandas tries to convert datetime index values to an ordinal using time.mktime. mktime does not support any dates before 1969.
error output shows the offending line to be:
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 697, in _convert_index
> converted = np.array([time.mktime(v.timetuple()) for v in values],
I suggest datetime.toordinal() instead of time.mktime(). However, doing so break existing HDF files.
test program to demonstrate the problem
``` python
import pandas
import numpy
newIndex = pandas.DateRange('1/1/2000', periods=8)
newDF = pandas.DataFrame(numpy.random.randn(8, 3), index=newIndex,
columns=['A', 'B', 'C'])
oldIndex = pandas.DateRange('1/1/1950', periods=8)
oldDF = pandas.DataFrame(numpy.random.randn(8, 3), index=oldIndex,
columns=['A', 'B', 'C'])
store = pandas.io.pytables.HDFStore('store.h5')
print '--- writing new ---'
store['newDF'] = newDF
print '--- writing old ---'
store['oldDF'] = oldDF
print 'done'
```
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In [1]: from pandas import DataFrame
In [2]: df = DataFrame({u'c/\u03c3':[1,2,3]})
In [3]: df.to_csv('test')
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
.../<ipython-input-3-9b2e5ea53beb> in <module>()
----> 1 df.to_csv('test')
.../lib/python2.7/site-packages/pandas-0.7.0.dev_88fcac5-py2.7-macosx-10.4-x86_64.egg/pandas/core/frame.pyc in to_csv(self, path, sep, na_rep, cols, header, index, index_label, mode, nanRep)
891 # given a string for a DF with Index
892 index_label = [index_label]
--> 893 csvout.writerow(list(index_label) + list(cols))
894 else:
895 csvout.writerow(cols)
UnicodeEncodeError: 'ascii' codec can't encode character u'\u03c3' in position 2: ordinal not in range(128)
```
I think this should be separate from [#680](https://github.com/wesm/pandas/issues/680). The CSV issue is also mentioned in this [comment on bug #300](https://github.com/wesm/pandas/issues/300#issuecomment-2735592).
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https://api.github.com/repos/pandas-dev/pandas/issues/707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/707/events | https://github.com/pandas-dev/pandas/issues/707 | 3,001,490 | MDU6SXNzdWUzMDAxNDkw | 707 | DataFrame.ix[n] for numerical (float) indices has an error | {
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} | 5 | "2012-01-27T23:10:52Z" | "2016-10-12T23:02:47Z" | "2012-01-29T20:29:56Z" | NONE | null | So my understanding for a DataFrame with numerical indices, DataFrame.ix[i] indexes to the position where index=i, and not the ith row of the DataFrame. Although I find this to be confusing, i accept that this was the decision that was made.
However, the getting and setting with .ix is not consistent
from pandas import DataFrame
df = DataFrame(['a','b','c'],index=[0,0.5,1])
df.ix[1] = 'zoo'
print df.ix[1]
Current:
'b'
Expected:
'zoo'
Basically, df.ix[1] returns 'b', of index 0.5, instead of 'zoo' of index 1
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https://api.github.com/repos/pandas-dev/pandas/issues/708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/708/events | https://github.com/pandas-dev/pandas/issues/708 | 3,007,287 | MDU6SXNzdWUzMDA3Mjg3 | 708 | concat DataFrames bug with keys on axis=1 | {
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} | 0 | "2012-01-28T20:49:43Z" | "2012-01-29T23:43:02Z" | "2012-01-29T23:43:02Z" | MEMBER | null | ```
In [11]: t1
Out[11]:
value
id
a 1
b 2
c 3
In [12]: t2
Out[12]:
value
id
a 7
b 8
In [13]: concat([t1, t2], axis=1, keys=['t1', 't2'])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-13-8c00deb927ee> in <module>()
----> 1 concat([t1, t2], axis=1, keys=['t1', 't2'])
/home/wesm/code/pandas/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)
649 keys=keys, levels=levels, names=names,
650 verify_integrity=verify_integrity)
--> 651 return op.get_result()
652
653
/home/wesm/code/pandas/pandas/tools/merge.pyc in get_result(self)
715 return Series(new_data, index=self.new_axes[0], name=name)
716 else:
--> 717 new_data = self._get_concatenated_data()
718 return self.objs[0]._from_axes(new_data, self.new_axes)
719
/home/wesm/code/pandas/pandas/tools/merge.pyc in _get_concatenated_data(self)
735 stacked_block = self._concat_blocks(klass_blocks)
736 new_blocks.append(stacked_block)
--> 737 new_data = BlockManager(new_blocks, self.new_axes)
738 except Exception: # EAFP
739 # should not be possible to fail here for the expected reason with
/home/wesm/code/pandas/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check)
283
284 if do_integrity_check:
--> 285 self._verify_integrity()
286
287 def __nonzero__(self):
/home/wesm/code/pandas/pandas/core/internals.pyc in _verify_integrity(self)
362
363 def _verify_integrity(self):
--> 364 _union_block_items(self.blocks)
365 mgr_shape = self.shape
366 for block in self.blocks:
/home/wesm/code/pandas/pandas/core/internals.pyc in _union_block_items(blocks)
1071
1072 if tot_len > len(the_union):
-> 1073 raise Exception('item names overlap')
1074 return the_union
1075
Exception: item names overlap
```
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} | 0 | "2012-01-28T20:54:38Z" | "2012-01-30T18:14:20Z" | "2012-01-30T18:14:20Z" | MEMBER | null | This should work IMHO
```
In [20]: df
Out[20]:
value
id
t1 a 1
b 2
c 3
t2 a 7
b 8
In [21]: df.ix[:, 'value']
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-21-7d089958dc90> in <module>()
----> 1 df.ix[:, 'value']
/home/wesm/code/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
28 pass
29
---> 30 return self._getitem_tuple(key)
31 else:
32 return self._getitem_axis(key, axis=0)
/home/wesm/code/pandas/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
97 def _getitem_tuple(self, tup):
98 try:
---> 99 return self._getitem_lowerdim(tup)
100 except IndexingError:
101 pass
/home/wesm/code/pandas/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
126 pass
127 except Exception:
--> 128 if tup[0] not in ax0:
129 raise
130
/home/wesm/code/pandas/pandas/core/index.pyc in __contains__(self, key)
209
210 def __contains__(self, key):
--> 211 return key in self._engine
212
213 def __hash__(self):
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.DictIndexEngine.__contains__ (pandas/src/engines.c:1958)()
TypeError: unhashable type
```
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https://api.github.com/repos/pandas-dev/pandas/issues/710 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/710/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/710/comments | https://api.github.com/repos/pandas-dev/pandas/issues/710/events | https://github.com/pandas-dev/pandas/pull/710 | 3,007,894 | MDExOlB1bGxSZXF1ZXN0NzUwNjg3 | 710 | Py3 unicode fixes 2 | {
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} | [] | closed | false | null | [] | null | 1 | "2012-01-28T21:55:52Z" | "2012-01-29T02:33:38Z" | "2012-01-29T02:33:38Z" | CONTRIBUTOR | null | The code to let Python 2 use Unicode for CSV had broken reading CSV for Python 3. This simply makes it use the standard `csv.reader` in Python 3, which is unicode aware.
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} | 3 | "2012-01-29T13:47:52Z" | "2012-02-01T19:51:06Z" | "2012-02-01T19:30:57Z" | CONTRIBUTOR | null | This is a prototype for functions to work with series of strings. There's a lot more to be done if we like the approach, but I wanted to check in before I spent too much time on it.
You can easily map a function onto a series:
```
In [4]: s = Series(["hi","hoot", "far"])
In [5]: strings.startswith(s, "h")
Out[5]:
0 True
1 True
2 False
```
You can also use other series to give arguments; they're not reindexed to the main series yet, but this is NaN aware:
```
In [6]: s = Series(["hi","hoot", "far", np.nan])
In [7]: strings.search_re(s, "h(.*)", groups=Series([0,1,1,0]))
Out[7]:
0 hi
1 oot
2 NaN
3 NaN
```
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} | 2 | "2012-01-30T08:34:50Z" | "2013-12-04T01:20:15Z" | "2012-01-30T18:54:44Z" | NONE | null | Example:
```
df = DataFrame({}, columns=['a', 'b'])
df.pivot_table(rows='a')
```
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} | 4 | "2012-01-30T10:23:55Z" | "2014-05-16T12:03:32Z" | "2012-02-01T16:08:51Z" | NONE | null | IMHO the result should be integer type:
```
df = DataFrame({
'x':('a', 'b'),
'y':('A', 'B'),
'i':(1, 2),
})
pivot_table(df, rows='x', cols='y')
y A B
x
a 1.0 NaN
b NaN 2.0
```
Love pandas!
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} | 5 | "2012-01-30T11:08:22Z" | "2012-02-04T22:40:26Z" | "2012-02-04T22:40:26Z" | NONE | null | Series.unique() dies with many NaNs:
```
import time
def test_unique(obj):
for n in range(6):
objs = Series([obj] * 10 ** n)
start = time.time()
objs.unique()
stop = time.time()
print('%6.0f %s' % (len(objs), stop - start))
test_unique('a')
1 4.98294830322e-05
10 2.40802764893e-05
100 4.10079956055e-05
1000 6.91413879395e-05
10000 0.000164985656738
100000 0.0013279914856
test_unique(float('nan'))
1 3.91006469727e-05
10 3.60012054443e-05
100 0.000331163406372
1000 0.0283088684082
10000 2.71325206757
100000 Boom!
```
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} | 5 | "2012-01-31T08:35:47Z" | "2012-01-31T20:49:47Z" | "2012-01-31T20:49:47Z" | NONE | null | In certain circumstances I found the following exception was raised when calling series.mean():
I'm running pandas 0.6.0. (I also checked the latest build on GitHub just in case!)
File "C:/Work/PyCharm/EPD-7.2.1/python/bin/heatmap.py", line 166, in zscore
logger.debug('b:{}'.format(series.mean()))
File "C:\Python27\lib\site-packages\pandas\core\series.py", line 650, in mean
return nanops.nanmean(self.values, skipna=skipna)
File "C:\Python27\lib\site-packages\pandas\core\nanops.py", line 30, in nanmean
the_mean = values.sum(axis) / float(values.shape[axis])
TypeError: tuple indices must be integers, not NoneType
The axis parameter is not being handed to nanops.nanmean(), where it is used for indexing.
So I changed (series.py line 650):
return nanops.nanmean(self.values, skipna=skipna)
to:
return nanops.nanmean(self.values, skipna=skipna, axis=axis)
to fix my code.
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} | 0 | "2012-01-31T17:28:23Z" | "2012-01-31T19:29:22Z" | "2012-01-31T21:54:04Z" | CONTRIBUTOR | null | For review
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} | 2 | "2012-01-31T20:37:46Z" | "2013-06-04T16:43:48Z" | "2013-06-04T16:43:48Z" | MEMBER | null | Probably just want to make numexpr a submodule of pandas. It's a fairly tidy piece of code and would not be too onerous to simply make part of the build.
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``` python
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.7.0.dev-e3df4e2'
In [3]: df = pandas.DataFrame({'A': [746.03, 0.00, 5620.00, 1592.36]})
In [4]: df
Out[4]:
A
0 746.
1 0.00
2 5620
3 1592
In [5]:
```
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} | 0 | "2012-02-01T15:06:32Z" | "2014-07-05T02:40:53Z" | "2012-02-01T17:36:53Z" | CONTRIBUTOR | null | Just wanted to clarify that sort takes a more than one column. Had to look at the source to be sure.
There's also an accidental end of line whitespace fix from my editor.
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} | 0 | "2012-02-01T16:39:19Z" | "2012-02-01T18:51:11Z" | "2012-02-01T18:51:11Z" | MEMBER | null | should sort these out prior to release. all look very straightforward
```
python setup.py build_ext --inplace
running build_ext
cythoning pandas/src/tseries.pyx to pandas/src/tseries.c
warning: /home/wmckinney/pandas/pandas/src/moments.pyx:486:53: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/moments.pyx:488:54: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:21:45: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:56:71: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:116:57: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:148:45: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:198:44: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:238:52: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:269:52: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:321:46: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:337:50: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:378:54: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:425:46: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:441:50: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:453:50: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:520:54: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:553:54: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:576:54: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:622:60: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:669:57: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:685:61: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:736:65: local variable 'ret' referenced before assignment
warning: /home/wmckinney/pandas/pandas/src/hashtable.pyx:768:65: local variable 'ret' referenced before assignment
building 'pandas._tseries' extension
gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/py27/lib/python2.7/site-packages/numpy/core/include -I/opt/python27/include/python2.7 -c pandas/src/tseries.c -o build/temp.linux-x86_64-2.7/pandas/src/tseries.o
In file included from pandas/src/tseries.c:243:
pandas/src/skiplist.h:61: warning: function declaration isn't a prototype
pandas/src/skiplist.h: In function 'skiplist_insert':
pandas/src/skiplist.h:189: warning: unused variable 'tmp'
In file included from pandas/src/tseries.c:244:
pandas/src/khash.h: At top level:
pandas/src/khash.h:589: warning: function declaration isn't a prototype
pandas/src/khash.h:594: warning: function declaration isn't a prototype
pandas/src/khash.h:603: warning: function declaration isn't a prototype
pandas/src/khash.h:605: warning: function declaration isn't a prototype
pandas/src/khash.h:606: warning: function declaration isn't a prototype
pandas/src/khash.h:607: warning: function declaration isn't a prototype
/opt/py27/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:227: warning: '_import_umath' defined but not used
pandas/src/tseries.c: In function '__pyx_pf_6pandas_8_tseries_14Int64HashTable_4get_iter_test':
pandas/src/tseries.c:69783: warning: '__pyx_v_val' may be used uninitialized in this function
pandas/src/tseries.c: In function '__pyx_pf_6pandas_8_tseries_14Int32HashTable_4get_iter_test':
pandas/src/tseries.c:68338: warning: '__pyx_v_val' may be used uninitialized in this function
```
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https://api.github.com/repos/pandas-dev/pandas/issues/730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/730/events | https://github.com/pandas-dev/pandas/issues/730 | 3,057,035 | MDU6SXNzdWUzMDU3MDM1 | 730 | SparseSeries.dropna() returns "NaN"s for values | {
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} | 6 | "2012-02-01T19:14:42Z" | "2012-02-05T15:44:28Z" | "2012-02-05T04:07:11Z" | NONE | null | A SparseSeries object I have called ".dropna()" on will output the indexes which are **not** NaN (which is correct), but shows "NaN" in the value column.
If I run SparseSeries.to_dense().dropna() I get the expected output ie. non-NaN indexes and their values.
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} | 0 | "2012-02-02T00:21:27Z" | "2012-02-05T20:50:51Z" | "2012-02-05T20:50:51Z" | MEMBER | null | Merge keys are "added back" post facto. This is problematic both from a performance perspective and also causes the keys to be in the wrong order in the joined result (the join keys are inserted in the beginning rather than in their natural places). Consolidation of the DataFrame is being improperly deferred until after the join result is returned.
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} | 4 | "2012-02-02T13:59:56Z" | "2012-02-06T09:14:28Z" | "2012-02-05T00:53:57Z" | NONE | null | Just a tiny suggestion:
The keyword argument for specifying which columns to consider in drop_duplicates is called col_or_columns. Maybe change the name to col or to columns? Or the strangely long name is intended to make it clear that you can either pass in a label or a sequence of labels?
Thanks,
Marius
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} | 17 | "2012-02-02T15:57:03Z" | "2014-06-27T12:30:51Z" | "2012-02-08T20:28:12Z" | CONTRIBUTOR | null | I added a ExcelWriter class to io/parsers.py
Added a to_excel method to DataFrame and refactored to_csv to use a helper method that also serves to_excel.
I still need to add some tests, but wanted to get feedback before spending any more time.
There are still some issues, like xlwt throws exceptions when writing np.int64 types, but we should be able to special case some of that. I also didn't add any encoding handling, although that should be reasonably easy.
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} | 44 | "2012-02-02T17:29:40Z" | "2016-07-20T21:12:40Z" | "2016-07-20T21:11:50Z" | MEMBER | null | The name is a misnomer. It's really a pivot operation
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} | 0 | "2012-02-02T17:35:27Z" | "2012-02-05T21:07:57Z" | "2012-02-05T21:07:57Z" | MEMBER | null | ```
>>> y = [Decimal('1.3'), Decimal('2.3')]
>>> y2 = [datetime.date(2012,1,1), datetime.date(2012,1,2)]
>>> y3 = Series(y, index=y2)
In [21]: y3 + y3.shift(1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-21-9b098b74978c> in <module>()
----> 1 y3 + y3.shift(1)
/home/wesm/code/pandas/pandas/core/series.py in wrapper(self, other)
52 if self.index.equals(other.index):
53 name = _maybe_match_name(self, other)
---> 54 return Series(op(self.values, other.values), index=self.index,
55 name=name)
56
TypeError: unsupported operand type(s) for +: 'Decimal' and 'float'
In [17]: s = Series(['foo', 'bar', 'baz', np.nan])
In [18]: s
Out[18]:
0 foo
1 bar
2 baz
3 NaN
In [19]: 'prefix_' + s
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-19-64e88cc97e2c> in <module>()
----> 1 'prefix_' + s
/home/wesm/code/pandas/pandas/core/series.py in wrapper(self, other)
65 else:
66 # scalars
---> 67 return Series(op(self.values, other), index=self.index,
68 name=self.name)
69 return wrapper
/home/wesm/code/pandas/pandas/core/series.py in _radd_compat(left, right)
73 # GH #353, NumPy 1.5.1 workaround
74 try:
---> 75 output = radd(left, right)
76 except TypeError:
77 cond = (_np_version_under1p6 and
/home/wesm/code/pandas/pandas/core/series.py in <lambda>(x, y)
70
71 def _radd_compat(left, right):
---> 72 radd = lambda x, y: y + x
73 # GH #353, NumPy 1.5.1 workaround
74 try:
TypeError: cannot concatenate 'str' and 'float' objects
```
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} | 0 | "2012-02-02T18:58:56Z" | "2012-02-05T22:52:55Z" | "2012-02-05T22:52:54Z" | CONTRIBUTOR | null | #
## ERROR: test_to_csv_unicode (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "c:\python32\lib\site-packages\pandas-0.7.0.dev-py3.2-win-amd64.egg\panda
s\tests\test_frame.py", line 2488, in test_to_csv_unicode
df.to_csv(path, encoding='UTF-8')
File "c:\python32\lib\site-packages\pandas-0.7.0.dev-py3.2-win-amd64.egg\panda
s\core\frame.py", line 914, in to_csv
csvout.writerow(encoded_labels + encoded_cols)
File "c:\python32\lib\encodings\cp1252.py", line 19, in encode
return codecs.charmap_encode(input,self.errors,encoding_table)[0]
UnicodeEncodeError: 'charmap' codec can't encode character '\u03c3' in position
8: character maps to <undefined>
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But error is generated when Index holds e.g strings, or when MultiIndex is used.
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} | 0 | "2012-02-03T22:42:02Z" | "2016-10-12T23:02:47Z" | "2012-02-05T23:44:13Z" | NONE | null | from pandas import DataFrame
df = DataFrame([['b','c','a'],['a','c','b']])
ranked = df..rank(1)
Currently:
ranked = Empty DataFrame
Desired
ranked = a ranked DataFrame (by row) as in the case of DataFrame.rank(1) of dtype = float/int/etc
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} | 20 | "2012-02-04T09:25:29Z" | "2016-11-02T22:12:59Z" | "2012-02-04T21:37:43Z" | CONTRIBUTOR | null | import datetime
import numpy
import pandas
def simple_test(use_date=True):
data = [
[1, '2012-01-01', 1.0],
[2, '2012-01-02', 2.0],
[3, None, 3.0]
]
if use_date:
data = [[row[0], datetime.datetime.strptime(row[1], '%Y-%m-
%d').date() if row[1] else None, row[2]] for row in data]
df = pandas.DataFrame({'key': [x[0] for x in data], 'date': [x[1]
for x in data], 'value': [x[2] for x in data]})
df['weights'] = df['value']/df['value'].sum()
gb = df.groupby('date').aggregate(numpy.sum)
```
print 'II) Using date objects' if use_date else 'I) Using date
```
strings'
print df
print ''
print gb
if **name** == '**main**':
simple_test(use_date=False)
print '*' \* 40
simple_test(use_date=True)
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} | 2 | "2012-02-04T17:35:51Z" | "2016-03-13T21:30:05Z" | "2016-03-13T21:26:33Z" | NONE | null | Following up on Wes's email from the pandas group: I was having a problem trying to use converters in "read_table". I have a file that looks like this:
```
id value
a a=0,b=4,c=6
b a=10,b=5,c=-1
c a=20,c=60
d a=430
e c=200
```
The "id" field is unique and so can be used as an index with "read_table":
```
In [7]: df = p.read_table("test.csv", index_col=[0])
In [8]: df
Out[8]:
value
id
a a=0,b=4,c=6
b a=10,b=5,c=-1
c a=20,c=60
d a=430
e c=200
```
The value field is constrained to have at most three values (for "a", "b" and "c"). I'd like to store that as a numpy array with canonical ordering where you have the value for a, b, c for each "id" entry, making each 0 if it is not present. My converter is:
```
import numpy as np
def value_convert(value, fields=["a","b","c"]):
pairs = value.split(",")
from collections import defaultdict
d = defaultdict(int)
for pair in pairs:
k, v = pair.split("=")
d[k] = v
results = np.array([d[f] for f in fields],
dtype='int64')
return results
```
Example:
```
In [32]: value_convert(df["value"][0])
Out[32]: array([0, 4, 6])
```
If I try to use that as a converter:
```
df = p.read_table("test.csv", index_col=[0], converters={"value": value_convert})
```
I get the error:
```
/usr/local/lib/python2.6/dist-packages/numpy-1.5.1-py2.6-linux-x86_64.egg/numpy/lib/function_base.pyc in __call__(self, *args)
1831 if self.nout == 1:
1832 _res = array(self.ufunc(*newargs),copy=False,
-> 1833 subok=True,dtype=self.otypes[0])
1834 else:
1835 _res = tuple([array(x,copy=False,subok=True,dtype=c) \
ValueError: setting an array element with a sequence.
```
I can get around this using apply:
```
In [37]: df["value"].apply(value_convert)
Out[37]:
id
a [0 4 6]
b [10 5 -1]
c [20 0 60]
d [430 0 0]
e [ 0 0 200]
Name: value
In [38]: df["int_value"] = df["value"].apply(value_convert)
In [39]: df["int_value"]
Out[39]:
id
a [0 4 6]
b [10 5 -1]
c [20 0 60]
d [430 0 0]
e [ 0 0 200]
Name: int_value
```
It would be convenient to access "int_value" as an array, e.g. pull out all the "a" values for all entries in the DataFrame, but this yeilds an error:
```
In [40]: df["int_value"][:, 0]
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (772, 0))
....
ValueError: Can only tuple-index with a MultiIndex
```
Though that might be a separate issue from the converters. Using "apply" works, but the idea of a converters is a very nice feature that I would in general like to use, since I run into this use case a lot. More important for me personally however is being able to slice these non-scalar values.
Thanks very much! best, Yarden
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} | 2 | "2012-02-04T21:22:14Z" | "2012-05-07T16:36:59Z" | "2012-05-07T16:36:59Z" | MEMBER | null | from mailing list "[pystatsmodels] read_csv + text file with one column"
```
Yes. Suppose that function f returns a boolean value given a column
value. To filter you would do:
df[df[column_to_filter_by].map(f)]
oftentimes filtering is using NumPy vector operations like
df[df[column] == val]
but sometimes you have an element-wise Python function you want to apply.
You said you are a new Python programmer so I can understand lambdas
and regular expressions looking weird =) Lambda is just an alternative
to doing something like:
def condition(x):
return x.startswith('A')
df[df[column].map(condition)]
you could also do:
df[[condition(x) for x in df[column]]]
the map method is just a faster way of doing the list comprehension
(and returns an ndarray with the right data type)
The select method applies a function to the *axis labels*, directly
from the docstring:
In [1]: DataFrame.select?
Type: instancemethod
Base Class: <type 'instancemethod'>
String Form:<unbound method DataFrame.select>
Namespace: Interactive
File: /home/wesm/code/pandas/pandas/core/generic.py
Definition: DataFrame.select(self, crit, axis=0)
Docstring:
Return data corresponding to axis labels matching criteria
Parameters
----------
crit : function
To be called on each index (label). Should return True or False
axis : int
Returns
-------
selection : type of caller
hope this helps,
Wes
```
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```
dtypes: float64(18), object(1), object(6)
```
In reality first block is `<O8` (unpickled pickle from 32-bit platform) and second block is `|O8` (originated from 64-bit platform)
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} | 2 | "2012-02-05T19:44:17Z" | "2012-02-05T21:25:25Z" | "2012-02-05T21:25:25Z" | MEMBER | null | ```
import StringIO
import numpy as np
import pandas
csv = """\
id,score,days
1,2,12
2,2-5,
3,,14+
4,6-12,2
"""
def convert_days(x):
x = x.strip()
if not x: return np.nan
is_plus = x.endswith('+')
if is_plus:
x = int(x[:-1]) + 1
else:
x = int(x)
return x
def convert_score(x):
x = x.strip()
if not x: return np.nan
if x.find('-')>0:
valmin, valmax = map(int, x.split('-'))
val = 0.5*(valmin + valmax)
else:
val = float(x)
return val
fh = StringIO.StringIO(csv)
p = pandas.read_csv(fh, converters={'score':convert_score,
'days':convert_days}, na_values=[-1,'',None])
print p
```
cc @jdh2358
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``` python
try:
import bottleneck as bn
_USE_BOTTLENECK = True
except ImportError: # pragma: no cover
_USE_BOTTLENECK = False
def _bottleneck_switch(bn_name, alt, **kwargs):
try:
bn_func = getattr(bn, bn_name)
```
`bn` is only defined if bottleneck is imported and the `try` statement doesn't catch the NameError that ensues (only AttributeError).
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} | 4 | "2012-02-07T01:20:16Z" | "2012-02-08T23:06:56Z" | "2012-02-08T22:51:48Z" | NONE | null | Hi,
The following:
df = DataFrame(randn(10, 4), columns=['A', 'B', 'C', 'D'])
print df #df.to_string fails also
crashes my system, no error given, simply asked to shut down application.
However:
print df.values
does not. Any help apprecitated.
running:
pandas-0.7.0rc1.win32-py2.6
ActivePython-2.6.7.20-win32-x86
numpy-1.6.1-win32-superpack-python2.6
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} | 2 | "2012-02-07T02:09:23Z" | "2014-07-24T05:29:24Z" | "2012-02-07T23:52:31Z" | CONTRIBUTOR | null | Minor change to allow passing subclasses of dict into DataFrame constructor. I assume there is no way to pass these directly to cython, we have to make a copy first?
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} | 4 | "2012-02-07T09:10:33Z" | "2012-02-08T01:30:48Z" | "2012-02-07T18:41:45Z" | NONE | null | I had a trouble with Series and TimeSeries using datetime index. Here is an example:
In [1]: from pandas import *
In [2]: t = [datetime(2012, 2, 7, 0, 0, 0), datetime(2012, 2, 7, 23, 0, 0)]
In [3]: s = Series([0, 1], index=t)
In [4]: print s
2012-02-07 0
2012-02-07 00:00:00 1
The first index value should be '2012-02-07 00:00:00', and the second should be '2012-02-07 23:00:00' Am I doing wrong?
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} | 0 | "2012-02-07T16:18:27Z" | "2012-02-07T21:38:53Z" | "2012-02-07T21:38:53Z" | MEMBER | null | Some casts to float are needed
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} | 1 | "2012-02-07T16:37:15Z" | "2014-03-09T15:19:59Z" | "2014-03-09T15:19:59Z" | MEMBER | null | from @yarden on the mailing list
```
Hi,
I have a very common use case which pandas which involve a JOIN
operation related to coordinate arithmetic on two dataframes,
with queries like: for each entry in dataframe A, return all the
entries from dataframe B that have coordinates overlapping with
this entry. This join is not just matching on the columns values
of A and B, since the column values here are coordinates that
require simple arithmetic operations like intersection, overlap,
etc.
Following Wes's answer to someone else's question
here (http://stackoverflow.com/questions/7837722/), I implemented
this in a naive way using iteration. My solution is extremely
inefficient however, probably in part since it is non-vectorized
and requires iterating through each element of the DataFrame.
Do you know of a more efficient/elegant way to do this? The
naive implementation is unfortunately not workable for anything
even moderately large (e.g. dataframes with a couple thousand
entries.)
Complete example below. The first dataframe, df1, is made up of
two datasources, and each entry has a coordinate
column "loc1". The second dataframe df2 has an interval (start
and end coordinate) for each position. The "join" operation
function my_join() simply returns all the entry numbers in df2
that have intervals that contain the loc1 column from df1. The
join is not unique, so entries in df1 can be contained in
multiple entries in df2 (in which case they are stored as
comma-separated strings), or they can be contained in none of the
entries in df2, in which case the function stores the value
as "-1".
I'd like this to scale to instances where df1 and df2 contain
~50k entries. What is the best way to do this?
Thanks very much.
import pandas as p
import scipy
import numpy
# DataFrames for each data source
source1_data = np.array([0, 7, 2, 5, 0, 5, 4, 3, 9, 0, 4, 2, 5, 8, 0, 4, 0, 3, 3, 6])
source1_size = len(source1_data)
source1 = p.DataFrame({"id": range(source1_size),
"loc1": source1_data})
source1.set_index(["id"])
source2_data = np.array([8, 4, 1, 1, 9, 3, 7, 2, 8, 7])
source2_size = len(source2_data)
source2 = p.DataFrame({"id": range(source2_size),
"loc1": source2_data})
source2.set_index(["id"])
dataframe_dict = {"source1": source1,
"source2": source2}
# Concatenated into one DataFrame
df1 = p.concat(dataframe_dict, keys=["source1", "source2"])
df2_size = 6
# dataframe with intervals
intervals = np.array([[ 5, 6],
[ 3, 6],
[ 6, 13],
[ 7, 10],
[ 8, 9],
[ 9, 12]])
df2 = p.DataFrame({"entry": range(df2_size),
"coord1": intervals[:, 0],
"coord2": intervals[:, 1]})
# JOIN df1 and df2 based on interval containment
def my_join(first_df, second_df):
all_hits = []
for entry, value in first_df.T.iteritems():
# Look for cases where loc1 field of df1 falls within
# (coord1, coord2)
coord = value["loc1"]
hits = second_df.ix[(coord > second_df["coord1"]) & \
(coord < second_df["coord2"])]
num_hits = len(hits)
if num_hits == 0:
hits = [-1]
else:
hits = hits["entry"].values
# If there are any hits, record their entries in a
# column of df1. When none, record them as -1
print "%d falls within %d hits: " %(coord, num_hits)
if num_hits > 0: print hits
# Must convert hits to string to store these
# non-scalars in first_df later on
hits_str = ",".join(map(str, hits))
all_hits.append(hits_str)
# Store hits in first_df
first_df["hits"] = all_hits
print "Revised first_df: "
print first_df
my_join(df1, df2)
```
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} | 6 | "2012-02-08T09:22:29Z" | "2014-06-03T01:18:06Z" | "2014-06-03T01:18:06Z" | CONTRIBUTOR | null | Pandas installs its tests now with executable flag. Result is that nosetests skips them and no tests are find.
Running nosetests in the repository itself works fine.
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} | [] | closed | false | null | [] | null | 0 | "2012-02-08T11:09:44Z" | "2012-02-08T15:34:40Z" | "2012-02-08T15:34:40Z" | CONTRIBUTOR | null | This commit fixes a SyntaxError observed when installing (as in: running `python setup.py install`) on Linux (python 2.7): the original error I saw was
```
Extracting pandas-0.7.0.dev_6253b07-py2.7-linux-x86_64.egg to /usr/lib64/python2.7/site-packages
SyntaxError: ('invalid syntax', ('/usr/lib64/python2.7/site-packages/pandas-0.7.0.dev_6253b07-py2.7-linux-x86_64.egg/pandas/util/map.py', 65, 42, ' def wrapped(arr, *otherargs, n_results=None):\n'))
```
and a check showed that `*args` was put before a keyword expression in the function. Reversing the order fixed the error.
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In [116]: ra.ix[:5,:5]
Out[116]:
005665 023740 028758 040828 045449
2011-05-09 NaN NaN NaN NaN NaN
2011-05-10 -0.00284467268624 0.0 0.0 -0.002844627571 0.000353248710642
2011-05-11 -0.00674436854779 0.0 0.0 -0.00674432257973 0.0
2011-05-12 0.0117105570627 0.0 0.0 0.0117104073878 0.0
2011-05-13 0.00367090573068 0.0 0.0 -0.00102855609881 0.0
In [117]: ra.ix[:5,:5].mean(axis=0,skipna=True)
Out[117]:
005665 0.0014481
023740 0.0000000
028758 0.0000000
040828 0.0002732
045449 8.831e-05
In [118]: ra.ix[:5,:5].mean(axis=1,skipna=True)
Out[118]:
2011-05-09 NaN
2011-05-10 NaN
2011-05-11 NaN
2011-05-12 NaN
2011-05-13 NaN
In [122]: ra.ix[1:5,:5].mean(axis=1,skipna=True)
Out[122]:
2011-05-10 -0.0010672
2011-05-11 -0.0026977
2011-05-12 0.0046842
2011-05-13 0.0005285
In step 118, I would expect the same output I get at step 122. Maybe the check if values are all NaN on a given row are done on the wrong axis?
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```
In [8]: df
Out[8]:
a b c routine1 routine2 top
result1 result2 result1 OD
wx wy
0 -0.914239 -0.230830 -0.421134 0.131751 -0.902347 0.382893 0.334615 0.909842
1 -0.533285 -0.878316 -3.046224 -0.760047 0.299502 -0.268321 -0.709366 -0.878868
2 0.804353 -1.203268 -1.221442 -1.948503 0.156240 -0.439375 -0.921254 1.119102
In [9]: df2 = df.set_index([('a','','')])
In [10]: df2
Out[10]:
<class 'pandas.core.frame.DataFrame'>
Index: 3 entries, -0.914238702285 to 0.804353019197
Data columns:
('b', '', '') 3 non-null values
('c', '', '') 3 non-null values
('routine1', 'result1', '') 3 non-null values
('routine1', 'result2', '') 3 non-null values
('routine2', 'result1', '') 3 non-null values
('top', 'OD', 'wx') 3 non-null values
('top', 'OD', 'wy') 3 non-null values
dtypes: float64(7)
In [11]: df2.columns
Out[11]:
Index([('b', '', ''), ('c', '', ''), ('routine1', 'result1', ''),
('routine1', 'result2', ''), ('routine2', 'result1', ''),
('top', 'OD', 'wx'), ('top', 'OD', 'wy')], dtype=object)
```
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} | [] | closed | false | null | [] | null | 6 | "2012-02-09T01:44:59Z" | "2014-06-18T04:20:32Z" | "2012-02-09T17:26:43Z" | CONTRIBUTOR | null | Modifications to column-hierarchical behaviour as per "'Mixed depth' hierarchical column labels" discussion on pystatsmodels.
If column label tuples contain only empty strings at the lower levels, allows the DataFrame to be indexed with only the higher levels. So for example if a column has the hierarchical label ('a','',''), and there are no other columns with 'a' at their top level, then this change allows the column to be pulled out with df['a'], rather than df['a','','']. It also names the resulting series 'a'.
The use case for this is when some columns have a deeper hierarchy than others, and you want to treat the shallower columns as if the lower levels of the hierarchy weren't there.
If column label tuples contain only empty strings at the lower levels, allows the DataFrame to be indexed with only the higher levels. So for example if a column has the hierarchical label ('a','',''), and there are no other columns with 'a' at their top level, then this change allows the column to be pulled out with df['a'], rather than df['a','','']. It also names the resulting series 'a'.
The use case for this is when some columns have a deeper hierarchy than others, and you want to treat the shallower columns as if the lower levels of the hierarchy weren't there.
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- Put the ExcelWriter in the pandas namespace
- Add sheet_names property to ExcelFile
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```
DateRange("2011-1-1","2012-1-31",offset=DateOffset(months=3))
```
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} | 1 | "2012-02-09T18:46:40Z" | "2012-02-09T20:28:08Z" | "2012-02-09T20:28:07Z" | NONE | null | When using the daterange intersection function, we found that the intersection only partially works as follows:
> > > a = pandas.DateRange('11/30/2011','12/31/2011')
> > > a
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 BusinessDay>, tzinfo: None
> > > [2011-11-30 00:00:00, ..., 2011-12-30 00:00:00]
> > > length: 23
> > >
> > > b = pandas.DateRange('12/10/2011','12/20/2011')
> > > b
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 BusinessDay>, tzinfo: None
> > > [2011-12-12 00:00:00, ..., 2011-12-20 00:00:00]
> > > length: 7
> > >
> > > a.intersection(b)
> > > <class 'pandas.core.daterange.DateRange'>
> > > offset: <1 BusinessDay>, tzinfo: None
> > > [2011-12-12 00:00:00, ..., 2011-12-30 00:00:00]
> > > length: 15
The intersection function restricts the output on the left side of the range but does not restrict the output on the right side of the range. Ideally, an intersection function should produce the true overlap between the two dateranges instead of the partial (one-sided) overlap.
Thanks,
Tej
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```
$> git grep 'def.*column='
pandas/core/frame.py: def boxplot(self, column=None, by=None, ax=None, fontsize=None,
pandas/stats/misc.py:def percentileRank(frame, column=None, kind='mean'):
pandas/tools/plotting.py:def boxplot(data, column=None, by=None, ax=None, fontsize=None,
```
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} | 0 | "2012-02-12T17:17:02Z" | "2012-02-18T18:45:12Z" | "2012-02-18T18:45:12Z" | CONTRIBUTOR | null | Opposite of read_clipboard
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} | 1 | "2012-02-13T00:26:37Z" | "2012-02-13T14:54:05Z" | "2012-02-13T14:54:05Z" | NONE | null | edit: This happens on Windows 7 running IPython from cmd.
In pandas 0.6.1:
In [13]: pandas.version.version
Out[13]: '0.6.1'
In [14]: pandas.DataFrame(['\xc2'])
Out[14]:
0
0 ┬
In pandas 0.7.0:
In [2]: pandas.version.version
Out[2]: '0.7.0'
In [3]: pandas.DataFrame(['\xc2'])
.....
C:\Python26\lib\site-packages\pandas\core\common.pyc in _stringify(col)
503 def _stringify(col):
504 # unicode workaround
--> 505 return unicode(col)
506
507 def _maybe_make_list(obj):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 0: ordinal not in range(128)
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} | 1 | "2012-02-13T19:45:28Z" | "2012-02-18T00:03:15Z" | "2012-02-18T00:03:15Z" | MEMBER | null | from mailing list. this is caused by the change in handling of None in `PyObjectHashTable`
```
import datetime
import numpy
import pandas
def simple_test2():
data = [
[1, 'string1', 1.0],
[2, 'string2', 2.0],
[3, None, 3.0]
]
df = pandas.DataFrame({'key': [x[0] for x in data], 'grouper':
[x[1] for x in data], 'value': [x[2] for x in data]})
df['weights'] = df['value']/df['value'].sum()
gb = df.groupby('grouper').aggregate(numpy.sum)
print
print df
print ''
print gb
if __name__ == '__main__':
simple_test2()
0.7rc1 yields:
grouper key value weights
0 string1 1 1 0.1667
1 string2 2 2 0.3333
2 NaN 3 3 0.5000
key value weights
grouper
None 3 3 0.5000
string1 1 1 0.1667
string2 2 2 0.3333
0.7 final yields:
grouper key value weights
0 string1 1 1 0.166667
1 string2 2 2 0.333333
2 None 3 3 0.500000
key value weights
grouper
string1 1 1 0.166667
string2 2 2 0.333333
missing the last line
```
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} | 1 | "2012-02-13T20:32:37Z" | "2012-02-18T00:17:19Z" | "2012-02-18T00:17:19Z" | MEMBER | null | Completely my fault when I refactored the to_string code recently
```
ticker date pnl mv
0 aapl 01/01/12 100 1000
1 aapl 01/02/12 -50 950
2 aapl 01/03/12 200 1150
3 goog 01/01/12 100 2000
4 goog 01/02/12 100 2100
5 goog 01/03/12 -50 2050
6 goog 01/04/12 -50 200
```
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} | 2 | "2012-02-14T07:23:50Z" | "2012-02-18T00:50:47Z" | "2012-02-18T00:50:47Z" | NONE | null | In [1]: import pandas
In [2]: index = [52195.504153, 52196.303147, 52198.369883]
In [3]: a = pandas.DataFrame(randn(3, 2), index)
In [4]: a
Out[4]:
0 1
52195.504153 1.367681 0.243237
52196.303147 -0.745796 -1.054106
52198.369883 -1.462461 -0.683286
In [5]: a.ix[52195.:52196.]
Out[5]:
Empty DataFrame
Columns: array([0, 1])
Index: array([], dtype=object)
In [6]: a.ix[52195.1:52196.5]
Out[6]:
Empty DataFrame
Columns: array([0, 1])
Index: array([], dtype=object)
In [7]: a.ix[52195.1:52196.6]
Out[7]:
0 1
52195.504153 1.367681 0.243237
52196.303147 -0.745796 -1.054106
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} | 0 | "2012-02-15T16:06:14Z" | "2012-02-18T01:11:36Z" | "2012-02-18T01:11:36Z" | CONTRIBUTOR | null | This works as expected:
```
df = DataFrame(index=[0], columns=['A', 'B', 'C'])
df3 = DataFrame(index=[1, 2], columns=['A', 'B'])
df5 = df.append(df3)
expected = DataFrame({'A': [nan, nan, nan],
'B': [nan, nan, nan],
'C': [nan, nan, nan]})
```
This doesn't:
```
df = DataFrame(columns=['A', 'B', 'C'])
df3 = DataFrame(index=[0, 1], columns=['A', 'B'])
df5 = df.append(df3)
expected = DataFrame({'A': [nan, nan],
'B': [nan, nan],
'C': [nan, nan]})
```
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} | 24 | "2012-02-15T20:05:10Z" | "2016-10-12T18:18:42Z" | "2016-09-29T22:12:12Z" | MEMBER | null | Not sure if possible to do in a generic way. But maybe will make sense once we have an OHLC aggregator implemented
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```
df = DataFrame(index=np.arange(100))
for i in range(5000):
df[i] = 5
```
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} | 7 | "2012-02-17T04:48:12Z" | "2012-03-15T19:30:54Z" | "2012-03-15T19:30:54Z" | MEMBER | null | ```
In [9]: df = read_clipboard(header=None, sep='\s+')
In [10]: df
Out[10]: ---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
/Users/wesm/<ipython-input-10-7ed0097d7e9e> in <module>()
----> 1 df
/Users/wesm/code/repos/ipython/IPython/core/displayhook.pyc in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/Users/wesm/code/repos/ipython/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/Users/wesm/code/repos/ipython/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/Users/wesm/code/repos/ipython/IPython/core/formatters.pyc in __call__(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/Users/wesm/code/repos/ipython/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()
/Users/wesm/code/repos/ipython/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
469 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
470 # A user-provided repr.
--> 471 p.text(repr(obj))
472 return
473 p.begin_group(1, '<')
/Users/wesm/code/pandas/pandas/core/frame.pyc in __repr__(self)
458 self.info(buf=buf, verbose=self._verbose_info)
459 else:
--> 460 self.to_string(buf=buf)
461 value = buf.getvalue()
462 if max([len(l) for l in value.split('\n')]) > terminal_width:
/Users/wesm/code/pandas/pandas/core/frame.pyc in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode)
1038 index_names=index_names,
1039 header=header, index=index)
-> 1040 formatter.to_string(force_unicode=force_unicode)
1041
1042 if buf is None:
/Users/wesm/code/pandas/pandas/core/format.pyc in to_string(self, force_unicode)
193
194 if self.index:
--> 195 to_write.append(adjoin(1, str_index, *stringified))
196 else:
197 to_write.append(adjoin(1, *stringified))
/Users/wesm/code/pandas/pandas/core/common.pyc in adjoin(space, *lists)
398 toJoin = zip(*newLists)
399 for lines in toJoin:
--> 400 outLines.append(''.join(lines))
401 return '\n'.join(outLines)
402
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 1: ordinal not in range(128)
```
here are lines
```
('0 ', u' .gitignore ', u' 5 ', ' \xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2\xe2\x80\xa2')
```
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} | 3 | "2012-02-17T09:42:28Z" | "2013-03-14T10:44:33Z" | "2013-03-14T10:44:33Z" | CONTRIBUTOR | null | I thought this was fixed(see issue 411), but it appears to keep resurfacing ;)
In [27]: a=Panel(items=range(2),major_axis=range(10),minor_axis=range(5),dtype=np.float32)
In [28]: a[0].dtypes
Out[28]:
0 float64
1 float64
2 float64
3 float64
4 float64
A quick debug shows that the routine form_blocks just considers generic "float", "int" etc datatypes, so maybe it does not distinguish between float32 and float64?
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} | 0 | "2012-02-18T01:14:09Z" | "2012-02-18T01:48:41Z" | "2012-02-18T01:48:41Z" | MEMBER | null | interesting corner case raised by #780
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https://api.github.com/repos/pandas-dev/pandas/issues/799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/799/events | https://github.com/pandas-dev/pandas/issues/799 | 3,277,225 | MDU6SXNzdWUzMjc3MjI1 | 799 | read_csv delimiter auto-detection no longer works | {
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