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} | 3 | "2012-06-05T16:02:02Z" | "2012-06-12T07:45:52Z" | "2012-06-11T22:14:31Z" | CONTRIBUTOR | null | ```
import pandas
l = [['count', 'values'], ['to filter', '']]
midx = pandas.MultiIndex.from_tuples(l)
df = pandas.DataFrame([[1L, 'A']], columns=midx) #one line
print(df.groupby('to filter').groups)
#Out: {'to filter': [0L]} #was expecting 'A': [0L]
print(df.groupby([('to filter', '')]).groups)
#Out: {'to filter': [0L]} #was expecting same as above
df = pandas.DataFrame([[1L, 'A'], [2L, 'B']], columns=midx) #two lines, different group
print(df.groupby('to filter').groups)
#Out: {'A': [0L], 'B': [1L]} #fine
print(df.groupby([('to filter', '')]).groups)
#Out: {'': [1L], 'to filter': [0L]} #was expecting same as above
df = pandas.DataFrame([[1L, 'A'], [2L, 'A']], columns=midx) #two lines, same group
print(df.groupby('to filter').groups)
#Out: {'A': [0L, 1L]} #fine
print(df.groupby([('to filter', '')]).groups)
#Out: {'': [1L], 'to filter': [0L]} #was expecting same as above
```
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} | 1 | "2012-06-05T16:05:58Z" | "2012-06-06T04:02:48Z" | "2012-06-06T04:02:48Z" | CONTRIBUTOR | null | DataFrame.set_index and functions like pivot_table should automatically convert to DatetimeIndex if possible.
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https://api.github.com/repos/pandas-dev/pandas/issues/1409 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1409/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1409/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1409/events | https://github.com/pandas-dev/pandas/issues/1409 | 4,921,435 | MDU6SXNzdWU0OTIxNDM1 | 1,409 | Out of bounds checking / handling in cut, qcut | {
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```
In [5]: cut(frame.data1, [-4, -2, 0, 2, 1])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-5-dcb858d11751> in <module>()
----> 1 cut(frame.data1, [-4, -2, 0, 2, 1])
/home/wesm/code/pandas/pandas/tools/tile.pyc in cut(x, bins, right, labels, retbins, precision)
93 bins = np.asarray(bins)
94 if (np.diff(bins) < 0).any():
---> 95 raise ValueError('bins must increase monotonically.')
96
97 return _bins_to_cuts(x, bins, right=right, labels=labels,
ValueError: bins must increase monotonically.
In [6]: cut(frame.data1, [-4, -2, 0, 1])
Out[6]: ---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-6-19824849af62> in <module>()
----> 1 cut(frame.data1, [-4, -2, 0, 1])
/home/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)
/home/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):
/home/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
/home/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()
/home/wesm/code/repos/ipython/IPython/lib/pretty.pyc in pretty(self, obj)
352 if callable(obj_class._repr_pretty_):
353 return obj_class._repr_pretty_(obj, self, cycle)
--> 354 return _default_pprint(obj, self, cycle)
355 finally:
356 self.end_group()
/home/wesm/code/repos/ipython/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
472 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
473 # A user-provided repr.
--> 474 p.text(repr(obj))
475 return
476 p.begin_group(1, '<')
/home/wesm/code/pandas/pandas/core/factor.pyc in __repr__(self)
54 def __repr__(self):
55 temp = 'Factor:%s\n%s\nLevels (%d): %s'
---> 56 values = np.asarray(self)
57 return temp % ('' if self.name is None else self.name,
58 repr(values), len(self.levels), self.levels)
/home/wesm/epd/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
233
234 """
--> 235 return array(a, dtype, copy=False, order=order)
236
237 def asanyarray(a, dtype=None, order=None):
/home/wesm/code/pandas/pandas/core/factor.pyc in __array__(self, dtype)
47
48 def __array__(self, dtype=None):
---> 49 return com.take_1d(self.levels, self.labels)
50
51 def __len__(self):
/home/wesm/code/pandas/pandas/core/common.pyc in take_1d(arr, indexer, out, fill_value)
235 if out is None:
236 out = np.empty(n, dtype=arr.dtype)
--> 237 take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
238 else:
239 out = ndtake(arr, indexer, out=out)
/home/wesm/code/pandas/pandas/_algos.so in pandas._algos.take_1d_object (pandas/src/generated.c:19923)()
IndexError: Out of bounds on buffer access (axis 0)
```
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} | 0 | "2012-06-06T02:12:32Z" | "2014-06-18T10:04:29Z" | "2012-06-07T14:34:43Z" | CONTRIBUTOR | null | #1373
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https://api.github.com/repos/pandas-dev/pandas/issues/1412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1412/events | https://github.com/pandas-dev/pandas/issues/1412 | 4,926,227 | MDU6SXNzdWU0OTI2MjI3 | 1,412 | isin method buggy on SparseSeries | {
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} | 4 | "2012-06-06T10:05:35Z" | "2012-06-08T03:50:28Z" | "2012-06-08T03:50:28Z" | NONE | null | The isin method doesn't seem to work correctly for SparseSeries objects, but does work on an equivalent dense object, example code below:
``` python
In [203]: pandas.__version__
Out[203]: '0.7.3'
In [204]: sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.)
In [205]: sparse_df[sparse_df.flag == 1.]
Out[205]:
flag
0 1
2 1
In [206]: sparse_df[sparse_df.flag.isin([1.])]
Out[206]:
flag
0 1
1 0
In [207]: dense_df = DataFrame({'flag': [1., 0., 1.]})
In [208]: dense_df[dense_df.flag == 1.]
Out[208]:
flag
0 1
2 1
In [209]: dense_df[dense_df.flag.isin([1.])]
Out[209]:
flag
0 1
2 1
```
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} | 13 | "2012-06-06T11:58:19Z" | "2012-08-23T20:26:30Z" | "2012-06-07T06:44:13Z" | NONE | null | I've tried to install the dev version with `pip`, but it did not work. I had to manually download the tarball and run `python setup.py install`, which breaks a 100% pip setup of my local environment.
The following did not work:
```
pip install -e git+git://github.com/pydata/pandas.git#egg=pandas
```
It also did not work with the v0.8.0b1 version:
```
pip install -e git+git://github.com/pydata/[email protected]#egg=pandas
```
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} | 8 | "2012-06-06T12:12:47Z" | "2012-06-11T18:28:11Z" | "2012-06-11T18:27:53Z" | CONTRIBUTOR | null | I'm unsure if it's by design or not, so I'm leaving this here.
``` python
In [84]: original = pandas.Series([1,2,3, np.nan], dtype="object")
In [85]: def convert(value):
return np.nan if pandas.isnull(value) else int(value)
....:
In [86]: original.apply(convert).dtype
Out[86]: dtype('float64')
```
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} | 2 | "2012-06-06T21:21:54Z" | "2012-06-07T16:26:26Z" | "2012-06-07T15:40:13Z" | CONTRIBUTOR | null | Now that I've added a `.travis.yml` in #1382, about 2 minutes of book-keeping is required to register pandas on Travis CI.
See http://about.travis-ci.org/docs/user/getting-started/
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} | 1 | "2012-06-07T08:28:10Z" | "2012-06-12T00:05:20Z" | "2012-06-12T00:03:12Z" | CONTRIBUTOR | null | ``` python
In [67]: a = pandas.DataFrame(np.random.rand(3,3),
....: columns=list('ABC'),
....: index=pandas.Index(list('abc'), name='index_a'))
In [68]: b = pandas.DataFrame(np.random.rand(3,3),
....: columns=list('ABC'),
....: index=pandas.Index(list('abc'), name='index_b'))
In [69]: a
Out[69]:
A B C
index_a
a 0.734551 0.364354 0.989970
b 0.356027 0.625983 0.855296
c 0.895759 0.144406 0.068718
In [70]: b
Out[70]:
A B C
index_b
a 0.130305 0.981312 0.541476
b 0.046006 0.369983 0.822214
c 0.621787 0.047847 0.420459
In [71]: c = pandas.concat([a, b], keys=['key0', 'key1'], names=['lvl0'])
In [72]: c
Out[72]:
A B C
lvl0 index_a
key0 a 0.734551 0.364354 0.989970
b 0.356027 0.625983 0.855296
c 0.895759 0.144406 0.068718
key1 a 0.130305 0.981312 0.541476
b 0.046006 0.369983 0.822214
c 0.621787 0.047847 0.420459
In [73]: c = pandas.concat([a, b], keys=['key0', 'key1'], names=['lvl0', 'lvl1'])
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
...
AssertionError:
```
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} | 1 | "2012-06-07T14:00:07Z" | "2012-06-07T14:47:14Z" | "2012-06-07T14:34:31Z" | CONTRIBUTOR | null | ...th and file-like objects for io.
It also seems a good idea to align the argument names: _path_, _path_or_buffer_, _filepath_or_buffer_ are all three used among `to_csv()`, `from_csv()`, `read_table()` to indicate the same. I did not touch those since that is an api change.
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} | 0 | "2012-06-07T17:01:36Z" | "2012-06-11T21:07:40Z" | "2012-06-11T21:07:40Z" | NONE | null | Hi,
I'm trying to compute an outer join on several columns applied to several DataFrame objects in one step. However, the result I get seems to force the uniqueness on the set of join columns and, as a consequence, some rows are missing.
Here is some example code that shows the output from a join operation over 3 dataframes in one step and a merge operation (in 2 steps) over the same data. Comparing both, we see that the join operation doesn't include the row "1 1 10 100 1000".
```
from pandas import DataFrame, merge
def multiple_join():
df1 = DataFrame({"a": [1,1], "b": [1,1], "c": [10,20]})
df2 = DataFrame({"a": [1,1], "b": [1,2], "d": [100,200]})
df3 = DataFrame({"a": [1,1], "b": [1,2], "e": [1000,2000]})
df1.set_index(["a", "b"], inplace=True)
df2.set_index(["a", "b"], inplace=True)
df3.set_index(["a", "b"], inplace=True)
df_joined = df1.join([df2, df3], how='outer')
return df_joined
def cascade_merge():
df1 = DataFrame({"a": [1,1], "b": [1,1], "c": [10,20]})
df2 = DataFrame({"a": [1,1], "b": [1,2], "d": [100,200]})
df3 = DataFrame({"a": [1,1], "b": [1,2], "e": [1000,2000]})
df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer')
df_merged = merge(df_partially_merged, df3, on=['a', 'b'], how='outer')
return df_merged
if __name__ == "__main__":
print multiple_join()
print cascade_merge()
# c d e
# a b
# 1 1 20 100 1000
# 2 NaN 200 2000
#
# a b c d e
# 0 1 1 10 100 1000
# 1 1 1 20 100 1000
# 2 1 2 NaN 200 2000
```
However, this problem doesn't seem to arise when we specify "how={anything other that outer}" in the join operation.
So, either this is a bug or I'm missing something here. In either case, I would appreciate any comment regarding this issue. And, BTW, it would be really could if "merge" could accept a list of DataFrames and join them efficiently in one step.
Thanks and regards.
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https://api.github.com/repos/pandas-dev/pandas/issues/1423 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1423/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1423/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1423/events | https://github.com/pandas-dev/pandas/issues/1423 | 4,961,186 | MDU6SXNzdWU0OTYxMTg2 | 1,423 | select() within a function closure not working as agg function | {
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} | 2 | "2012-06-07T22:50:52Z" | "2012-06-11T21:03:10Z" | "2012-06-11T18:54:21Z" | CONTRIBUTOR | null | I'm running into a weird issue with groupby and function closure. For some reason the function closure doesn't work unless I access the grouped series. You can see in agg_before I have a fix flag that will just access the data var.
``` python
from pandas import *
import numpy as np
periods = 1000
ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods), 'low': np.arange(periods)}, index=ind)
def agg_before(hour, func, fix=False):
"""
Run an aggregate func on the subset of data.
"""
def _func(data):
d = data.select(lambda x: x.hour < 11).dropna()
if fix:
data[data.index[0]]
if len(d) == 0:
return None
return func(d)
return _func
def afunc(data):
d = data.select(lambda x: x.hour < 11).dropna()
return np.max(d)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
closure_bad = grouped.agg({'high': agg_before(11, np.max)})
closure_good = grouped.agg({'high': agg_before(11, np.max, True)})
lambda_good = grouped.agg({'high': afunc})
```
```
In [33]: np.__version__
Out[39]: '1.6.2'
In [34]: pandas.__version__
Out[34]: '0.8.0.dev-dc6ce90'
In [35]: closure_bad
Out[35]:
high
2012-01-01 131
2012-01-02 NaN
2012-01-03 NaN
2012-01-04 NaN
In [36]: closure_good
Out[36]:
high
2012-01-01 131
2012-01-02 419
2012-01-03 707
2012-01-04 995
In [37]: lambda_good
Out[37]:
high
2012-01-01 131
2012-01-02 419
2012-01-03 707
2012-01-04 995
```
Running an agg function that isn't a closure works fine. Any ideas on this?
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} | 0 | "2012-06-07T23:18:57Z" | "2012-06-08T01:00:40Z" | "2012-06-08T01:00:40Z" | MEMBER | null | ```
In [5]: rng = date_range('1/1/2000', periods=3, freq='5t')
In [6]: ts = Series(np.random.randn(len(rng)), rng)
In [7]: ts.resample('t', fill_method='ffill', limit=2)
Out[7]:
2000-01-01 00:00:00 0.204905
2000-01-01 00:01:00 0.204905
2000-01-01 00:02:00 0.204905
2000-01-01 00:03:00 0.204905
2000-01-01 00:04:00 0.204905
2000-01-01 00:05:00 0.804447
2000-01-01 00:06:00 0.804447
2000-01-01 00:07:00 0.804447
2000-01-01 00:08:00 0.804447
2000-01-01 00:09:00 0.804447
2000-01-01 00:10:00 -0.602923
Freq: T
```
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} | 5 | "2012-06-07T23:27:29Z" | "2014-06-15T07:05:12Z" | "2012-06-11T18:12:16Z" | CONTRIBUTOR | null | http://pandasplotting.blogspot.com/2012/06/autocorrelation-plot.html
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} | 2 | "2012-06-08T04:18:58Z" | "2014-07-06T05:59:15Z" | "2012-06-10T23:17:17Z" | CONTRIBUTOR | null | raises Exception if mixed or non-boolean #1416
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} | 0 | "2012-06-08T13:57:30Z" | "2013-12-04T00:58:08Z" | "2012-06-08T21:19:26Z" | CONTRIBUTOR | null | related to #1423
```
from pandas import *
import numpy as np
periods = 1000
ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods), 'low': np.arange(periods)}, index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
In [9]: grouped.groups
ValueError Traceback (most recent call last)
<ipython-input-9-aec229cf123a> in <module>()
----> 1 grouped.groups
~/pandas/pandas/core/groupby.pyc in groups(self)
150 @property
151 def groups(self):
--> 152 return self.grouper.groups
153
154 @property
~/pandas/pandas/core/index.pyc in groupby(self, to_groupby)
740
741 def groupby(self, to_groupby):
--> 742 return self._groupby(self.values, to_groupby)
743
744 def map(self, mapper):
...
...
...
~/pandas/pandas/lib.so in pandas.lib.groupby_arrays (pandas/src/tseries.c:67246)()
ValueError: Buffer dtype mismatch, expected 'int64_t' but got Python object
```
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} | 2 | "2012-06-08T14:29:04Z" | "2012-06-08T14:43:02Z" | "2012-06-08T14:40:33Z" | NONE | null | happened with latest pandas.
```
from pandas import *
x = DataFrame([(0,1),(2,3)], columns = ['a','b'])
x[:,['a']]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-2277db18002c> in <module>()
1 from pandas import *
2 x = DataFrame([(0,1),(2,3)], columns = ['a','b'])
----> 3 x[:,['a']]
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in __getitem__(self, key)
1679 raise ValueError('Cannot index using non-boolean DataFrame')
1680 else:
-> 1681 return self._get_item_cache(key)
1682
1683 def _getitem_array(self, key):
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in _get_item_cache(self, item)
471 return cache[item]
472 except Exception:
--> 473 values = self._data.get(item)
474 res = self._box_item_values(item, values)
475 cache[item] = res
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in get(self, item)
783
784 def get(self, item):
--> 785 _, block = self._find_block(item)
786 return block.get(item)
787
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _find_block(self, item)
892
893 def _find_block(self, item):
--> 894 self._check_have(item)
895 for i, block in enumerate(self.blocks):
896 if item in block:
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _check_have(self, item)
898
899 def _check_have(self, item):
--> 900 if item not in self.items:
901 raise KeyError('no item named %s' % str(item))
902
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_ed7a3d2-py2.7-linux-x86_64.egg/pandas/core/index.pyc in __contains__(self, key)
276
277 def __contains__(self, key):
--> 278 hash(key)
279 # work around some kind of odd cython bug
280 try:
TypeError: unhashable type
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1432 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1432/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1432/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1432/events | https://github.com/pandas-dev/pandas/issues/1432 | 4,972,825 | MDU6SXNzdWU0OTcyODI1 | 1,432 | DataFrame.ix assign fails for mixed dtypes | {
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} | 1 | "2012-06-08T15:28:58Z" | "2012-06-11T16:55:22Z" | "2012-06-11T16:55:14Z" | CONTRIBUTOR | null | from [mailing list](http://groups.google.com/group/pydata/browse_thread/thread/43c1bc3a314a9bb3#)
``` python
data = pandas.DataFrame.from_dict({1: [1.,2.,3.], 2: [3,4,5]})
print data.dtypes
data.ix[1] = [10., 20] # fails
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1433/events | https://github.com/pandas-dev/pandas/issues/1433 | 4,972,912 | MDU6SXNzdWU0OTcyOTEy | 1,433 | Unexpected behaviour when converting datetime column to np.datetime64 | {
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} | 1 | "2012-06-08T15:34:05Z" | "2012-06-14T03:04:56Z" | "2012-06-14T03:04:37Z" | NONE | null | To me this behaviour appeared to be weird.
Numpy 1.7 and latest Pandas
```
from pandas import *
from datetime import datetime
x = DataFrame([datetime.now(), datetime.now()])
print x[0].dtype
print to_datetime(x[0]).dtype
x[0] = to_datetime(x[0])
print x[0].dtype, "shouldn't this be datetime64?"
x[1] = to_datetime(x[0])
print x[1].dtype, "this is"
object
datetime64[ns]
object shouldn't this be datetime64?
datetime64[ns] this is
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1434 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1434/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1434/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1434/events | https://github.com/pandas-dev/pandas/issues/1434 | 4,976,160 | MDU6SXNzdWU0OTc2MTYw | 1,434 | On main website: url to vbench is wrong | {
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} | [] | closed | false | null | [] | null | 1 | "2012-06-08T18:37:59Z" | "2012-06-08T20:34:51Z" | "2012-06-08T20:34:51Z" | NONE | null | Hello,
In "RELATED TOOLS" (right column, bottom), the link to vbench is https://github.com/wesm/vbench (404) while it should be https://github.com/pydata/vbench.
(Didn't found the github repository of the main website to send a pull request).
Have a nice day,
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https://api.github.com/repos/pandas-dev/pandas/issues/1435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1435/events | https://github.com/pandas-dev/pandas/issues/1435 | 4,976,877 | MDU6SXNzdWU0OTc2ODc3 | 1,435 | Inconsistent results using custom function though resample & how | {
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} | 2 | "2012-06-08T19:17:39Z" | "2012-06-14T03:08:38Z" | "2012-06-14T03:08:38Z" | NONE | null | I have noticed some inconsistent results between using resample & how calling a defined function. It might be I have misunderstood how this is meant to function.
Here's some code to illustrate it:
```
import numpy as np
import pandas
from datetime import *
start = datetime(2010,6,1)
end = datetime(2012,1,1)
rng = pandas.date_range(start, end, freq='BM')
ts = pandas.Series(np.random.randn(len(rng)), index=rng)
def testfunction1(data):
return np.sum(data) / len(data)
def testfunction2(data):
return np.mean(data) / np.std(data)
t1 = ts.resample('Q', how=testfunction1)
t0 = ts.resample('Q', how=np.mean)
t2 = ts.resample('Q', how=np.sum)
t3 = ts.resample('Q', how=len)
# Compare defined function with numpy function
t1 == t0
# Everything is True
# Compare defined function (t1) with explicit calculation on timeseries
t2/t3 == t1
# Everything is True
# Apply second function involving mean/std
d0 = ts.resample('Q', how=testfunction2)
d1 = ts.resample('Q', how=np.mean)
d2 = ts.resample('Q', how=np.std)
# Compare defined function with explicit calculation on timeseries
d1/d2 == d0
# Everything is False
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1436/events | https://github.com/pandas-dev/pandas/issues/1436 | 4,977,665 | MDU6SXNzdWU0OTc3NjY1 | 1,436 | bug in DataFrame.plot() | {
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} | [] | closed | false | null | [] | null | 5 | "2012-06-08T20:04:41Z" | "2012-06-08T22:32:58Z" | "2012-06-08T22:32:58Z" | NONE | null | I seem to run into a bug in pandas 0.8 with plot().
This is the sequence of commands, the external file that is read can be send to reproduce the exception.
import pandas
s1=pandas.read_table('DATA_serie1.TXT')
dr = pandas.date_range(start='2012-01-01', periods=1500, freq='30Min')
s1.index=dr
s1.plot()
This is the exception trace:
## In [13]: s1.plot()
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 693, in plot_frame
plot_obj.generate()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 241, in generate
self._make_plot()
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 431, in _make_plot
self._make_ts_plot(data)
File "C:\Python27\lib\site-packages\pandas\tools\plotting.py", line 492, in _make_ts_plot
tsplot(data[col], plotf, ax=ax, label=label, *_kwargs)
File "C:\Python27\lib\site-packages\pandas\tseries\plotting.py", line 111, in tsplot
plotted = plotf(ax, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 3783, in plot
for line in self._get_lines(_args, *_kwargs):
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 317, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 294, in _plot_args
x, y = self._xy_from_xy(x, y)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 210, in _xy_from_xy
bx = self.axes.xaxis.update_units(x)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 1256, in update_units
converter = munits.registry.get_converter(data)
File "C:\Python27\lib\site-packages\matplotlib\units.py", line 132, in get_converter
for thisx in x:
File "C:\Python27\lib\site-packages\pandas\tseries\period.py", line 687, in __iter__
yield Period(ordinal=val, freq=self.freq)
File "C:\Python27\lib\site-packages\pandas\tseries\period.py", line 138, in **init**
raise ValueError('Only mult == 1 supported')
ValueError: Only mult == 1 supported
I will send the file to wes on his personal e-mail address, unless I can add it to this issue somehow?
thanks,
roel
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} | 2 | "2012-06-08T22:34:54Z" | "2012-06-11T16:31:10Z" | "2012-06-11T16:30:50Z" | NONE | null | In [82]: dr = pandas.date_range(start='2012-01-01', periods=10, freq='30Min')
In [83]: dr
Out[83]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-01 04:30:00]
Length: 10, Freq: 30T, Timezone: None
In [84]: dr.values
Out[84]:
array([1970-01-16 224:00:00, 1970-01-16 224:30:00, 1970-01-16 225:00:00,
1970-01-16 225:30:00, 1970-01-16 226:00:00, 1970-01-16 226:30:00,
1970-01-16 227:00:00, 1970-01-16 227:30:00, 1970-01-16 228:00:00,
1970-01-16 228:30:00], dtype=datetime64[ns])
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} | 0 | "2012-06-09T00:18:52Z" | "2012-06-09T00:29:51Z" | "2012-06-09T00:29:51Z" | CONTRIBUTOR | null | In case users forget 3rd argument is periods and not freq.
e.g., date_range('2011/1/1', '2012/1/1', 'B') raises MemoryError
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```
$ python
Python 2.6.6 (r266:84292, Dec 27 2010, 00:02:40)
[GCC 4.4.5] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> a = [1,2,3,4,5]
>>> a[-1]
5
>>> import numpy
>>> npa = numpy.array([1,2,3,4,5])
>>> npa[-1]
5
>>> import pandas
>>> s = pandas.Series([1,2,3,4,5])
>>> s[-1]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.6/dist-packages/pandas-0.7.2-py2.6-linux-i686.egg/pandas/core/series.py", line 376, in __getitem__
return self.index.get_value(self, key)
File "/usr/local/lib/python2.6/dist-packages/pandas-0.7.2-py2.6-linux-i686.egg/pandas/core/index.py", line 516, in get_value
return self._engine.get_value(series, key)
File "engines.pyx", line 21, in pandas._engines.IndexEngine.get_value (pandas/src/engines.c:1558)
File "engines.pyx", line 29, in pandas._engines.IndexEngine.get_value (pandas/src/engines.c:1437)
File "engines.pyx", line 101, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2664)
File "engines.pyx", line 108, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2616)
KeyError: -1
>>>
```
Notice that if indexes aren't numeric, everything work as expected:
```
>>> s = pandas.Series([1,2,3,4,5],index=['a','b','c','d','e'])
>>> s[-1]
5
```
Is this a bug or "by design"?
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} | 1 | "2012-06-09T13:23:54Z" | "2014-06-19T14:06:42Z" | "2012-06-11T18:01:51Z" | CONTRIBUTOR | null | {
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> > root@master:~/project/data# python
> > Python 2.6.5 (r265:79063, Apr 16 2010, 13:57:41)
> > [GCC 4.4.3] on linux2
> > Type "help", "copyright", "credits" or "license" for more information.
> >
> > > from pandas import \*
> > > df = read_csv('test.csv', index_col=0)
> > > /usr/local/lib/python2.6/dist-packages/pandas/io/parsers.py:698: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
> > > result = lib.maybe_convert_numeric(values, na_values)
> > > Segmentation fault
Let me know if you can reproduce this or if I can give any additional information. ( I am able to run it without problem on OSX 10.7.4. )
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} | 4 | "2012-06-10T18:51:08Z" | "2014-06-14T22:36:05Z" | "2012-06-11T17:54:27Z" | CONTRIBUTOR | null | This should fix bug #826 that had yahoo randomly failing downloads.
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} | 1 | "2012-06-11T10:10:06Z" | "2012-06-11T17:45:35Z" | "2012-06-11T17:45:35Z" | NONE | null | Cursor must always be closed before committing a connection
(http://stackoverflow.com/questions/9173073/python-mysql-mysqldb-testing-the-insert-function-its-not-adding-rows),
otherwise some DB client libraries will complain (specifically,
MySQLdb).
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} | 0 | "2012-06-11T15:38:49Z" | "2012-06-11T17:59:16Z" | "2012-06-11T17:59:16Z" | CONTRIBUTOR | null | ujson was never auto-sniffing the dtype because `PyArray_DescrConverter` converts `None` to the numpy default dtype. It should instead use `PyArray_DescrConverter2` which converts `None` to `NULL`.
The rest of the changes are to fix tests related to this "new" behaviour.
Tested with Python 2.7 on 64 bit OSX, 32 bit Ubuntu and 32 bit WinXP with Mingw32 and MSVC. EPD is still seg faulting.
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http://travis-ci.org/#!/msabramo/pandas/builds/1596467
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} | 25 | "2012-06-12T03:33:45Z" | "2014-02-18T19:51:18Z" | "2014-02-18T19:51:18Z" | CONTRIBUTOR | null | Wouldn't it be nice to have a common minimal interface for pandas' DataFrame and rpy2.robjects.vectors.DataFrame ?
This would allow duck typing and make the use of such structures across pandas and R as seamless as possible. This might be at the expense of performances in cases where conversion are made behind the scene but the general is to make a first implementation easy for a developper, and optimization made later whenever needed.
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} | 0 | "2012-06-12T21:49:29Z" | "2012-06-14T16:47:57Z" | "2012-06-14T16:47:57Z" | MEMBER | null | To reproduce:
```
from pandas import *
dates = date_range('4/16/2012 20:00', periods=5000, freq='h')
ts = Series(randn(len(dates)), index=dates)
ts.resample('M')
```
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} | 4 | "2012-06-12T22:03:26Z" | "2013-09-23T00:24:04Z" | "2013-09-23T00:24:04Z" | MEMBER | null | cf C code
`dtype = PyArray_DescrFromType(NPY_INT64);
new_obj = PyArray_View(obj, dtype, NULL);
Py_DECREF(dtype);
if (new_obj == NULL) {
...
}`
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} | 0 | "2012-06-13T00:34:02Z" | "2012-06-14T02:36:48Z" | "2012-06-14T02:36:48Z" | CONTRIBUTOR | null | {
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} | 1 | "2012-06-13T00:46:11Z" | "2012-06-14T14:49:05Z" | "2012-06-14T14:48:58Z" | CONTRIBUTOR | null | ``` python
from pandas import *
dr = date_range(start='1/1/2012', freq='5min', periods=10)
# BAD Example, datetimes first
labels = zip(dr, range(10))
s = Series(np.arange(10), index=MultiIndex.from_tuples(labels, names=['a','b']))
grouped = s.groupby(lambda x: x[1] % 2 == 0)
grouped.count()
# DateParseError: Could not parse COUNT
# Works fine
labels = zip(range(10), dr)
s = Series(np.arange(10), index=MultiIndex.from_tuples(labels, names=['a','b']))
grouped = s.groupby(lambda x: x[0] % 2 == 0)
grouped.count()
# False 5
# True 5
```
Seems to have happened within the last few days, maybe even just today.
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https://api.github.com/repos/pandas-dev/pandas/issues/1456 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1456/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1456/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1456/events | https://github.com/pandas-dev/pandas/pull/1456 | 5,038,859 | MDExOlB1bGxSZXF1ZXN0MTU1MTQyNA== | 1,456 | Add parentheses to a print statement so it compiles under Python 3 | {
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} | 3 | "2012-06-13T01:41:43Z" | "2012-06-14T03:07:15Z" | "2012-06-14T02:38:45Z" | CONTRIBUTOR | null | Just fixing a Python 2-style print statement in docs/make.py so it'll compile under Python 3. (Untested but I can't imagine how this would break anything)
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} | 4 | "2012-06-13T19:42:48Z" | "2014-12-20T23:39:38Z" | "2012-06-14T02:50:37Z" | NONE | null | ```
dr = date_range(start='2012-4-13', end='2012-5-1')
ts = Series(range(len(dr)), dr)
ts_utc = ts.tz_convert('UTC')
ts_local = ts_utc.tz_convert('America/Los_Angeles')
ts_local.resample('W')
```
yields
``` traceback
~/env/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _get_time_bins(self, axis)
107
108 # general version, knowing nothing about relative frequencies
--> 109 bins = lib.generate_bins_dt64(axis.asi8, binner.asi8, self.closed)
110
111 if self.label == 'right':
~/env/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.generate_bins_dt64 (pandas/src/tseries.c:60938)()
ValueError: Values falls before first bin
```
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} | 2 | "2012-06-13T23:21:03Z" | "2012-06-14T02:36:24Z" | "2012-06-14T02:36:24Z" | CONTRIBUTOR | null | ``` python
from pandas import *
dr = date_range(start='1/1/2012', freq='5min', periods=1000)
s = Series(np.array(100), index=dr)
# subset the data.
s = s[s.index.hour < 7]
# len(s) is now 336
s.resample('10min', how=lambda x: len(x))
# Wrong, thinks we're upsampling
#2012-01-01 00:00:00 100
#2012-01-01 00:15:00 100
#2012-01-01 00:30:00 100
#2012-01-01 00:45:00 100
#2012-01-01 01:00:00 100
# 30min index has less labels.
s.resample('30min', how=lambda x: len(x))
# Right
#2012-01-01 00:00:00 1
#2012-01-01 00:30:00 6
#2012-01-01 01:00:00 6
#2012-01-01 01:30:00 6
#2012-01-01 02:00:00 6
```
What's going on here is that resample tests whether you are down/up sampling by the len of label arrays. If your source data is ordered but sparse, the resampling will sometimes assume you're up-sampling when you're not.
I run into this with intraday finance data since mine is restricted to trading hours.
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} | 1 | "2012-06-14T01:40:20Z" | "2013-12-04T00:43:13Z" | "2012-06-14T03:38:05Z" | CONTRIBUTOR | null | ``` python
from pandas import *
# ERROR
s = Series(0, index=date_range(start='1/1/2012 9:30', freq='1min', periods=21))
s.resample('10min', how='mean',closed='left', label='right')
# Works. Only changed the start time.
s = Series(0, index=date_range(start='1/1/2012 9:31', freq='1min', periods=21))
s.resample('10min', how='mean',closed='left', label='right')
```
Happens whenever the binlabels are trimmed and the label is set to 'right'. It ends up reducing the labels twice.
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} | 3 | "2012-06-14T02:58:52Z" | "2012-06-19T17:20:00Z" | "2012-06-19T17:20:00Z" | CONTRIBUTOR | null | add option to drop parts of the xaxis without data
from mailing list:
Hi,
I am having difficulty sorting out how to do any intra-day plot of time series data in matplotlib or pandas. I have looked through the documentation and also purchased the Python_for_Data_Analysis ebook but it does not seem covered in either. There are many references to daily samplings.
I can produce images by using the DataFrame.plot or Series.plot function. This results in a well laid out plot, except for the hours outside of my timeseries (in the example of exchange hours for financial data) are included in the axis and interpolated. If I give it the criteria use_index=False, the plot looks as I would expect but I look my Timestamp index and it becomes an integer index as I would receive if I used matplotlib/pyplot.
I am looking for direction on this and to recommend an inclusion of this topic in either the references and documentation. Pandas is great and I love learning and working with it.
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should probably be tuned - especially for integers
maybe related to issue #1462
qcut(Series([0,1,2,3,4,5,3531132324355]))
Factor:
array([(-3531132324.355, 1.5], (-3531132324.355, 1.5], (1.5, 3], (1.5, 3],
(3, 4.5], (4.5, 3531132324355], (4.5, 3531132324355]], dtype=object)
Levels (4): array([(-3531132324.355, 1.5], (1.5, 3], (3, 4.5], (4.5, 3531132324355]], dtype=object)
qcut(Series([0,1,2,3,4,5,3535))
Factor:
array([(-3.535, 1.5], (-3.535, 1.5], (1.5, 3], (1.5, 3], (3, 4.5],
(4.5, 3535], (4.5, 3535]], dtype=object)
Levels (4): array([(-3.535, 1.5], (1.5, 3], (3, 4.5], (4.5, 3535]], dtype=object)
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} | 0 | "2012-06-14T16:54:28Z" | "2013-11-24T13:44:10Z" | "2012-06-14T19:09:35Z" | MEMBER | null | The convention of, say, MonthEnd at midnight will result in on _on that day_ being placed in the wrong bucket
Referenced in #1458
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} | 8 | "2012-06-15T12:01:41Z" | "2012-06-22T16:21:39Z" | "2012-06-21T17:03:18Z" | CONTRIBUTOR | null | There seems to be some kind of wrap-around phenomenon if your index is too far away from now. For example if I create a timeseries with an index in 1400, the timeseries I get back has an index which is some time in 1984:
``` python
In [1]: import datetime, pandas
In [2]: pandas.version.version
Out[2]: '0.8.0b2'
In [3]: ts = pandas.TimeSeries(index=[ datetime.datetime(1400,1,2) ], data = [1])
In [4]: ts
Out[4]: 1984-07-22 23:34:33.709551 1
```
It doesn't seem to be a display issue because I can do:
``` python
In [7]: ts.index[0].to_pydatetime()
Warning: discarding nonzero nanoseconds
Out[7]: datetime.datetime(1984, 7, 22, 23, 34, 33, 709551)
```
I tested I could get the same behaviour both using 32-bit and 64-bit python on windows. It seems to work fine for 0.7.3:
``` python
In [1]: import datetime, pandas
In [2]: pandas.version.version
Out[2]: '0.7.3'
In [3]: ts = pandas.TimeSeries(index=[datetime.datetime(1400,1,2)], data = [1])
In [4]: ts.index[0]
Out[4]: datetime.datetime(1400, 1, 2, 0, 0)
```
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Maybe allow users to use Series.interpolate and other custom interpolation functions
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} | 0 | "2012-06-15T19:38:31Z" | "2012-06-19T17:36:17Z" | "2012-06-19T17:36:17Z" | MEMBER | null | ```
If one has data with non-unique indices like this one:
X.3
X.1_X.2
2004-01-02 08:12:30 1.2586
2004-01-02 08:12:31 1.2586
2004-01-02 08:12:32 1.2586
2004-01-02 08:12:32 1.2585
2004-01-02 08:12:36 1.2585
2004-01-02 08:12:37 1.2585
and one does a slicing like:
data[datetime(2004,1,2,8,12,32):]
it fails with
IndexError: invalid slice
Should it work ?
Jochen
whole traceback:
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.0b2-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in __getitem__(self, key)
1572 else:
1573 indexer = self.ix._convert_to_indexer(key, axis=0)
-> 1574 new_data = self._data.get_slice(indexer, axis=1)
1575 return self._constructor(new_data)
1576 # either boolean or fancy integer index
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.0b2-py2.6-linux-x86_64.egg/pandas/core/internals.pyc in get_slice(self, slobj, axis)
651 def get_slice(self, slobj, axis=0):
652 new_axes = list(self.axes)
--> 653 new_axes[axis] = new_axes[axis][slobj]
654
655 if axis == 0:
/home/jgarcke/Prog/lib64/python2.6/site-packages/pandas-0.8.0b2-py2.6-linux-x86_64.egg/pandas/tseries/index.pyc in __getitem__(self, key)
1040 new_offset = self.offset
1041
-> 1042 result = arr_idx[key]
1043 if result.ndim > 1:
1044 return result
IndexError: invalid slice
```
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} | 0 | "2012-06-15T20:11:25Z" | "2012-06-19T18:32:41Z" | "2012-06-19T18:32:41Z" | CONTRIBUTOR | null | X-axis should omit weekends if bday freq
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} | 2 | "2012-06-15T21:32:02Z" | "2012-06-19T20:46:47Z" | "2012-06-19T20:46:47Z" | CONTRIBUTOR | null | ``` python
from pandas import *
N = 1000
s = Series(np.arange(N), index=date_range(start='1/1/2012', freq='5min', periods=N))
s.resample('MS')
```
Not sure if this is new, first time using MS.
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} | 3 | "2012-06-16T23:24:50Z" | "2012-06-30T01:16:54Z" | "2012-06-21T23:11:06Z" | NONE | null | # Synopsis
The FreeBSD port for matplotlib has not yet been updated to v1.1.0. In the mean time, this test fails due to a default marker type ('.') that is unknown in matplotlib v1.0.1.
# Details
## Environment
```
# uname -a
FreeBSD REDACTED 9.0-STABLE FreeBSD 9.0-STABLE #0 r236450: Sat Jun 2 11:08:51 EDT 2012 root@REDACTED:/usr/obj/usr/src/sys/NIPPL amd64
# pkg_info -oxQ pandas matplotlib
py27-matplotlib-1.0.1_4:math/py-matplotlib
py27-pandas-0.8.0.b2:math/py-pandas
```
## How to reproduce
```
# nosetests -xv pandas.tests.test_graphics:TestDataFramePlots.test_scatter
test_scatter (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
======================================================================
ERROR: test_scatter (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas-0.8.0b2-py2.7-freebsd-9.0-STABLE-amd64.egg/pandas/tests/test_graphics.py", line 252, in test_scatter
_check_plot_works(scat)
File "/usr/local/lib/python2.7/site-packages/pandas-0.8.0b2-py2.7-freebsd-9.0-STABLE-amd64.egg/pandas/tests/test_graphics.py", line 301, in _check_plot_works
ret = f(*args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas-0.8.0b2-py2.7-freebsd-9.0-STABLE-amd64.egg/pandas/tests/test_graphics.py", line 251, in scat
return plt.scatter_matrix(df, **kwds)
File "/usr/local/lib/python2.7/site-packages/pandas-0.8.0b2-py2.7-freebsd-9.0-STABLE-amd64.egg/pandas/tools/plotting.py", line 71, in scatter_matrix
marker=marker, alpha=alpha, **kwds)
File "/usr/local/lib/python2.7/site-packages/matplotlib/axes.py", line 5738, in scatter
raise ValueError('Unknown marker symbol to scatter')
ValueError: Unknown marker symbol to scatter
----------------------------------------------------------------------
Ran 1 test in 0.633s
FAILED (errors=1)
```
## Code analysis
[pandas/tests/test_graphics.py:252](https://github.com/pydata/pandas/blob/fde270b20861fbf36d3063d504fb299d0b58695b/pandas/tests/test_graphics.py#L252) does not specify a marker type.
[pandas/tools/plotting.py:22](https://github.com/pydata/pandas/blob/d7966d22f14375547e263347ec648dcba7b65617/pandas/tools/plotting.py#L22) defines `'.'` as the default value for the `marker`
[matplotlib/axes.py:5669-5682](https://github.com/matplotlib/matplotlib/blob/a9f3f3a50745a1ca0e666bb1b6d0b9d782553dd9/lib/matplotlib/axes.py#L5669-5682) list the marker types recognized in matplotlib v1.0.1.
## Suggested resolution
Choose a marker type available in matplotlib v1.0.1 (e.g. `'o'`).
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} | 0 | "2012-06-17T18:48:55Z" | "2012-06-19T01:02:40Z" | "2012-06-19T01:02:40Z" | CONTRIBUTOR | null | from pandas import Series, DataFrame
import pandas as pd
rng1 = pd.date_range('1/1/1999', '1/1/2012', freq='MS')
s1 = Series(np.random.randn(len(rng1)), rng1)
rng2 = pd.date_range('1/1/1980', '12/1/2001', freq='MS')
s2 = Series(np.random.randn(len(rng2)), rng2)
df = DataFrame({'s1' : s1, 's2' : s2})
# s1 is fine
In [39]: s1.index.dtype
Out[39]: dtype('datetime64[ns]')
In [41]: s1.index.values.dtype
Out[41]: dtype('datetime64[ns]')
# s2 is fine
In [40]: s2.index.dtype
Out[40]: dtype('datetime64[ns]')
In [42]: s2.index.values.dtype
Out[42]: dtype('datetime64[ns]')
# df.index looks fine
In [43]: df.index.dtype
Out[43]: dtype('datetime64[ns]')
# WAT?!
In [44]: df.index.values.dtype
Out[44]: dtype('datetime64[us]')
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} | 1 | "2012-06-17T22:31:09Z" | "2012-06-21T22:57:20Z" | "2012-06-21T22:57:20Z" | NONE | null | This might be an API change instead of an issue, but just in case, could you please confirm that this should crash?
```
DataFrame({'a':(1,2,3)})
```
with this error:
```
*** ValueError: If use all scalar values, must pass index
```
Using a dictionary where the values are equal length tuples used to work in 0.7.
Thanks!
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} | 1 | "2012-06-18T01:19:59Z" | "2012-06-19T17:04:08Z" | "2012-06-19T17:04:08Z" | CONTRIBUTOR | null | Changed "get_quote_yahoo" to parse the change_pct as a float instead of a string.
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} | 1 | "2012-06-18T14:49:20Z" | "2012-06-18T19:02:53Z" | "2012-06-18T19:02:53Z" | MEMBER | null | ```
Pandas uses the matplotlib boxplot, but it does not support passing down the argument 'notch=1' to the actual matplotlib boxplot call.
df.boxplot(notch=1) gives a traceback
Although according to documentation there should be some kind of kwds mechanism, there is none in fact.
Docstring:
boxplot(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, **kwds) method of pandas.core.frame.DataFrame instance
```
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} | 0 | "2012-06-18T19:43:01Z" | "2012-06-25T14:14:07Z" | "2012-06-25T14:14:07Z" | CONTRIBUTOR | null | From [mailing list](https://groups.google.com/forum/?fromgroups#!topic/pydata/RP_LdVyD1v0)
When constructing a DataFrame from a list of series, use the series name as index value (if possible).
Workaround is to use `DataFrame.from_items()`
``` python
In [21]: series = [pandas.Series(np.random.rand(3), name=c) for c in list('abcdefg')]
In [22]: df = pandas.DataFrame(series)
In [23]: df
Out[23]:
0 1 2
0 0.609676 0.023603 0.687154
1 0.060276 0.173545 0.216510
2 0.016216 0.078186 0.493883
3 0.648037 0.640642 0.652898
4 0.590155 0.132365 0.257735
5 0.590068 0.124617 0.104589
6 0.364781 0.473282 0.218554
In [24]: df = pandas.DataFrame.from_items([(s.name, s) for s in series]).T
In [25]: df
Out[25]:
0 1 2
a 0.609676 0.023603 0.687154
b 0.060276 0.173545 0.216510
c 0.016216 0.078186 0.493883
d 0.648037 0.640642 0.652898
e 0.590155 0.132365 0.257735
f 0.590068 0.124617 0.104589
g 0.364781 0.473282 0.218554
```
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} | 1 | "2012-06-18T20:28:21Z" | "2012-06-19T17:01:37Z" | "2012-06-19T17:01:37Z" | CONTRIBUTOR | null | can specify by column in subplots=True
need more complex handling (esp. with legends) if need to do secondary y-axis by column when subplots=False
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```
df[20:30]
```
but not
```
df[20]
```
?
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} | 1 | "2012-06-20T10:05:07Z" | "2013-12-04T00:58:03Z" | "2012-06-20T20:35:20Z" | NONE | null | ```
Traceback (most recent call last):
File "/home/agerigk/python-packages/pandas/pandas/tseries/tests/test_timeseries.py", line 1106, in test_argmin_argmax
self.assertEqual(idx.argmin(), 1)
File "/home/agerigk/python-packages/pandas/pandas/tseries/index.py", line 1041, in argmin
return (-self).argmax()
TypeError: ufunc 'negative' did not contain a loop with signature matching types dtype('<M8[ns]') dtype('<M8[ns]')
----------------------------------------------------------------------
Ran 2037 tests in 23.856s
FAILED (SKIP=46, errors=1)
```
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