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https://api.github.com/repos/pandas-dev/pandas/issues/7501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7501/events | https://github.com/pandas-dev/pandas/issues/7501 | 36,024,133 | MDU6SXNzdWUzNjAyNDEzMw== | 7,501 | Can't slice float indices, but can slice integer indices? | {
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} | 94 | 2014-06-18T20:26:40Z | 2020-04-15T22:12:59Z | null | NONE | null | Using pandas 0.14, the slicing changed strangely. I can slice rows using start/end with an integer index, but not a float index:
``` python
In [18]: df = pandas.DataFrame(np.random.randn(10, 5), index=np.arange(10, 20))
In [19]: df
Out[19]:
0 1 2 3 4
10 -1.878123 -0.581537 0.189536 0.173014 0.132059
11 1.229246 -0.988689 0.632404 0.939126 -0.186367
12 0.376735 0.329723 1.480293 -0.209164 0.080897
13 0.461558 0.303541 -0.669196 -1.032077 1.634512
14 -0.972455 0.657357 -0.566609 0.154165 -0.561543
15 -2.502244 1.022540 -1.019376 -0.934582 1.751852
16 -1.875567 0.504288 -0.524922 0.048277 -1.587904
17 0.636652 0.441224 -1.391552 0.650876 0.374673
18 -1.503102 0.822411 1.776667 -0.879583 1.035291
19 -0.620467 0.319855 -0.779280 -0.168827 0.502470
In [20]: df[3:5]
Out[20]:
0 1 2 3 4
13 0.461558 0.303541 -0.669196 -1.032077 1.634512
14 -0.972455 0.657357 -0.566609 0.154165 -0.561543
In [21]: df.index = [float(x) for x in df.index]
In [22]: df
Out[22]:
0 1 2 3 4
10 -1.878123 -0.581537 0.189536 0.173014 0.132059
11 1.229246 -0.988689 0.632404 0.939126 -0.186367
12 0.376735 0.329723 1.480293 -0.209164 0.080897
13 0.461558 0.303541 -0.669196 -1.032077 1.634512
14 -0.972455 0.657357 -0.566609 0.154165 -0.561543
15 -2.502244 1.022540 -1.019376 -0.934582 1.751852
16 -1.875567 0.504288 -0.524922 0.048277 -1.587904
17 0.636652 0.441224 -1.391552 0.650876 0.374673
18 -1.503102 0.822411 1.776667 -0.879583 1.035291
19 -0.620467 0.319855 -0.779280 -0.168827 0.502470
In [23]: df[3:5]
Out[23]:
Empty DataFrame
Columns: [0, 1, 2, 3, 4]
Index: []
In [24]: df.index
Out[24]: Float64Index([10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0], dtype='float64')
In [25]: pandas.__version__
Out[25]: '0.14.0'
```
Was this an intentional change, or a bug?
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} | 1 | 2014-06-18T20:50:11Z | 2014-06-21T22:30:16Z | 2014-06-21T20:13:29Z | MEMBER | null | Currently, `offsets.appy`, `rollforward` and `rollback` returns `Timestamp` if argument is `Timestamp` or `np.datetime64`.
If input is `datetime`, these functions return `datetime` or `Timestamp` inconsistently depending on internal process. It may better to always return `Timestanp`?
### Affected Offsets
- 'pandas.tseries.offsets.Day'
- 'pandas.tseries.offsets.MonthBegin'
- 'pandas.tseries.offsets.FY5253Quarter'
- 'pandas.tseries.offsets.FY5253'
- 'pandas.tseries.offsets.Week'
- 'pandas.tseries.offsets.WeekOfMonth'
- 'pandas.tseries.offsets.Easter'
- 'pandas.tseries.offsets.Hour'
- 'pandas.tseries.offsets.Minute'
- 'pandas.tseries.offsets.Second'
- 'pandas.tseries.offsets.Milli'
- 'pandas.tseries.offsets.Micro'
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} | 0 | 2014-06-18T21:37:53Z | 2014-06-19T00:01:34Z | 2014-06-19T00:01:34Z | CONTRIBUTOR | null | closes #7500
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| null | 3 | 2014-06-18T21:54:07Z | 2014-06-19T15:25:15Z | 2014-06-19T15:25:15Z | MEMBER | null | closes #7501
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} | 5 | 2014-06-18T22:40:48Z | 2014-06-30T19:38:12Z | 2014-06-30T19:38:04Z | CONTRIBUTOR | null | `pandas.core.strings.str_contains` does not match in a case insensitive fashion _at all_ when given `regex=False` and `case=False`. This PR should fix the situation. Additionally, there is test coverage for `pandas.core.strings.str_contains` case insensitive matching both with and without regular expressions enabled.
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} | 0 | 2014-06-19T00:23:26Z | 2014-06-19T09:53:31Z | 2014-06-19T09:53:31Z | CONTRIBUTOR | null | from SO: http://stackoverflow.com/questions/24296480/pandas-dataframe-where-misbehaving
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| null | 2 | 2014-06-19T00:55:50Z | 2014-06-19T11:40:26Z | 2014-06-19T11:40:20Z | NONE | null | Seems that it's looking for an attribute that's invalid.
```
print type(idx)
<class 'pandas.core.index.Float64Index'>
```
Where I created Float64Index elsewhere...
```
np.round(idx)
Traceback (most recent call last):
File "/home/glue/Desktop/PYUVVIS/pyuvvis/pyuvvis/core/timespectra.py", line 1, in <module>
"""Provides core "TimeSpectra" class and associated utilities."""
File "/home/glue/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 2629, in round_
return round(decimals, out)
AttributeError: rint
```
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} | 11 | 2014-06-19T13:51:00Z | 2015-03-17T00:18:39Z | 2015-03-17T00:18:39Z | MEMBER | null | Series is not correctly truncated according to the max_rows (number of rows is halved).
```
In [146]: s = pd.Series(np.random.randn(20))
In [147]: pd.options.display.max_rows = 10
In [148]: s
Out[148]:
0 1.545202
1 -1.427565
2 -0.961094
...
17 0.125228
18 2.153724
19 -0.384024
Length: 20, dtype: float64
In [149]: s.to_frame()
Out[149]:
0
0 1.545202
1 -1.427565
2 -0.961094
3 0.387392
4 1.036472
.. ...
15 0.918433
16 -0.874253
17 0.125228
18 2.153724
19 -0.384024
[20 rows x 1 columns]
```
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} | 15 | 2014-06-19T14:53:55Z | 2021-01-15T16:30:22Z | 2021-01-15T16:30:22Z | NONE | null | I found a number of issues that seemed related, all closed over a year ago, but there still seem to be some inconsistencies here:
``` python
In [1]: from datetime import datetime
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.13.1'
In [4]: dfa = pd.DataFrame([[datetime.now(), 2]], columns=['a','b'])
In [5]: dfb = pd.DataFrame([[4],[5]], columns=['b'])
In [6]: dfa.dtypes
Out[6]:
a datetime64[ns]
b int64
dtype: object
In [7]: dfb.dtypes
Out[7]:
b int64
dtype: object
In [8]: # int64 becomes float64 when combining the two frames
In [9]: dfa.combine_first(dfb).dtypes
Out[9]:
a datetime64[ns]
b float64
dtype: object
In [10]: # datetime64[ns] becomes float64 if the first frame is empty
In [11]: dfa.iloc[:0].combine_first(dfb).dtypes
Out[11]:
a float64
b int64
dtype: object
```
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} | 3 | 2014-06-19T15:01:01Z | 2014-06-20T22:15:59Z | 2014-06-20T22:15:59Z | CONTRIBUTOR | null | some warnings are generated (parser.pyx, msgpack.pyx) that are new
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} | 3 | 2014-06-19T16:31:31Z | 2014-09-10T19:51:03Z | 2014-09-10T19:51:03Z | NONE | null | The following code:
```
import pandas as pd
p1 = {'name': 'willy', 'age': 10}
p2 = {'name': 'willy', 'age': 11}
p3 = {'name': 'zoe', 'age': 10}
df = pd.DataFrame([p1, p2, p3])
df = df[['name','name','age']]
df.groupby('name')
```
gives the following error:
```
TypeError: 'DataFrame' object is not callable
```
Instead, it should say "Cannot groupby a column which exists more than once"
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} | 4 | 2014-06-19T17:16:44Z | 2014-07-01T10:14:18Z | 2014-07-01T10:14:18Z | CONTRIBUTOR | null | related #7514
There appears to be a bug in the expanding_{cov,corr} functions when dealing with two objects with different indexes.
First, there is a problem with series. See example below, where I would expect expanding_corr(s1, s2) to produce the result produced by expanding_corr(s1, s2a).
The problem is due to the fact that expanding_corr is implemented in terms of rolling_corr with window = max(len(arg1), len(arg2)), but then rolling_corr resets window to window = min(window, len(arg1), len(arg2)). The end result is that window = min(len(arg1), len(arg2)) -- and these are the raw, unaligned arg1 and arg2. Thus in the expanding_corr(s1, s2) example below, window=2, and so when calculating the third row (index=2) it tries to calculate the correlation between [2, 3] and [NaN, 3], producing NaN -- rather than calculating the correlation between [1, 2, 3] and [1, Nan, 3] and producing 1.
The solution would appear to be simply deleting the window = min(window, len(arg1), len(arg2)) line from rolling_cov and rolling_corr, as I believe the rolling_\* functions run fine with a window larger than the data, or at least replacing it with window = min(window, max(len(arg1), len(arg2))).
```
In [1]: from pandas import Series, expanding_corr
In [2]: s1 = Series([1, 2, 3], index=[0, 1, 2])
In [3]: s2 = Series([1, 3], index=[0, 2])
In [4]: expanding_corr(s1, s2)
Out[4]:
0 NaN
1 NaN
2 NaN
dtype: float64
In [5]: s2a = Series([1, None, 3], index=[0, 1, 2])
In [6]: expanding_corr(s1, s2a)
Out[6]:
0 NaN
1 NaN
2 1
dtype: float64
```
Next, there is a problem with data frames. [This was originally reported separately in https://github.com/pydata/pandas/issues/7512, but I've merged it into this issue.]
The problem is with with _flex_binary_moment(). When pairwise=True, it doesn't properly handle two DataFrames with different index sets. In the following example, I believe [6], [7], and [8] should all produce the result in [9].
```
In [1]: from pandas import DataFrame, expanding_corr
In [2]: df1 = DataFrame([[1,2], [3, 2], [3,4]], columns=['A','B'])
In [3]: df1a = DataFrame([[1,2], [3,4]], columns=['A','B'], index=[0,2])
In [4]: df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
In [5]: df2a = DataFrame([[5,6], [2,1]], columns=['X','Y'], index=[0,2])
In [6]: expanding_corr(df1, df2, pairwise=True)[2]
Out[6]:
X Y
A -1.224745 -1.224745
B -1.224745 -1.224745
In [7]: expanding_corr(df1, df2a, pairwise=True)[2]
Out[7]:
X Y
A NaN NaN
B NaN NaN
In [8]: expanding_corr(df1a, df2, pairwise=True)[2]
Out[8]:
X Y
A NaN NaN
B NaN NaN
In [9]: expanding_corr(df1a, df2a, pairwise=True)[2]
Out[9]:
X Y
A -1 -1
B -1 -1
```
And there are similar problems with rolling_cov and rolling_corr. For example, continuing with the previous example, [77], [78], and [79] should give the same result as [80].
```
In [77]: rolling_corr(df1, df2, window=3, pairwise=True, min_periods=2)[2]
Out[77]:
X Y
A -1.224745 -1.224745
B -1.224745 -1.224745
In [78]: rolling_corr(df1, df2a, window=3, pairwise=True, min_periods=2)[2]
Out[78]:
X Y
A NaN NaN
B NaN NaN
In [79]: rolling_corr(df1a, df2, window=3, pairwise=True, min_periods=2)[2]
Out[79]:
X Y
A NaN NaN
B NaN NaN
In [80]: rolling_corr(df1a, df2a, window=3, pairwise=True, min_periods=2)[2]
Out[80]:
X Y
A -1 -1
B -1 -1
```
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} | 2 | 2014-06-19T18:39:52Z | 2021-04-11T04:50:37Z | 2021-04-11T04:50:37Z | CONTRIBUTOR | null | Faster way to concat a mixture of of axis=0 and axis=1 (axis='meh') ?
I don't know what a good api would be for this... (assuming there isn't one already).
http://stackoverflow.com/a/24301138/1240268
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} | 8 | 2014-06-19T19:36:24Z | 2014-06-28T17:29:42Z | 2014-06-28T17:29:42Z | CONTRIBUTOR | null | There are several issues with _flex_binary_moment().
A. When pairwise=True, it doesn't properly handle two DataFrames with different index sets. In the following example, I believe [6], [7], and [8] should all produce the result in [9].
```
In [1]: from pandas import DataFrame, expanding_corr
In [2]: df1 = DataFrame([[1,2], [3, 2], [3,4]], columns=['A','B'])
In [3]: df1a = DataFrame([[1,2], [3,4]], columns=['A','B'], index=[0,2])
In [4]: df2 = DataFrame([[5,6], [None,None], [2,1]], columns=['X','Y'])
In [5]: df2a = DataFrame([[5,6], [2,1]], columns=['X','Y'], index=[0,2])
In [6]: expanding_corr(df1, df2, pairwise=True)[2]
Out[6]:
X Y
A -1.224745 -1.224745
B -1.224745 -1.224745
In [7]: expanding_corr(df1, df2a, pairwise=True)[2]
Out[7]:
X Y
A NaN NaN
B NaN NaN
In [8]: expanding_corr(df1a, df2, pairwise=True)[2]
Out[8]:
X Y
A NaN NaN
B NaN NaN
In [9]: expanding_corr(df1a, df2a, pairwise=True)[2]
Out[9]:
X Y
A -1 -1
B -1 -1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7515/events | https://github.com/pandas-dev/pandas/pull/7515 | 36,110,874 | MDExOlB1bGxSZXF1ZXN0MTczNzE1ODY= | 7,515 | BUG: area plot raises ValueError with tz-aware data | {
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} | 3 | 2014-06-19T19:41:30Z | 2014-07-05T04:46:54Z | 2014-07-05T01:31:30Z | MEMBER | null | Closes #7471.
Must be revisit after #7322.
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} | 0 | 2014-06-19T20:44:59Z | 2014-06-20T12:04:29Z | 2014-06-20T12:04:29Z | CONTRIBUTOR | null | I'd expect the [7] to produce the same result as [6], but instead it gives an error.
```
Python 3.4.0 (v3.4.0:04f714765c13, Mar 16 2014, 19:25:23) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: from numpy import zeros
In [2]: from pandas import Panel, MultiIndex
In [3]: p1 = Panel(zeros((3,3,3)), items=['a','b','c'], major_axis=['x','y','z'], minor_axis=['u','v','w'])
In [4]: mi = MultiIndex.from_tuples([(0,'x'), (1,'y'), (1,'z')])
In [5]: p2 = Panel(zeros((3,3,3)), items=['a','b','c'], major_axis=mi, minor_axis=['u','v','w'])
In [6]: p1.iloc[:, 1, 0]
Out[6]:
a 0
b 0
c 0
Name: u, dtype: float64
In [7]: p2.iloc[:, 1, 0]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-f7c04712ad2b> in <module>()
----> 1 p2.iloc[:, 1, 0]
C:\Python34\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1125 def __getitem__(self, key):
1126 if type(key) is tuple:
-> 1127 return self._getitem_tuple(key)
1128 else:
1129 return self._getitem_axis(key, axis=0)
C:\Python34\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
1332 continue
1333
-> 1334 retval = getattr(retval, self.name)._getitem_axis(key, axis=axis)
1335
1336 # if the dim was reduced, then pass a lower-dim the next time
C:\Python34\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis, validate_iterable)
1386 self._is_valid_integer(key, axis)
1387
-> 1388 return self._get_loc(key, axis=axis)
1389
1390 def _convert_to_indexer(self, obj, axis=0, is_setter=False):
C:\Python34\lib\site-packages\pandas\core\indexing.py in _get_loc(self, key, axis)
87
88 def _get_loc(self, key, axis=0):
---> 89 return self.obj._ixs(key, axis=axis)
90
91 def _slice(self, obj, axis=0, typ=None):
C:\Python34\lib\site-packages\pandas\core\panel.py in _ixs(self, i, axis)
803 if _is_list_like(key):
804 indexer = {self._get_axis_name(axis): key}
--> 805 return self.reindex(**indexer)
806
807 # a reduction
C:\Python34\lib\site-packages\pandas\core\panel.py in reindex(self, items, major_axis, minor_axis, **kwargs)
1109 else kwargs.pop('minor', None))
1110 return super(Panel, self).reindex(items=items, major_axis=major_axis,
-> 1111 minor_axis=minor_axis, **kwargs)
1112
1113 @Appender(_shared_docs['rename'] % _shared_doc_kwargs)
C:\Python34\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
1622 # perform the reindex on the axes
1623 return self._reindex_axes(axes, level, limit,
-> 1624 method, fill_value, copy).__finalize__(self)
1625
1626 def _reindex_axes(self, axes, level, limit, method, fill_value, copy):
C:\Python34\lib\site-packages\pandas\core\generic.py in _reindex_axes(self, axes, level, limit, method, fill_value, copy)
1639 ax = self._get_axis(a)
1640 new_index, indexer = ax.reindex(
-> 1641 labels, level=level, limit=limit, method=method)
1642
1643 obj = obj._reindex_with_indexers(
C:\Python34\lib\site-packages\pandas\core\index.py in reindex(self, target, method, level, limit, copy_if_needed)
3256 else:
3257 # hopefully?
-> 3258 target = MultiIndex.from_tuples(target)
3259
3260 return target, indexer
C:\Python34\lib\site-packages\pandas\core\index.py in from_tuples(cls, tuples, sortorder, names)
2809 tuples = tuples.values
2810
-> 2811 arrays = list(lib.tuples_to_object_array(tuples).T)
2812 elif isinstance(tuples, list):
2813 arrays = list(lib.to_object_array_tuples(tuples).T)
C:\Python34\lib\site-packages\pandas\lib.pyd in pandas.lib.tuples_to_object_array (pandas\lib.c:52859)()
TypeError: object of type 'numpy.int64' has no len()
```
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| null | 12 | 2014-06-19T22:18:08Z | 2020-03-20T13:36:38Z | 2014-10-05T01:01:36Z | NONE | null | I've installed pandas using pip. I got the following versions of pip and numpy:
```
pvadrevu@MacPro~$ sudo pip freeze|grep pandas
pandas==0.14.0
pvadrevu@MacPro~$ sudo pip freeze|grep numpy
numpy==1.8.1
```
I am using this on OSX 10.9.2. I get the following error when trying to import pandas
```
numpy.dtype has the wrong size, try recompiling
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/__init__.py", line 6, in <module>
from . import hashtable, tslib, lib
File "numpy.pxd", line 157, in init pandas.hashtable (pandas/hashtable.c:22331)
ValueError: numpy.dtype has the wrong size, try recompiling
```
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} | 6 | 2014-06-19T23:49:06Z | 2017-12-22T19:19:01Z | 2017-12-22T19:18:44Z | NONE | null | Using 0.14.0. `pandas.io.parsers.read_csv` is supposed to ignore blank-looking values if `na_filter=False`, but it **does not do this** for `index_col` columns.
foo.csv:
```
fruit,size,sugar
apples,medium,2
pear,medium,3
grape,small,4
durian,,1
```
The default behavior gives a dataframe with a NaN in place of the empty value from this last row:
```
df = pd.io.parsers.read_csv("foo.csv")
```
This gives the same dataframe with a blank string instead of a NaN. So far so good:
```
df = pd.io.parsers.read_csv("foo.csv", na_filter=False)
```
My expectation was that this next version would give a dataframe with no NaN values in the index, but it does **not**:
```
df = pd.io.parsers.read_csv("foo.csv", index_col=['fruit','size'], na_filter=False)
print df
=> sugar
fruit size
apples medium 2
pear medium 3
grape small 4
durian NaN 1
```
Because it unexpectedly includes NaNs, I've been fighting with [issue 4862 in `unstack`](//github.com/pydata/pandas/issues/4862) for hours :-(.
In order to get the desired behavior, a DF with no NaNs in the index, I have to read the data without a multi-index, then `set_index` afterwards:
```
df = pd.io.parsers.read_csv("foo.csv", na_filter=False)
df.set_index(['fruit','size'])
```
As a temporary fix, perhaps the documentation ought to clarify the behavior of `na_filter` with respect to `index_col`.
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} | 0 | 2014-06-20T00:51:47Z | 2014-06-20T12:04:29Z | 2014-06-20T12:04:29Z | CONTRIBUTOR | null | closes #7516
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} | 2 | 2014-06-20T02:21:00Z | 2014-06-21T13:40:30Z | 2014-06-21T13:40:30Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_nanargmin (pandas.tests.test_nanops.TestnanopsDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.14.0+git213-g741b2fa/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py", line 346, in test_nanargmin
allow_str=False, allow_obj=False)
File "/tmp/buildd/pandas-0.14.0+git213-g741b2fa/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py", line 240, in check_funs
self.check_fun(testfunc, targfunc, 'arr_date', **kwargs)
File "/tmp/buildd/pandas-0.14.0+git213-g741b2fa/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py", line 202, in check_fun
testarval, targarval, targarnanval, **kwargs)
File "/tmp/buildd/pandas-0.14.0+git213-g741b2fa/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py", line 158, in check_fun_data
targ = targfunc(targartempval, axis=axis, **kwargs)
File "/tmp/buildd/pandas-0.14.0+git213-g741b2fa/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py", line 323, in _argminmax_wrap
res = func(value, axis)
File "/usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.py", line 754, in argmin
return argmin(axis)
TypeError: ("ufunc 'subtract' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule 'safe'", 'axis: 0 of 2', 'skipna: False', 'kwargs: {}', 'testar: arr_date', 'targar: arr_date', 'targarnan: arr_date')
```
that was while building 0.14.0+git213-g741b2fa
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.14.0.dev
nose: 1.1.2
Cython: 0.15.1
numpy: 1.6.1
scipy: 0.9.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.3.1
numexpr: 1.4.2
matplotlib: 1.1.1rc
openpyxl: 1.7.0
xlrd: 0.6.1
xlwt: 0.7.2
xlsxwriter: None
lxml: None
bs4: 4.0.2
html5lib: 0.90
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7521/events | https://github.com/pandas-dev/pandas/pull/7521 | 36,136,678 | MDExOlB1bGxSZXF1ZXN0MTczODczMzQ= | 7,521 | DOC: fix startrow typo in docstrings | {
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| null | 1 | 2014-06-20T02:59:59Z | 2014-06-20T07:54:37Z | 2014-06-20T07:54:37Z | CONTRIBUTOR | null | Minor, but worth fixing.
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https://api.github.com/repos/pandas-dev/pandas/issues/7522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7522/events | https://github.com/pandas-dev/pandas/issues/7522 | 36,148,205 | MDU6SXNzdWUzNjE0ODIwNQ== | 7,522 | API: fancier fancy indexing to enable coordinate lookup? | {
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| null | 6 | 2014-06-20T08:08:46Z | 2014-07-10T18:20:41Z | 2014-07-07T15:44:16Z | CONTRIBUTOR | null | While wading through setitem-related code I've come across a reference to a GH issue (?? — _will fill this in later, the browser history is on a different computer_), where a poster complained about weird numpy fancy indexing:
``` python
In [11]: arr = np.arange(27).reshape(3,3,3)
In [12]: arr
Out[12]:
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
In [13]: arr[[0,1],:,[0,1]]
Out[13]:
array([[ 0, 3, 6],
[10, 13, 16]])
```
which he'd apparently expect to be rather:
``` python
In [14]: arr[[[0,1]],:,[[0],[1]]]
Out[14]:
array([[[ 0, 3, 6],
[ 9, 12, 15]],
[[ 1, 4, 7],
[10, 13, 16]]])
```
or arguably in a more readable way _(hint: ix and ix.T are broadcasted against each other to become 2x2 arrays)_:
``` python
In [25]: ix = np.array([[0,1]])
In [26]: arr[ix, :, ix.T]
Out[26]:
array([[[ 0, 3, 6],
[ 9, 12, 15]],
[[ 1, 4, 7],
[10, 13, 16]]])
```
Trying to learn what's going on actually made me grind through the [arrays.indexing.advanced](http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#integer) section of numpy docs and it seems that dimensionality tricks offered by numpy are quite neat and the "take-ish" single-dim `arr[[1,2,3]]` is only the tip of the iceberg.
Now, to pandas.
The said issue resulted in pandas containers diverging from numpy and implementing the automatically-broadcasting version, i.e. `df[[0,1], [0,1]].shape == (2, 2)` which is a huge convenience. And while almost all other dimensionality-changing tricks offered by numpy-fancy indexing seem out of scope because label management goes through the window, there's one use-case that fits pandas perfectly, namely folding several dimensions into one (ironically, this was the exact type of behaviour that was complained about in _GH-forgot-the-number_):
``` python
# this is how it's done in numpy
In [39]: arr = np.arange(25).reshape(5,5)
In [40]: arr
Out[40]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
In [41]: arr[[0,1,2], [0,1,2]]
Out[41]: array([ 0, 6, 12])
# and this is the analogous operation in pandas
In [42]: df = pd.DataFrame(arr)
In [43]: df
Out[43]:
0 1 2 3 4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
In [44]: df.unstack()[[(0,0), (1,1), (2,2)]]
Out[44]:
0 0 0
1 1 6
2 2 12
dtype: int64
```
And while the diagonal-getitem is rather easy to implement as shown above, I can't see how do I do the same for setitem besides <del>"unstack->setitem->stack"</del> (it should be the other way round: "stack->setitem->unstack", but hopefully you get the point).
Is there a way to do that? If not, is it possible to fit it into pandas API somehow or that ship has already sailed?
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} | 5 | 2014-06-20T10:32:59Z | 2014-06-20T13:53:41Z | 2014-06-20T13:53:41Z | CONTRIBUTOR | null | This looks like a regression from 0.13.1. If you have a simple dataframe with a non-unique but sorted index like:
``` python
df = pd.DataFrame(np.arange(5.), index=[datetime(2001, 1, i, 10, 00)
for i in [1,2,2,3,4]])
df
Out[23]:
0
2001-01-01 10:00:00 0
2001-01-02 10:00:00 1
2001-01-02 10:00:00 2
2001-01-03 10:00:00 3
2001-01-04 10:00:00 4
[5 rows x 1 columns]
```
Then in pandas 0.13.1 you could slice a range out of that dataframe with a date not in the index like so:
``` python
# pandas 0.13.1
df.ix[datetime(2001, 1, 2, 11, 0):]
Out[22]:
0
2001-01-03 10:00:00 3
2001-01-04 10:00:00 4
[2 rows x 1 columns]
```
In the HEAD of pandas/master now, you can't. Instead you get a TypeError with long long traceback.
``` python
# pandas 0.14.1
In [58]: df.ix[datetime(2001, 1, 2, 11, 0):]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-58-c4fe729e46fe> in <module>()
----> 1 df.ix[datetime(2001, 1, 2, 11, 0):]
/users/is/dbew/src/pandas/pandas/core/indexing.py in __getitem__(self, key)
69 return self._getitem_tuple(key)
70 else:
---> 71 return self._getitem_axis(key, axis=0)
72
73 def _get_label(self, label, axis=0):
/users/is/dbew/src/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis, validate_iterable)
843 labels = self.obj._get_axis(axis)
844 if isinstance(key, slice):
--> 845 return self._get_slice_axis(key, axis=axis)
846 elif _is_list_like(key) and not (isinstance(key, tuple) and
847 isinstance(labels, MultiIndex)):
/users/is/dbew/src/pandas/pandas/core/indexing.py in _get_slice_axis(self, slice_obj, axis)
1090 if not _need_slice(slice_obj):
1091 return obj
-> 1092 indexer = self._convert_slice_indexer(slice_obj, axis)
1093
1094 if isinstance(indexer, slice):
/users/is/dbew/src/pandas/pandas/core/indexing.py in _convert_slice_indexer(self, key, axis)
161 # if we are accessing via lowered dim, use the last dim
162 ax = self.obj._get_axis(min(axis, self.ndim - 1))
--> 163 return ax._convert_slice_indexer(key, typ=self.name)
164
165 def _has_valid_setitem_indexer(self, indexer):
/users/is/dbew/src/pandas/pandas/core/index.py in _convert_slice_indexer(self, key, typ)
589 else:
590 try:
--> 591 indexer = self.slice_indexer(start, stop, step)
592 except Exception:
593 if is_index_slice:
/users/is/dbew/src/pandas/pandas/tseries/index.py in slice_indexer(self, start, end, step)
1350 raise TypeError('Cannot index datetime64 with float keys')
1351
-> 1352 return Index.slice_indexer(self, start, end, step)
1353
1354 def slice_locs(self, start=None, end=None):
/users/is/dbew/src/pandas/pandas/core/index.py in slice_indexer(self, start, end, step)
1712
1713 # loc indexers
-> 1714 return Index(start_slice) & Index(end_slice)
1715
1716 def slice_locs(self, start=None, end=None):
/users/is/dbew/src/pandas/pandas/core/index.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
170 pass # python2 - MultiIndex fails on mixed types
171 # other iterable of some kind
--> 172 subarr = com._asarray_tuplesafe(data, dtype=object)
173
174 if dtype is None:
/users/is/dbew/src/pandas/pandas/core/common.py in _asarray_tuplesafe(values, dtype)
2106 if not (isinstance(values, (list, tuple))
2107 or hasattr(values, '__array__')):
-> 2108 values = list(values)
2109 elif isinstance(values, Index):
2110 return values.values
TypeError: 'datetime.datetime' object is not iterable
```
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} | 2 | 2014-06-20T12:02:05Z | 2014-06-21T13:48:20Z | 2014-06-21T13:48:20Z | CONTRIBUTOR | null | cc @dstephens99
Almost certainly a bad data failure
for the first should guard this with a `try: except:` (and make that value nan)
2nd needs a guard on the test to skip with a RemoteError? (I think this is done with some of the other tests)?
```
======================================================================
ERROR: test_get_all_data (pandas.io.tests.test_data.TestYahooOptions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 1308, in wrapper
return t(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\tests\test_data.py", line 316, in test_get_all_data
data = self.aapl.get_all_data(put=True)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 1107, in get_all_data
frame = getattr(self, meth_name)(expiry=month)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 792, in get_call_data
return self._get_option_data(month, year, expiry, 'calls').sortlevel()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 719, in _get_option_data
tables = self._get_option_tables(month, year, expiry)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 686, in _get_option_tables
self.underlying_price, self.quote_time = self._get_underlying_price(root)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 691, in _get_underlying_price
underlying_price = float(root.xpath('.//*[@class="time_rtq_ticker"]')[0]\
IndexError: list index out of range
======================================================================
ERROR: test_get_all_data_calls_only (pandas.io.tests.test_data.TestYahooOptions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 1308, in wrapper
return t(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\tests\test_data.py", line 325, in test_get_all_data_calls_only
data = self.aapl.get_all_data(call=True, put=False)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 1107, in get_all_data
frame = getattr(self, meth_name)(expiry=month)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 792, in get_call_data
return self._get_option_data(month, year, expiry, 'calls').sortlevel()
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win-amd64-2.7\pandas\io\data.py", line 727, in _get_option_data
" found".format(table_loc, ntables))
IndexError: Table location 9 invalid, 2 tables found
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7525/events | https://github.com/pandas-dev/pandas/pull/7525 | 36,165,348 | MDExOlB1bGxSZXF1ZXN0MTc0MDQxNzg= | 7,525 | REGR: Regression in datetimelike slice indexing with a duplicated index and non-exact end-points (GH7523) | {
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} | 0 | 2014-06-20T13:05:25Z | 2014-06-20T13:53:41Z | 2014-06-20T13:53:41Z | CONTRIBUTOR | null | closes #7523
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https://api.github.com/repos/pandas-dev/pandas/issues/7526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7526/events | https://github.com/pandas-dev/pandas/issues/7526 | 36,170,198 | MDU6SXNzdWUzNjE3MDE5OA== | 7,526 | Handling datetimes in columns | {
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| null | 6 | 2014-06-20T14:10:38Z | 2014-06-20T15:08:15Z | 2014-06-20T14:53:21Z | CONTRIBUTOR | null | I couldn't see anything like this in the issues list. It's a change in behaviour between `0.13.1` and `master/HEAD` which might be intended, might not :)
Suppose you create a dataframe like this:
``` python
from datetime import datetime
B = pd.DataFrame(data = np.array([[1, datetime(2001, 1, 1)],
[2,datetime(2001, 2, 1)]]), columns=['a', 'b'])
```
In `pandas 0.13.1` you can slice out the date column and you get datetime64 data back:
``` python
B['b'].values
Out[90]:
array(['2001-01-01T00:00:00.000000000+0000',
'2001-02-01T00:00:00.000000000+0000'], dtype='datetime64[ns]')
B['b'].values.dtype
Out[93]: dtype('<M8[ns]')
```
but in `master/HEAD` you get
``` python
B['b'].values
Out[97]:
array([datetime.datetime(2001, 1, 1, 0, 0),
datetime.datetime(2001, 2, 1, 0, 0)], dtype=object)
B['b'].values.dtype
Out[99]: dtype('O')
```
I noticed the change because some of our tests construct expected values using `datetime` and it no longer matches result (which is constructed indirectly and has Timestamp/datetime64 in it).
I'm not sure if this is a bug/regression or a desirable change in behaviour.. I guess it depends whether you think that all datetime data should be stored as `datetime64` or if you'd prefer to preserve the original type of the data where possible...
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https://api.github.com/repos/pandas-dev/pandas/issues/7527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7527/events | https://github.com/pandas-dev/pandas/issues/7527 | 36,173,665 | MDU6SXNzdWUzNjE3MzY2NQ== | 7,527 | Strange reindex behavior | {
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| null | 6 | 2014-06-20T14:51:25Z | 2014-06-20T15:06:46Z | 2014-06-20T15:06:46Z | NONE | null | ```
regressors = regressors.reindex(index=factors.index)
logger.debug('Regressors have the same index as factors: {}'.format(regressors.index is factors.index))
logger.debug('Regressors and factors indexes are the same: {}'.format(all(map(lambda x, y: x == y, regressors.index, factors.index))))
```
Results in this:
```
2014-06-20 10:47:59,747 DEBUG pmt.portfolio_analytics.attribution.AlphaData - Regressors have the same index as factors: False
2014-06-20 10:48:00,033 DEBUG pmt.portfolio_analytics.attribution.AlphaData - Regressors and factors indexes are the same: True
```
Why?
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} | 7 | 2014-06-20T20:06:33Z | 2014-09-14T07:24:58Z | 2014-08-30T14:11:57Z | NONE | null | The head of my Pandas dataframe , `df`, is shown below:
```
count1 count2 totalcount season
0 3 13 16 1
1 8 32 40 1
2 5 27 32 1
3 3 10 13 1
4 0 1 1 1
```
I'd like to make boxplots of `count1`, `count2`, and `totalcount`, grouped by `season` (there are 4 seasons) and have each set of box plots show up on their own subplot in a single figure.
When I do this with only two of the columns, say `count1` and `count2`, everything looks great.
```
df.boxplot(['count1', 'count2'], by='season')
```
But when I add `totalcount` to the mix, the axis limits go haywire.
```
df.boxplot(['count1', 'count2', 'totalcount'], by='season')
```
This happens irregardless of the order of the columns. I realize there are several ways around this problem, but it would be much more convenient if this worked properly. I believe this worked as expected in Pandas 0.13.1.
Output from `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Darwin
OS-release: 13.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
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} | 1 | 2014-06-20T20:28:40Z | 2014-06-21T02:39:07Z | 2014-06-20T21:38:59Z | MEMBER | null | Derived from #7485. Comparison with `DatetimeIndex` result incorrectly if `NaT` is included.
```
idx1 = pd.DatetimeIndex(['2011-01-01', pd.NaT], freq='M')
idx2 = pd.DatetimeIndex([pd.NaT, '2011-01-01'], freq='M')
print(idx1 > idx2)
# [True, False]
# it must be [False, False]
```
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} | 1 | 2014-06-20T23:46:44Z | 2014-06-21T13:48:24Z | 2014-06-21T13:48:20Z | CONTRIBUTOR | null | Added try, except to underlying price / quote_time function.
Changed to RemoteDataError in the event that some tables are found, but the correct table is not available.
Fixes #7524.
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} | 5 | 2014-06-21T04:31:06Z | 2014-07-19T14:15:57Z | 2014-06-23T14:03:11Z | CONTRIBUTOR | null | closes #6809
This borrows the mechanisms already used in read_csv to enable reading of Excel objects via URLs.
Not sure what tests are appropriate, and I'm not certain what the various decorators do, so I tried to mirror the read_csv tests as best I could.
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} | 3 | 2014-06-21T07:04:50Z | 2014-07-02T16:46:04Z | 2014-07-01T15:29:53Z | MEMBER | null | Closes #7498.
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} | 3 | 2014-06-21T08:01:17Z | 2014-06-21T14:47:49Z | 2014-06-21T13:41:19Z | MEMBER | null | `Timestamp.tz_localize` resets `nanosecond`.
```
t = pd.Timestamp('2011-01-01') + pd.offsets.Nano(1)
t.tz_localize('US/Eastern')
# Warning: discarding nonzero nanoseconds
#2011-01-01 00:00:00-05:00
```
Even though `DatetimeIndex.tz_convert` can preserve it.
```
idx = pd.date_range('3/11/2012 04:00', periods=10, freq='N')
idx.tz_localize('US/Eastern')
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2012-03-11 04:00:00-04:00, ..., 2012-03-11 04:00:00.000000009-04:00]
# Length: 10, Freq: N, Timezone: US/Eastern
```
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| null | 4 | 2014-06-21T12:52:19Z | 2014-06-26T10:28:55Z | 2014-06-21T13:40:17Z | CONTRIBUTOR | null | I think this will fix the issue #7520. The issue appears to be that older versions of `numpy`, probably less than `1.7.0`, can't handle `datetime64` dtypes very well. I have added simple check to see if `numpy` can handle a standard operation with `datetime64` dtypes, and if not it skips those tests.
I can't test this personally since I don't have the older version of `numpy`.
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| null | 3 | 2014-06-21T14:13:04Z | 2014-06-21T14:48:38Z | 2014-06-21T14:48:38Z | CONTRIBUTOR | null | Somewhere along the way (3230ed4bd, I think) `frame.py` had its permissions changed, presumably by accident:
```
-rwxr-xr-x 1 dsm dsm 178861 Jun 21 09:51 pandas/core/frame.py
```
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| null | 0 | 2014-06-21T14:31:32Z | 2014-06-21T14:48:38Z | 2014-06-21T14:48:38Z | CONTRIBUTOR | null | Closes #7536.
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| null | 0 | 2014-06-21T14:46:41Z | 2014-06-21T22:30:08Z | 2014-06-21T14:49:05Z | MEMBER | null | Release note correction for #7534. `tz_convert` has no problem.
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} | 1 | 2014-06-21T15:15:37Z | 2014-06-24T11:49:40Z | 2014-06-24T11:49:40Z | MEMBER | null | Related to #7485.
Currently, `DatetimeIndex.asobject` raises `ValueError` when it contains `NaT`, otherwise `PeriodIndex` doesn't inconsistently.
I think these should work the same way, and would like to confirm `PeriodIndex` should raise `ValueError`, or `DatetimeIndex.asobject` allows `NaT`. There may be historical reason, but I'm not sure why `DatetimeIndex` raises `ValueError` even though we can create object-dtype `Index` containning `NaT`
```
didx = pd.DatetimeIndex([datetime.datetime(2014, 1, 1), pd.NaT])
didx.asobject
# ValueError: DatetimeIndex with NaT cannot be converted to object
pidx = pd.PeriodIndex(['2014-01', 'NaT'], freq='M')
pidx.asobject
# Index([2014-01, NaT], dtype='object')
```
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} | 1 | 2014-06-21T15:22:33Z | 2014-07-26T14:44:41Z | 2014-07-26T14:44:41Z | MEMBER | null | Currently, `Index.unique` returns `ndarray` but `DatetimeIndex.unique` returns `DatetimeIndex` to preserve datetime information. I think `PeriodIndex.unique` should also return `PeriodIndex` (currently it returns `ndarray`).
I'd like to ask which is better to fix all `Index.unique` to return `Index` for consistency, or only fix `PeriodIndex`.
```
didx = pd.DatetimeIndex([datetime.datetime(2014, 1, 1), datetime.datetime(2014, 2, 1)])
didx.unique()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01, 2014-02-01]
pidx = pd.PeriodIndex(['2014-01', '2014-02'], freq='M')
pidx.unique()
# [528 529]
type(pidx.unique())
# <type 'numpy.ndarray'>
```
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| null | 4 | 2014-06-21T19:27:09Z | 2014-06-28T13:54:28Z | 2014-06-28T13:54:28Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/7512 and https://github.com/pydata/pandas/issues/7514.
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} | 0 | 2014-06-21T19:39:51Z | 2014-07-25T14:34:07Z | 2014-07-25T14:34:07Z | CONTRIBUTOR | null | _flex_binary_moment() sorts the columns alphabetically. I think it should preserve the order.
```
In [23]: df = DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=['B','A'])
In [24]: expanding_corr(df, df, pairwise=True)[3]
Out[24]:
A B
A 1.000000 -0.670166
B -0.670166 1.000000
```
(I originally mentioned this in https://github.com/pydata/pandas/issues/7514, but then broke it out as a separate issue.)
Also, _flex_binary_moment() doesn't work if multiple columns have the same name. While it's not obvious to me what the outcome should be for two distinct DataFrames with pairwise=False, there's no reason things shouldn't work for all other cases (i.e. a DataFrame with itself with pairwise=True or False; two distinct DataFrames with pairwise=True; and a DataFrame with a Series).
```
In [13]: df = DataFrame([[2,4],[1,2],[5,2],[8,1]], columns=['C','C'])
In [14]: expanding_corr(df, df, pairwise=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-f20d94c2d8dc> in <module>()
----> 1 expanding_corr(df, df, pairwise=True)
C:\Python34\lib\site-packages\pandas\stats\moments.py in expanding_corr(arg1, arg2, min_periods, freq, center, pairwise)
940 return rolling_corr(arg1, arg2, window,
941 min_periods=min_periods,
--> 942 freq=freq, center=center, pairwise=pairwise)
943
944
C:\Python34\lib\site-packages\pandas\stats\moments.py in rolling_corr(arg1, arg2, window, min_periods, freq, center, pairwise, how)
246 return num / den
247
--> 248 return _flex_binary_moment(arg1, arg2, _get_corr, pairwise=bool(pairwise))
249
250
C:\Python34\lib\site-packages\pandas\stats\moments.py in _flex_binary_moment(arg1, arg2, f, pairwise)
278 results[k1][k2] = results[k2][k1]
279 else:
--> 280 results[k1][k2] = f(*_prep_binary(arg1[k1], arg2[k2]))
281 return Panel.from_dict(results).swapaxes('items', 'major')
282 else:
C:\Python34\lib\site-packages\pandas\stats\moments.py in _get_corr(a, b)
239 adj_window = min(window, len(a), len(b))
240 num = rolling_cov(a, b, adj_window, min_periods, freq=freq,
--> 241 center=center)
242 den = (rolling_std(a, adj_window, min_periods, freq=freq,
243 center=center) *
C:\Python34\lib\site-packages\pandas\stats\moments.py in rolling_cov(arg1, arg2, window, min_periods, freq, center, pairwise, how)
217 bias_adj = count / (count - 1)
218 return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj
--> 219 rs = _flex_binary_moment(arg1, arg2, _get_cov, pairwise=bool(pairwise))
220 return rs
221
C:\Python34\lib\site-packages\pandas\stats\moments.py in _flex_binary_moment(arg1, arg2, f, pairwise)
290 results[col] = f(X[col], Y)
291
--> 292 return DataFrame(results, index=X.index, columns=res_columns)
293 else:
294 return _flex_binary_moment(arg2, arg1, f)
C:\Python34\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
201 dtype=dtype, copy=copy)
202 elif isinstance(data, dict):
--> 203 mgr = self._init_dict(data, index, columns, dtype=dtype)
204 elif isinstance(data, ma.MaskedArray):
205 import numpy.ma.mrecords as mrecords
C:\Python34\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
325
326 return _arrays_to_mgr(arrays, data_names, index, columns,
--> 327 dtype=dtype)
328
329 def _init_ndarray(self, values, index, columns, dtype=None,
C:\Python34\lib\site-packages\pandas\core\frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
4623
4624 # don't force copy because getting jammed in an ndarray anyway
-> 4625 arrays = _homogenize(arrays, index, dtype)
4626
4627 # from BlockManager perspective
C:\Python34\lib\site-packages\pandas\core\frame.py in _homogenize(data, index, dtype)
4932
4933 v = _sanitize_array(v, index, dtype=dtype, copy=False,
-> 4934 raise_cast_failure=False)
4935
4936 homogenized.append(v)
C:\Python34\lib\site-packages\pandas\core\series.py in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
2507 raise Exception('Data must be 1-dimensional')
2508 else:
-> 2509 subarr = _asarray_tuplesafe(data, dtype=dtype)
2510
2511 # This is to prevent mixed-type Series getting all casted to
C:\Python34\lib\site-packages\pandas\core\common.py in _asarray_tuplesafe(values, dtype)
2191 except ValueError:
2192 # we have a list-of-list
-> 2193 result[:] = [tuple(x) for x in values]
2194
2195 return result
ValueError: cannot copy sequence with size 2 to array axis with dimension 4
```
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} | 11 | 2014-06-21T21:01:56Z | 2014-07-24T17:07:33Z | 2014-07-24T17:07:33Z | CONTRIBUTOR | null | I mentioned this originally on https://groups.google.com/forum/#!topic/pydata/woxbB8na3C8.
ewma() simply ignores missing values, effectively calculating the exponentially weighted moving average on the compacted series without the missing values. I think this is incorrect, and that values should be weighted based on their absolute location.
In the code below, I reproduce the ewma() calculation using "wrong" weights, and then show what I believe the correct result should be using the "right" weights.
```
In [1]: from pandas import Series, ewma
In [2]: def simple_wma(x, w):
...: return x.multiply(w).cumsum() / w.cumsum()
...:
In [3]: s = Series([0, None, 100])
In [4]: com = 2
In [5]: alpha = 1/(1+com)
In [6]: wrong_weights_adjust_false = Series([(1-alpha), None, alpha])
In [7]: wrong_weights_adjust_true = Series([(1-alpha), None, 1])
In [8]: right_weights_adjust_false = Series([(1-alpha)**2, None, alpha])
In [9]: right_weights_adjust_true = Series([(1-alpha)**2, None, 1])
In [10]: ewma(s, com=com, adjust=False)
Out[10]:
0 0.000000
1 0.000000
2 33.333333
dtype: float64
In [11]: simple_wma(s, wrong_weights_adjust_false)
Out[11]:
0 0.000000
1 NaN
2 33.333333
dtype: float64
In [12]: simple_wma(s, right_weights_adjust_false)
Out[12]:
0 0.000000
1 NaN
2 42.857143
dtype: float64
In [13]: ewma(s, com=com, adjust=True)
Out[13]:
0 0
1 0
2 60
dtype: float64
In [14]: simple_wma(s, wrong_weights_adjust_true)
Out[14]:
0 0
1 NaN
2 60
dtype: float64
In [15]: simple_wma(s, right_weights_adjust_true)
Out[15]:
0 0.000000
1 NaN
2 69.230769
dtype: float64
In [16]: s2 = Series([0, 100])
In [17]: ewma(s2, com=com, adjust=False)
Out[17]:
0 0.000000
1 33.333333
dtype: float64
In [18]: ewma(s2, com=com, adjust=True)
Out[18]:
0 0
1 60
dtype: float64
```
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} | 4 | 2014-06-21T23:37:26Z | 2014-06-26T10:59:59Z | 2014-06-24T11:49:40Z | MEMBER | null | Closes #7539.
Also, this fixed #7112 case 3 and case 4 to preserve tz. I've modified the tests for it.
Related to #6469.
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| null | 1 | 2014-06-22T00:57:09Z | 2014-06-25T02:17:52Z | 2014-06-23T13:41:18Z | CONTRIBUTOR | null | Fix a minor typo in the `pandas.frame.to_excel` docstring.
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| null | 7 | 2014-06-22T02:44:32Z | 2017-05-20T13:16:58Z | 2015-10-20T22:26:27Z | NONE | null | Hi,
In R, there is a way to perform a rolling join as shown below.
library("data.table")
DT <-
data.table(
x = rep(c("a", "b", "c"), each = 3),
y = c(1, 3, 6),
v = 1:9)
setkey(DT, x, y) # Necessary for following self join.
DT[J("a", 4:6)]
DT[J("a", 4:6), roll = TRUE]
The relevant output looks like this (`J` is shorthand for self join):
> DT
> x y v
> 1: a 1 1
> 2: a 3 2
> 3: a 6 3
> 4: b 1 4
> 5: b 3 5
> 6: b 6 6
> 7: c 1 7
> 8: c 3 8
> 9: c 6 9
> DT[J("a", 4:6)] # v columns does not have "2".
> x y v
> 1: a 4 NA
> 2: a 5 NA
> 3: a 6 3
> DT[J("a", 4:6), roll = TRUE] # v column rolls "2" forward.
> x y v
> 1: a 4 2
> 2: a 5 2
> 3: a 6 3
In Pandas, the closest that I could think of is to use Join then DataFrame,fillna(method='pad') . But this will not yield the above R rolling join result since the first 2 rows would be NA in the joined column (it starts with (a,3) and not (a,2))
I am just wondering whether there is an equivalent operation in Pandas for this?
Kind regards,
Kris
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} | 6 | 2014-06-22T15:03:28Z | 2014-06-26T10:29:04Z | 2014-06-26T10:26:02Z | CONTRIBUTOR | null | This includes the requested fix for `nanops`, using `common.is_*_dtype` functions whenever possible.
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} | 20 | 2014-06-22T17:23:48Z | 2018-12-11T06:29:52Z | 2018-12-11T06:29:52Z | NONE | null | I used to do convert a dict with tuple keys to DataFrame like
```
DataFrame.from_dict(a_dict, 'index')
```
but it produces "IndexError: index out of bounds" from pandas 0.14. Is there a way that I still can use tuple as Dataframe label? My old codes do not work with 0.14.
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} | 0 | 2014-06-23T16:22:13Z | 2014-06-23T17:18:29Z | 2014-06-23T17:18:29Z | CONTRIBUTOR | null | different results when mixed vs single dtypes
```
In [1]: df = DataFrame(dict(A = np.arange(5), B = np.arange(5,10)))
In [2]: df
Out[2]:
A B
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
In [4]: df.iloc[2:4] = [[10,11],[12,13]]
In [5]: df
Out[5]:
A B
0 0 5
1 1 6
2 10 11
3 12 13
4 4 9
```
```
In [6]: df = DataFrame(dict(A = list('abcde'), B = np.arange(5,10)))
In [7]: df.iloc[2:4] = [['x',11],['y',13]]
In [8]: df
Out[8]:
A B
0 a 5
1 b 6
2 x y
3 11 13
4 e 9
```
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} | 0 | 2014-06-23T16:37:05Z | 2014-06-23T17:18:29Z | 2014-06-23T17:18:29Z | CONTRIBUTOR | null | closes #7551
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} | 15 | 2014-06-23T18:11:36Z | 2014-07-07T00:07:17Z | 2014-07-07T00:02:26Z | CONTRIBUTOR | null | In the old docstring, `'index'` is listed as a valid value for the `method` kwarg for `DataFrame.interpolate`. It looks like a typo, because there was no comma after `'index'`, and the explanation for `'index'` corresponded to `'values'`. Furthermore, it appears `'index'` is not a valid value for `method`:
``` python
In [67]: df = pd.DataFrame([[1], [2], [np.nan], [3]], index=[0, 0.7, 1.1, 4])
In [68]: df
Out[68]:
0
0.0 1
0.7 2
1.1 NaN
4.0 3
In [69]: df.interpolate(method="index")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-69-4cef75cee92e> in <module>()
----> 1 df.interpolate(method="index")
/home/david/miniconda3/envs/frackoptima/lib/python3.4/site-packages/pandas/core/generic.py in interp
olate(self, method, axis, limit, inplace, downcast, **kwargs)
2580 inplace=inplace,
2581 downcast=downcast,
-> 2582 **kwargs)
2583 if inplace:
2584 if axis == 1:
/home/david/miniconda3/envs/frackoptima/lib/python3.4/site-packages/pandas/core/internals.py in inte
rpolate(self, **kwargs)
2195
2196 def interpolate(self, **kwargs):
-> 2197 return self.apply('interpolate', **kwargs)
2198
2199 def shift(self, **kwargs):
/home/david/miniconda3/envs/frackoptima/lib/python3.4/site-packages/pandas/core/internals.py in appl
y(self, f, axes, filter, do_integrity_check, **kwargs)
2162 copy=align_copy)
2163
-> 2164 applied = getattr(b, f)(**kwargs)
2165
2166 if isinstance(applied, list):
/home/david/miniconda3/envs/frackoptima/lib/python3.4/site-packages/pandas/core/internals.py in inte
rpolate(self, method, axis, index, values, inplace, limit, fill_value, coerce, downcast, **kwargs)
667 **kwargs)
668
--> 669 raise ValueError("invalid method '{0}' to interpolate.".format(method))
670
671 def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
ValueError: invalid method 'index' to interpolate.
In [70]: df.interpolate(method="values")
Out[70]:
0
0.0 1.000000
0.7 2.000000
1.1 2.121212
4.0 3.000000
```
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| null | 7 | 2014-06-24T01:04:15Z | 2014-06-24T15:49:29Z | 2014-06-24T15:46:41Z | NONE | null | I am puzzled by the following
```
logger.debug('Indexes are identical: {}'.format(data_frame.index is categories.index))
logger.debug('Index is unique: {}'.format(data_frame.index.is_unique))
logger.debug('All columns are in data_frame: {}'.format(all(map(lambda x: x in data_frame, columns_regression))))
df_regression = data_frame[columns_regression].join(categories)
```
```
2014-06-23 21:02:56,125 DEBUG pmt.portfolio_analytics.attribution.AlphaData - Indexes are identical: True
2014-06-23 21:02:56,141 DEBUG pmt.portfolio_analytics.attribution.AlphaData - Index is unique: True
2014-06-23 21:02:56,142 DEBUG pmt.portfolio_analytics.attribution.AlphaData - All columns are in data_frame: True
Traceback (most recent call last):
File "C:/Users/BKovtunenko/Perforce/bos-perforce01_1666/bkovtunenko_BKOVTUNENKO-D7/aam/branches/boris/pmt/python/pmt/portfolio_analytics/attribution/AlphaData.py", line 1782, in <module>
thresholds_mkt_cap=thresh_mkt_cap)
File "C:/Users/BKovtunenko/Perforce/bos-perforce01_1666/bkovtunenko_BKOVTUNENKO-D7/aam/branches/boris/pmt/python/pmt/portfolio_analytics/attribution/AlphaData.py", line 1640, in save_regression_results
regression_type=regresion_type)
File "C:/Users/BKovtunenko/Perforce/bos-perforce01_1666/bkovtunenko_BKOVTUNENKO-D7/aam/branches/boris/pmt/python/pmt/portfolio_analytics/attribution/AlphaData.py", line 1489, in save_regression_decomposition
df_regression = data_frame[columns_regression].join(categories)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 3580, in join
rsuffix=rsuffix, sort=sort)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 3594, in _join_compat
suffixes=(lsuffix, rsuffix), sort=sort)
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 40, in merge
return op.get_result()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 189, in get_result
ldata, rdata = self._get_merge_data()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 284, in _get_merge_data
copydata=False)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 3439, in _maybe_rename_join
to_rename = self.items.intersection(other.items)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 962, in intersection
indexer = self.get_indexer(other.values)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 1120, in get_indexer
raise InvalidIndexError('Reindexing only valid with uniquely'
pandas.core.index.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
Process finished with exit code 1
```
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} | 11 | 2014-06-24T08:48:52Z | 2014-06-25T14:14:08Z | 2014-06-24T13:32:37Z | NONE | null | I have seen a dramatic (around 100x) slowdown of `groupby.first` method for `datetime64` type after updating to 0.14.
Here is an example:
``` python
from time import time
d = pd.DataFrame({'a': pd.date_range('1/1/2011', periods=100000, freq='s'), 'b': range(100000)})
t = time()
d.groupby('b').first()
print(time() - t)
```
Result:
```
3.87164497375
```
Same for `int` type
``` python
from time import time
d = pd.DataFrame({'a': range(100000), 'b': range(100000)})
t = time()
d.groupby('b').first()
print(time() - t)
```
Result:
```
0.0148799419403
```
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} | 3 | 2014-06-24T08:49:09Z | 2014-06-25T05:01:27Z | 2014-06-25T05:01:27Z | CONTRIBUTOR | null | closes #7426
As suggested by @jreback in #7426, I've added a quick cookbook recipe for emulating R's `expand.grid()`, which creates a dataframe from every combination of the values you give it.
None of the existing sections really seemed to apply, so I've added it in a new section.
Example usage:
``` python
import itertools
import pandas as pd
def expand_grid(data_dict):
rows = itertools.product(*data_dict.values())
return pd.DataFrame.from_records(rows, columns=data_dict.keys())
df = expand_grid(
{'height': [60, 70],
'weight': [100, 140, 180],
'sex': ['Male', 'Female']}
)
df
Out[2]:
sex weight height
0 Male 100 60
1 Male 100 70
2 Male 140 60
3 Male 140 70
4 Male 180 60
5 Male 180 70
6 Female 100 60
7 Female 100 70
8 Female 140 60
9 Female 140 70
10 Female 180 60
11 Female 180 70
```
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} | 12 | 2014-06-24T12:20:47Z | 2015-11-14T05:26:39Z | 2015-05-09T16:08:45Z | MEMBER | null | Related to #7485, `isnull` and `notnull` is required to be changed.
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} | 2 | 2014-06-24T12:23:01Z | 2016-10-12T23:04:58Z | 2014-06-24T12:30:34Z | CONTRIBUTOR | null | ```
In[31]: s2 = pd.Series(["a","z",np.nan])
In[32]: s2.max()
Out[32]: 'z'
In[33]: s2.min()
Out[33]: inf
In[34]: s2.min(skipna=True)
Out[34]: inf
```
Reading the docs I get the impression that it should return "a" in both cases (as skipna defaults to True).
If so, #7217 also needs to change.
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} | 0 | 2014-06-24T12:37:37Z | 2014-06-30T11:05:58Z | 2014-06-30T11:05:58Z | CONTRIBUTOR | null | This is a vbench/groupby.py
```
In [1]: df = DataFrame(np.random.randint(1, 100, (10000, 2)))
In [2]: df[1].groupby(df[0]).nth(0)
IndexError: index 9999 is out of bounds for size 9999
```
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} | 0 | 2014-06-24T13:13:05Z | 2014-06-24T13:32:37Z | 2014-06-24T13:32:37Z | CONTRIBUTOR | null | closes #7555
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_mixed_first | 10.8150 | 3900.5923 | 0.0028 |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [bc5599a] : PERF: vbench for mixed groupby with datetime (GH7555)
PERF: perform coercing and casting on groupby agg by blocks
Base [2bd4517] : Merge pull request #7544 from sinhrks/dtmixin
```
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} | 1 | 2014-06-24T14:15:47Z | 2014-06-25T14:50:17Z | 2014-06-25T14:50:17Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/28313605
@cpcloud
cc @dstephens99
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} | 1 | 2014-06-24T14:46:40Z | 2017-11-22T03:28:03Z | 2014-06-24T14:50:40Z | NONE | null | I found that when using pandas.concat on dataframes with timezone information, the timezone might be lost when using 'innder' join:
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: from pandas.util.print_versions import show_versions
In [4]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-0.bpo.4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
In [5]: dr1 = pd.date_range('20110101 0000','20110101 2359',freq='T',tz='Asia/Tokyo')
In [6]: dr2 = pd.date_range('20110101 0000','20110102 2359',freq='T',tz='Asia/Tokyo')
In [7]: df1 = pd.DataFrame(np.random.randn(5,1440),columns=dr1)
In [8]: df2 = pd.DataFrame(np.random.randn(5,2880),columns=dr2)
In [9]: outer = pd.concat([df1,df2],join='outer')
In [10]: inner = pd.concat([df1,df2],join='inner')
In [11]: print outer.axes[1]
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00+09:00, ..., 2011-01-02 23:59:00+09:00]
Length: 2880, Freq: T, Timezone: Asia/Tokyo
In [12]: print inner.axes[1]
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-12-31 15:00:00, ..., 2011-01-01 14:59:00]
Length: 1440, Freq: T, Timezone: None
```
Looking at the output [11], the timezone information is kept after concat(), however, if we use 'inner' join, the timezone information is lost!
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} | 3 | 2014-06-24T18:34:15Z | 2021-04-11T04:51:12Z | null | CONTRIBUTOR | null | I was very happy to see pr #5879, allowing disabling the auto-regex feature for functions in the .str namespace. Doing something about this has been on my todo list for a while. Automatic promotion of strings to regular expressions is one of the two big points of difficulty for beginners using pandas (in my experience anyway). The most obvious problem comes up when you have literal dollar signs you wish to operate on.
I'd like to argue that the faster, less confusing `regex=False` be the default.
This is a trivial thing to do, and if folks are willing to accept it, I'm happy to submit a pull request.
I also ran into this problem when using unicode delimiters with the read_\* family of functions:
```
ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.
```
This is a another case where we're losing performance and increasing confusion. So, I'd like to expand this option elsewhere, and could submit this all as one pull request, or two (if it seems reasonable).
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} | 3 | 2014-06-25T02:48:04Z | 2014-06-25T14:50:24Z | 2014-06-25T14:50:17Z | CONTRIBUTOR | null | Also moved no tables found error to get_options_tables method (url was not defined in the get_option_data method).
Fixes #7561
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} | 30 | 2014-06-25T04:06:18Z | 2014-09-22T01:40:54Z | 2014-09-20T08:53:31Z | CONTRIBUTOR | null | This patch is an implementation of a `pandas.io.excel.ExcelWriter` subclass that is compatible with the style interface of `openpyxl` 2.0.0 or later.
closes #7177
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} | 2 | 2014-06-25T08:09:09Z | 2014-09-21T19:14:23Z | 2014-09-21T19:09:31Z | MEMBER | null | From http://stackoverflow.com/questions/24389580/pandas-to-sql-method-gives-error-with-date-column
```
import sqlite3
con = sqlite3.connect(':memory:')
df = pd.DataFrame({'date':pd.date_range('2014-01-01', periods=10)})
df.to_sql('test_datetime', con, index=False, if_exists='append')
```
gives an `InterfaceError: Error binding parameter 0 - probably unsupported type.`. While it does work if you have `datetime.datetime/date` objects (instead of `datetime64`):
```
df2 = df.copy()
df2['date'] = pd.DatetimeIndex(df2['date']).date
df2.to_sql('test_datetime', con, index=False, if_exists='append')
```
and it does also work with a sqlalchemy engine (sqlalchemy converts it to a string):
```
import sqlalchemy
engine = sqlalchemy.create_engine('sqlite:///memory')
df.to_sql('test_datetime', engine, index=False, if_exists='append')
```
I think this should also work in the fallback mode, options are to convert it to `datetime.datetime` objects and let `sqlite3` do the work to convert it to a string, or to convert it to a string directly ourselves.
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```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_object_last | 15.2340 | 4175.6846 | 0.0036 |
groupby_object_first | 15.6957 | 4166.6920 | 0.0038 |
groupby_transform_ufunc | 5.4990 | 5.7740 | 0.9524 |
groupby_indices | 6.7443 | 7.0153 | 0.9614 |
groupby_multi_different_functions | 12.6866 | 12.9040 | 0.9832 |
groupby_multi_series_op | 13.2560 | 13.4466 | 0.9858 |
groupby_multi_cython | 15.0924 | 15.2720 | 0.9882 |
groupby_multi_different_numpy_functions | 11.1883 | 11.2376 | 0.9956 |
groupby_first_float32 | 3.4907 | 3.4963 | 0.9984 |
groupby_last_float32 | 3.6677 | 3.6586 | 1.0025 |
groupby_frame_apply_overhead | 9.2754 | 9.2440 | 1.0034 |
groupby_first | 3.5706 | 3.5577 | 1.0036 |
groupby_pivot_table | 17.2007 | 17.1343 | 1.0039 |
groupby_transform | 174.6823 | 173.8520 | 1.0048 |
groupby_frame_singlekey_integer | 2.4533 | 2.4397 | 1.0056 |
groupby_frame_nth | 2.7863 | 2.7630 | 1.0085 |
groupby_simple_compress_timing | 35.6836 | 35.3360 | 1.0098 |
groupby_sum_booleans | 1.2880 | 1.2727 | 1.0121 |
groupby_transform2 | 162.5650 | 160.2323 | 1.0146 |
groupby_apply_dict_return | 39.7460 | 39.1597 | 1.0150 |
groupby_multi_size | 23.2656 | 22.9087 | 1.0156 |
groupby_datetimes_last | 12.0660 | 11.8797 | 1.0157 |
groupby_series_simple_cython | 192.2614 | 188.9360 | 1.0176 |
groupby_last | 3.8017 | 3.7254 | 1.0205 |
groupby_int_count | 4.6777 | 4.5680 | 1.0240 |
groupby_mixed_first | 12.3363 | 12.0014 | 1.0279 |
groupby_frame_median | 7.6403 | 7.4034 | 1.0320 |
groupby_frame_apply | 45.0197 | 43.6123 | 1.0323 |
groupby_object_nth | 430.5693 | 416.2673 | 1.0344 |
groupby_datetimes_nth | 428.6557 | 414.3833 | 1.0344 |
groupby_frame_cython_many_columns | 4.3843 | 4.2194 | 1.0391 |
groupby_multi_python | 142.0707 | 135.8123 | 1.0461 |
groupby_nth_float32 | 63.5343 | 59.9463 | 1.0599 |
groupby_nth_float64 | 64.0316 | 59.8179 | 1.0704 |
groupby_multi_count | 9.0540 | 8.2114 | 1.1026 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [e28ec0d] : BUG/PERF: perf issues in object groupby aggregations (GH7555)
Base [69bb0e8] : Merge pull request #7556 from onesandzeroes/expand-grid
DOC: Cookbook recipe for emulating R's expand.grid() (#7426)
```
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} | 1 | 2014-06-25T13:09:30Z | 2018-07-06T23:00:53Z | 2018-07-06T23:00:47Z | CONTRIBUTOR | null | see #7568
- `nth` is a fair bit slower than `first/last` which are calling cython routines. In a case where you don't `dropna` you can simply call the cython aggregation routines
- side issue is to move the `group_last_object/group_nth_object` from `algos.pyx` to `generate_code.py` (simply move `group_last/group_nth` from the `groupby` template to the same as `group_count` template, which generates the object dtypes)
- trap and reraise an error like `ValueError buffer type mismatch` in the cython trials. This is generated when a built in routine tries to use the cython routines, but the function is not defined (but it SHOULD be defined for all dtypes), so this is a trapped bug (in which case it goes to the python path).
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} | 7 | 2014-06-25T13:46:48Z | 2021-04-11T04:51:39Z | null | CONTRIBUTOR | null | alias `len/size` to `groupby.transform` speedup table (as these are already implemented as `size`,`count`` in cython, just a matter of transforming them
see here: http://stackoverflow.com/questions/24438273/aggregation-on-pandas-datetime-series-only-returns-as-datetime-series/24438366#24438366
(`len` is suspect for `.agg` as well)
so
```
g = df.groupby(...)
g.transform(len)
g.transform('size')
g.transform('len')
```
will do the same
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| null | 5 | 2014-06-25T20:02:34Z | 2014-06-25T22:10:44Z | 2014-06-25T22:08:36Z | NONE | null | See
http://stackoverflow.com/questions/24416140/long-integer-values-in-pandas-dataframe-change-when-sent-to-sqlite-database-usin?noredirect=1#comment37772983_24416140
I am using pandas to organize and manipulate data I am getting from the twitter API. The 'id' key returns a very long integer (int64) that pandas has no problem handling (i.e. 481496718320496643).
However, when I send to SQL:
df.to_sql('Tweets', conn, flavor='sqlite', if_exists='append', index=False)
I now have tweet id: 481496718320496640 or something close to that number.
I converted the tweet id to str but Pandas SQLite Driver / SQLite still messes with the number. The data type in the SQLite database is [tweet_id] INTEGER. This seems to be an issue.
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} | 8 | 2014-06-26T04:10:41Z | 2014-07-02T14:53:03Z | 2014-07-02T11:46:29Z | CONTRIBUTOR | null | fixes #7297
Other rolling window functions rely on the logic in `_check_minp`, and split the iteration into the ranges `[0, minp-1)` and `(minp-1, N)`. Because of the way `rolling_var` handles things, splitting the iteration into `[0, win)` and `(win, N)` makes more sense. Added also some comments as to what is going on elsewhere in the code.
No tests have been added, as @kdiether seems to have that very advanced.
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} | 1 | 2014-06-26T07:33:14Z | 2014-09-16T06:14:26Z | 2014-09-16T06:14:26Z | MEMBER | null | See http://stackoverflow.com/questions/24408557/pandas-read-sql-with-parameters.
Add a note mentioning this is driver-dependent which works, and they have to look at their drivers documentation.
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} | 2 | 2014-06-26T13:28:07Z | 2014-07-02T16:04:34Z | 2014-07-02T16:04:34Z | CONTRIBUTOR | null | Suppose I have this simple DataFrame:
``` python
In [91]: df = pd.DataFrame({'ref':[1, 2, 3], 'symbol':['GOOGL', 'IBM', 'MSFT'], 'price':[585.93, 180.72, 42.03]}, columns=['ref', 'symbol', 'price'])
In [92]: df
Out[92]:
ref symbol price
0 1 GOOGL 585.93
1 2 IBM 180.72
2 3 MSFT 42.03
In [93]: df.dtypes
Out[93]:
ref int64
symbol object
price float64
dtype: object
```
If I aggregate over an object and then reset the index, everything comes back as expected:
``` python
In [95]: df.groupby('symbol').first().reset_index()
Out[95]:
symbol ref price
0 GOOGL 1 585.93
1 IBM 2 180.72
2 MSFT 3 42.03
```
Similarly with aggregating over an integer:
``` python
In [96]: df.groupby('ref').first().reset_index()
Out[96]:
ref symbol price
0 1 GOOGL 585.93
1 2 IBM 180.72
2 3 MSFT 42.03
```
Now let's say I have an empty DataFrame. Aggregating over an object produces what I expect:
``` python
In [97]: df.query('price > 1000').groupby('symbol').first().reset_index()
Out[97]:
Empty DataFrame
Columns: [symbol, ref, price]
Index: []
```
But aggregating over an integer gives me `index` as the column name instead of the expected `ref`!
``` python
In [98]: df.query('price > 1000').groupby('ref').first().reset_index()
Out[98]:
Empty DataFrame
Columns: [index, symbol, price]
Index: []
^^^^^
```
Interestingly, setting the index in a non-aggregating function does the right thing:
``` python
In [100]: df.query('price > 1000').set_index('ref').reset_index()
Out[100]:
Empty DataFrame
Columns: [ref, symbol, price]
Index: []
```
So aggregating over an integer in an empty DataFrame removes the index name. This was discovered in pandas 0.14.0.
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} | 8 | 2014-06-26T13:46:36Z | 2014-09-04T01:33:38Z | 2014-09-04T01:32:36Z | CONTRIBUTOR | null | This accompanies #7470.
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} | 3 | 2014-06-26T15:02:17Z | 2021-04-11T04:51:56Z | null | CONTRIBUTOR | null | The `column_space` option doesn't seem to work.
```
In [28]: df=pd.DataFrame(np.array([np.random.randn(6), np.random.randint(1,9,6)*.1,
np.zeros(6)]).T, columns=['A', 'B', 'C'], dtype='float')
In [29]: pd.set_option('column_space', 12)
In [30]: df
Out[30]:
A B C
0 0.865356 0.1 0
1 -0.968568 0.2 0
2 -0.584314 0.8 0
3 0.347676 0.6 0
4 0.296440 0.8 0
5 -0.129957 0.2 0
In [31]: pd.set_option('column_space', 2)
In [32]: df
Out[32]:
A B C
0 0.865356 0.1 0
1 -0.968568 0.2 0
2 -0.584314 0.8 0
3 0.347676 0.6 0
4 0.296440 0.8 0
5 -0.129957 0.2 0
```
In `frame.py`, the option seems to be named `col_space` but the doc string wasn't updated in `config_init.py`. Happy to PR this, but, what is the agreed upon name/behavior? I like `col_space`? Does anyone have any background/history?
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| null | 1 | 2014-06-26T16:04:45Z | 2015-10-20T22:26:11Z | 2015-10-20T22:26:11Z | CONTRIBUTOR | null | The option `date_dayfirst` doesn't function as expected.
```
In [24]: df=pd.DataFrame(np.random.randn(5,5), index=pd.date_range('1/15/2000',periods=5, freq='D'))
In [25]: pd.set_option('date_dayfirst', True)
In [26]: df
Out[26]:
0 1 2 3 4
2000-01-15 0.487978 -0.224156 0.784338 0.473189 -0.661636
2000-01-16 1.090355 -1.211436 1.123339 -1.145785 -1.856864
2000-01-17 1.305624 -0.628171 -0.901822 -1.652694 0.290757
2000-01-18 -0.370491 -0.138887 0.393568 -1.489696 0.288056
2000-01-19 -0.169596 -0.585463 1.221087 0.226034 -0.677756
In [27]: pd.set_option('date_dayfirst', False)
In [28]: df
Out[28]:
0 1 2 3 4
2000-01-15 0.487978 -0.224156 0.784338 0.473189 -0.661636
2000-01-16 1.090355 -1.211436 1.123339 -1.145785 -1.856864
2000-01-17 1.305624 -0.628171 -0.901822 -1.652694 0.290757
2000-01-18 -0.370491 -0.138887 0.393568 -1.489696 0.288056
2000-01-19 -0.169596 -0.585463 1.221087 0.226034 -0.677756
```
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} | 14 | 2014-06-26T18:54:54Z | 2015-01-17T05:24:58Z | 2014-07-07T07:08:31Z | CONTRIBUTOR | null | closes #6838.
closes #3104
Breakout "options" section for docs, heavily using info already in `basics.rst`. Expands examples, and turns `describe_options` into a table.
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related #7287
And some improvements when using any/all for nth
(still the nth are 10x slower than the cythonized first/last, but that's another issue)
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_nth_object_any | 503.6680 | 17163.4649 | 0.0293 |
groupby_nth_datetimes_any | 553.3033 | 13455.8423 | 0.0411 |
groupby_series_nth_any | 2.7003 | 26.1161 | 0.1034 |
groupby_frame_nth_any | 5.4483 | 15.4017 | 0.3537 |
groupby_multi_different_functions | 11.3816 | 12.2033 | 0.9327 |
groupby_multi_different_numpy_functions | 10.4934 | 11.1040 | 0.9450 |
groupby_object_nth | 397.3277 | 416.7441 | 0.9534 |
groupby_pivot_table | 16.2361 | 16.9867 | 0.9558 |
groupby_frame_apply_overhead | 8.8013 | 9.1234 | 0.9647 |
groupby_multi_python | 129.7946 | 133.2393 | 0.9741 |
groupby_apply_dict_return | 37.9413 | 38.7277 | 0.9797 |
groupby_frame_apply | 41.9403 | 42.6593 | 0.9831 |
groupby_object_first | 15.0506 | 15.2097 | 0.9895 |
groupby_frame_cython_many_columns | 3.3430 | 3.3733 | 0.9910 |
groupby_nth_float64 | 51.4960 | 51.9506 | 0.9912 |
groupby_frame_nth | 2.7520 | 2.7746 | 0.9918 |
groupby_multi_size | 21.6714 | 21.8496 | 0.9918 |
groupby_transform2 | 159.0919 | 160.3904 | 0.9919 |
groupby_indices | 6.2350 | 6.2837 | 0.9923 |
groupby_multi_cython | 13.9600 | 14.0460 | 0.9939 |
groupby_frame_median | 6.1007 | 6.1367 | 0.9941 |
groupby_sum_booleans | 1.2400 | 1.2473 | 0.9941 |
groupby_datetimes_last | 10.7520 | 10.8067 | 0.9949 |
groupby_series_simple_cython | 178.8233 | 179.7036 | 0.9951 |
groupby_nth_float32 | 52.6213 | 52.8786 | 0.9951 |
groupby_multi_series_op | 12.5150 | 12.5673 | 0.9958 |
groupby_transform | 166.5533 | 167.0943 | 0.9968 |
groupby_frame_singlekey_integer | 2.3321 | 2.3394 | 0.9969 |
groupby_multi_count | 8.7133 | 8.7360 | 0.9974 |
groupby_last_float32 | 3.5094 | 3.5140 | 0.9987 |
groupby_transform_ufunc | 6.1913 | 6.1987 | 0.9988 |
groupby_last | 3.5419 | 3.5423 | 0.9999 |
groupby_first | 3.3600 | 3.3563 | 1.0011 |
groupby_object_last | 14.8129 | 14.7574 | 1.0038 |
groupby_int_count | 4.4247 | 4.4080 | 1.0038 |
groupby_first_float32 | 3.3464 | 3.3300 | 1.0049 |
groupby_mixed_first | 10.9203 | 10.8653 | 1.0051 |
groupby_datetimes_nth | 445.6376 | 423.4857 | 1.0523 |
groupby_simple_compress_timing | 33.8430 | 27.2744 | 1.2408 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [def0155] : BUG/TST: fix tests for groupby nth on Series (GH7559)
Base [4082c1a] : Merge pull request #7593 from jreback/timedelta_nat
BUG: Bug in timedelta inference when assigning an incomplete Series (GH7592)
```
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} | 3 | 2014-06-26T20:01:57Z | 2014-07-05T13:49:31Z | 2014-07-05T13:49:29Z | MEMBER | null | Closes #7396
For now, let PandasSQLAlchemy not reflect anything by default, and `read_sql_table` provides its own reflected meta. Had to change some of the other functions to get this working.
@danielballan @mangecoeur Can someone review? (it feeled a bit messy)
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} | 13 | 2014-06-26T20:32:33Z | 2014-08-27T15:01:32Z | 2014-06-30T19:26:24Z | CONTRIBUTOR | null | This is the first part of #7470.
closes #2685
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| null | 5 | 2014-06-27T00:30:59Z | 2014-06-27T03:45:50Z | 2014-06-27T01:24:02Z | NONE | null | I noticed this behavior, not sure it's a bug.
I create a dataframe with 2 integer columns and 1 float column
``` python
import pandas as pd
df = pd.DataFrame([[1,2,0.2],[3,2,0.1]])
df.info()
```
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 3 columns):
0 2 non-null int64
1 2 non-null int64
2 2 non-null float64
dtypes: float64(1), int64(2)
```
If I output that to Json, the dtype information is lost:
``` python
df.to_json(orient= 'records')
```
```
'[{"0":1.0,"1":2.0,"2":0.2},{"0":3.0,"1":2.0,"2":0.1}]'
```
All data is converted to float. This is a problem if for example one column contains ns timestamps, because they are converted to exponential notation and the sub-second information is lost.
Can you help?
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} | 9 | 2014-06-27T04:38:21Z | 2018-07-06T22:43:18Z | 2018-07-06T22:43:14Z | CONTRIBUTOR | null | Is it possible to get the unique values from a chosen column from a HDF file? That would be great feature to have this happening on disk.
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| null | 4 | 2014-06-27T06:17:46Z | 2014-06-27T18:36:29Z | 2014-06-27T10:15:29Z | CONTRIBUTOR | null | It seems to be possible to create a broken object by assigning to a slice of a column of dtype `timedelta64`. In this object, there seem to be two separate copies of a column, one of which is obtained when that column is selected _by name_ and the other of which is visible to all of the `DataFrame`'s other methods.
``` python
import numpy as np
import pandas as pd
temp = []
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64('NaT')},
index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.timedelta64(1000000000)},
index=pd.date_range('2014-1-1', periods=4, freq='D')))
temp.append(pd.DataFrame({'A': np.arange(4) + np.nan},
index=np.arange(4)))
# broken, see below
temp[0]['A'][:-1] = np.diff(temp[0].index.values)
# using a series instead of an ndarray does not change the behavior
temp[1]['A'][:-1] = pd.Series(np.diff(temp[1].index.values), index=temp[1].index[:-1])
# avoiding chained assignment *does* change the behavior
temp[2]['A'] = pd.Series(np.diff(temp[2].index.values), index=temp[2].index[:-1])
# avoiding NaT does not change the behavior
temp[3]['A'][:-1] = np.diff(temp[3].index.values)
# avoiding timedeltas *does* change the behavior
temp[4]['A'][:-1] = np.diff(temp[4].index.values)
for idx, x in enumerate(temp):
print "temp[%d]:" %idx
print x
print x['A']
print x.A
print x.loc[:,'A']
print x.iloc[:,0]
print x.iloc[0,:].loc['A']
print x.loc[:,'A'].iloc[0]
# temp[0]:
# A
#2014-01-01 NaT
#2014-01-02 NaT
#2014-01-03 NaT
#2014-01-04 NaT
#
# [4 rows x 1 columns]
#2014-01-01 1 days
#2014-01-02 1 days
#2014-01-03 1 days
#2014-01-04 NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01 1 days
#2014-01-02 1 days
#2014-01-03 1 days
#2014-01-04 NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01 1 days
#2014-01-02 1 days
#2014-01-03 1 days
#2014-01-04 NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
#2014-01-01 NaT
#2014-01-02 NaT
#2014-01-03 NaT
#2014-01-04 NaT
# Freq: D, Name: A, dtype: timedelta64[ns]
# NaT
#86400000000000 nanoseconds
# ...
pd.show_versions()
#
# INSTALLED VERSIONS
# ------------------
# commit: None
# python: 2.7.5.final.0
# python-bits: 64
# OS: Darwin
# OS-release: 13.2.0
# machine: x86_64
# processor: i386
# byteorder: little
# LC_ALL: None
# LANG: en_US.UTF-8
#
# pandas: 0.13.1
# Cython: None
# numpy: 1.8.0
# scipy: 0.12.1
# statsmodels: None
# IPython: 1.1.0
# sphinx: None
# patsy: None
# scikits.timeseries: None
# dateutil: 2.2
# pytz: 2013.9
# bottleneck: None
# tables: None
# numexpr: None
# matplotlib: 1.3.0
# openpyxl: None
# xlrd: None
# xlwt: None
# xlsxwriter: None
# sqlalchemy: None
# lxml: None
# bs4: None
# html5lib: None
# bq: None
# apiclient: None
```
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} | 8 | 2014-06-27T11:49:38Z | 2016-10-12T23:04:58Z | 2014-06-27T19:35:09Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/24446500/pandas-valueerror-when-assigning-dataframe-entries-using-index-due-to-a-change
```
In [2]: A = pd.DataFrame(np.random.rand(10,4), index=np.random.rand(10))
In [3]: A.loc[A.index] = A.loc[A.index]
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 1 | 2014-06-27T13:30:21Z | 2014-06-27T19:35:10Z | 2014-06-27T19:35:09Z | MEMBER | null | closes #7586
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} | 3 | 2014-06-27T13:53:37Z | 2018-06-01T15:20:41Z | 2016-04-11T15:03:57Z | NONE | null | Currently Akima is not a valid interpolation method in Pandas. However scipy does have Akima1Dinterpolator. As Pandas uses SciPy for doing interpolation, adding the Akima method to pandas should be easy.
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} | 8 | 2014-06-27T14:20:31Z | 2018-07-25T12:05:57Z | 2018-07-25T12:05:56Z | CONTRIBUTOR | null | Using a `namedtuple` as a column name for `read_csv` in Pandas 0.14 results `NaNs` being loaded.
Here is a simple demonstration of the problem (this code works in Pandas 0.13.1):
``` python
import pandas as pd
from collections import namedtuple
from StringIO import StringIO
TestTuple = namedtuple('test', ['a'])
CSV = """10
20
30"""
pd.read_csv(StringIO(CSV), header=None, names=[TestTuple('foo')],
tupleize_cols=True)
```
Pandas 0.14, this is the output:
```
(foo,)
0 NaN
1 NaN
2 NaN
```
Strangely enough, Pandas 0.14 works fine if we used a tuple instead of a namedtuple:
``` python
pd.read_csv(StringIO(CSV), header=None, names=[('foo')], tupleize_cols=False)
```
Here is the output:
```
foo
0 10
1 20
2 30
```
So, for some reason, `read_csv` in Pandas 0.14 doesn't like using a `namedtuple` as a column name. (The ugly fix is to not pass any column names to `read_csv` and then, once the DataFrame is loaded, replace the column names with `df.columns = [TestTuple('foo')]`)
(Really love Pandas by the way, thanks so much for all your work!)
My software versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
rpy2: 2.3.8
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/7590 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/7590/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/7590/comments | https://api.github.com/repos/pandas-dev/pandas/issues/7590/events | https://github.com/pandas-dev/pandas/issues/7590 | 36,672,814 | MDU6SXNzdWUzNjY3MjgxNA== | 7,590 | Pandas 14.0 Dataframe Clears sys.exc_info() When pprinted | {
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| null | 9 | 2014-06-27T15:03:38Z | 2015-10-20T22:26:37Z | 2015-10-20T22:26:37Z | NONE | null | I am running into an issue with error handing in my current project after upgrading to pandas 14.0. The issue is that when I use pprint to render the dataframe as part of the traceback message, it for some reason silently clobbers the sys.exc_info(), which makes it impossible to re-raise the error after my processing using a 'raise' command with no arguments. To reproduce (using the iris.csv from tests/data):
```
import pandas as pd
import sys
from pprint import pprint
df = pd.DataFrame.from_csv("./data/iris.csv")
try:
raise KeyError()
except:
# info is populated correctly here
print sys.exc_info()
pprint(df)
# info is now (None, None, None)
print sys.exc_info()
# trying to re-raise error causes TypeError as it is trying to raise 'None'
raise
```
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.0
nose: 1.3.3
Cython: None
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: 0.91.3
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 1 | 2014-06-27T18:25:31Z | 2014-06-30T21:05:10Z | 2014-06-30T19:26:49Z | CONTRIBUTOR | null | Closes #6893.
Currently the Python parser can read data with the index columns specified on the first line after the header, e.g.
``` python
In [3]: pd.__version__
Out[3]: '0.14.0-271-gf8b101c'
In [4]: text = """ A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""
In [5]: pd.read_table(StringIO(text), sep='\s+', engine='python')
Out[5]:
A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
q 20.0000 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30.0000 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838
```
but the C parser fails:
``` python
In [6]: pd.read_table(StringIO(text), sep='\s+', engine='c')
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
. . .
CParserError: Error tokenizing data. C error: Expected 5 fields in line 3, saw 9
```
This PR patches the C parser to enable this feature.
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} | 4 | 2014-06-27T18:31:42Z | 2014-06-27T20:44:54Z | 2014-06-27T20:44:03Z | CONTRIBUTOR | null | When assigning a `timedelta64` array to a subset of a new column of a `DataFrame`, missing data is not filled with `NaT` as expected; rather, the new column is cast to `float64` and `NaN` is used instead. This cast does not usually occur when all values are present, _except_ when there are already `float64` columns but no `timedelta64` columns in the `DataFrame` _and_ indexing is done through `.ix` or `.loc`.
It's possible these should be two separate issues.
There are a lot of issues involving `NaT` in the issue tracker; I'm not 100% sure that this isn't a duplicate. (Nor am I 100% sure this isn't intended behavior, but if it is I'd expect it to be documented more prominently.)
``` python
import numpy as np
import pandas as pd
one_hour = 60*60*10**9
temp = pd.DataFrame({}, index=pd.date_range('2014-1-1', periods=4))
temp['A'] = np.array([1*one_hour]*4, dtype='m8[ns]')
temp.loc[:,'B'] = np.array([2*one_hour]*4, dtype='m8[ns]')
temp.loc[:3,'C'] = np.array([3*one_hour]*3, dtype='m8[ns]')
temp.ix[:,'D'] = np.array([4*one_hour]*4, dtype='m8[ns]')
temp.ix[:3,'E'] = np.array([5*one_hour]*3, dtype='m8[ns]')
temp['F'] = np.timedelta64('NaT')
temp.ix[:-1,'F'] = np.array([6*one_hour]*3, dtype='m8[ns]')
temp
# A B C D E F
#2014-01-01 01:00:00 02:00:00 1.080000e+13 04:00:00 1.800000e+13 06:00:00
#2014-01-02 01:00:00 02:00:00 1.080000e+13 04:00:00 1.800000e+13 06:00:00
#2014-01-03 01:00:00 02:00:00 1.080000e+13 04:00:00 1.800000e+13 06:00:00
#2014-01-04 01:00:00 02:00:00 NaN 04:00:00 NaN NaT
#
# [4 rows x 6 columns]
temp = pd.DataFrame({}, index=pd.date_range('2014-1-1', periods=4))
# Partial assignment converts
temp.ix[:-1,'A'] = np.array([1*one_hour]*3, dtype='m8[ns]')
# DataFrame is all floats; converts
temp.ix[:,'B'] = np.array([2*one_hour]*4, dtype='m8[ns]')
# .ix and .loc behave the same
temp.loc[:,'C'] = np.array([3*one_hour]*4, dtype='m8[ns]')
# straight column assignment doesn't convert
temp['D'] = np.array([4*one_hour]*4, dtype='m8[ns]')
# Now there are timedeltas; doesn't convert
temp.ix[:,'E'] = np.array([5*one_hour]*4, dtype='m8[ns]')
# .ix and .loc still behave the same
temp.loc[:,'F'] = np.array([6*one_hour]*4, dtype='m8[ns]')
temp
# A B C D E \
#2014-01-01 3.600000e+12 7.200000e+12 1.080000e+13 04:00:00 05:00:00
#2014-01-02 3.600000e+12 7.200000e+12 1.080000e+13 04:00:00 05:00:00
#2014-01-03 3.600000e+12 7.200000e+12 1.080000e+13 04:00:00 05:00:00
#2014-01-04 NaN 7.200000e+12 1.080000e+13 04:00:00 05:00:00
#
# F
#2014-01-01 06:00:00
#2014-01-02 06:00:00
#2014-01-03 06:00:00
#2014-01-04 06:00:00
#
# [4 rows x 6 columns]
temp = pd.DataFrame({}, index=pd.date_range('2014-1-1', periods=4))
# No columns yet, no conversion
temp.ix[:,'A'] = np.array([2*one_hour]*4, dtype='m8[ns]')
# A
#2014-01-01 02:00:00
#2014-01-02 02:00:00
#2014-01-03 02:00:00
#2014-01-04 02:00:00
#
# [4 rows x 1 columns]
```
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} | 5 | 2014-06-27T21:18:31Z | 2014-06-28T02:09:17Z | 2014-06-28T01:38:16Z | CONTRIBUTOR | null | I just upgraded to 0.14 and am seeing an inconsistent behavior when converting datetimes to objects. This used to be consistent in 0.12.1 and now it loses timezone information when converting to datetime64. Is this expected?
```
import pytz
import datetime
from pandas import DataFrame
tz = pytz.timezone('US/Eastern')
dt = tz.localize(datetime.datetime(2012, 6, 1))
dt
Out[31]: datetime.datetime(2012, 6, 1, 0, 0, tzinfo=<DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>)
df = DataFrame([{'End Date': dt}])
df
Out[26]:
End Date
0 2012-06-01 00:00:00-04:00
df.dtypes
Out[27]:
End Date object
dtype: object
df_c = df.convert_objects(convert_dates=True)
df_c
Out[29]:
End Date
0 2012-06-01 04:00:00
df_c.dtypes
Out[30]:
End Date datetime64[ns]
dtype: object
```
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} | 0 | 2014-06-28T01:09:23Z | 2014-06-28T01:38:17Z | 2014-06-28T01:38:16Z | CONTRIBUTOR | null | closes #7594
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} | 2 | 2014-06-28T01:37:50Z | 2014-06-28T13:59:16Z | 2014-06-28T13:59:16Z | CONTRIBUTOR | null | https://travis-ci.org/jreback/pandas/jobs/28639538
cc @dstephens99
@cpcloud
odd.?
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} | 42 | 2014-06-28T11:48:04Z | 2015-07-20T03:46:52Z | 2015-07-14T15:52:23Z | MEMBER | null | ### Original Description
Allows `DatetimeIndex.__init__` to accept `NaT` string representation.
### Updated Description
The original problem is caused by the inconsistencies in parsing function. Make following results to be consistent.
```
date_str = '2011Q3'
pd.tseries.tools.parse_time_string(date_str)[0]
#2011-07-01 00:00:00
pd.DatetimeIndex([date_str])[0]
#2011-07-01 00:00:00
pd.tseries.tools.to_datetime(date_str)
# 2011Q3 <type str>, not parsed
pd.Timestamp(date_str)
# ValueError: Unable to parse 2011Q3
```
Summary:
- Added `yearfirst` to `to_datetime`.
- Defined private func `_to_datetime`, because `DatetimeIndex` uses `freq` kw to parse 6 character monthly strings (and do not want `to_datetime` to accept `freq`)
- `Timestamp` is left as it is (without `dayfirst` and `yearfirst`)
Also, closes #5418.
closes #10537
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} | 6 | 2014-06-28T11:56:31Z | 2014-07-05T04:47:11Z | 2014-06-30T12:33:49Z | MEMBER | null | Related to #7453 2nd issue:
- `GroupBy.size` created by `TimeGrouper` raises `AttributeError`
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