我尝试使用pandas 0.13.1来减少气象数据。我有一个大的浮动数据帧。感谢this answer我最有效地将数据分组为半小时。我使用groupby + apply代替resample，因为需要检查多个列。
>>> winddata
sonic_Ux sonic_Uy sonic_Uz
TIMESTAMP
2014-04-30 14:13:12.300000 0.322444 2.530129 0.347921
2014-04-30 14:13:12.400000 0.357793 2.571811 0.360840
2014-04-30 14:13:12.500000 0.469529 2.400510 0.193011
2014-04-30 14:13:12.600000 0.298787 2.212599 0.404752
2014-04-30 14:13:12.700000 0.259310 2.054919 0.066324
2014-04-30 14:13:12.800000 0.342952 1.962965 0.070500
2014-04-30 14:13:12.900000 0.434589 2.210533 -0.010147
... ... ...
[4361447 rows x 3 columns]
>>> winddata.dtypes
sonic_Ux float64
sonic_Uy float64
sonic_Uz float64
dtype: object
>>> hhdata = winddata.groupby(TimeGrouper('30T')); hhdata
<pandas.core.groupby.DataFrameGroupBy object at 0xb440790c>
我想在＆＃39; Ux / Uy＆＃39;上使用math.atan2列和成功apply任何功能。我得到关于属性ndim的追溯：
>>> hhdata.apply(lambda g: atan2(g['sonic_Ux'].mean(), g['sonic_Uy'].mean()))
Traceback (most recent call last):
<<snip>>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.1-py2.7-linux-i686.egg/pandas/tools/merge.py", line 989, in __init__
if not 0 <= axis <= sample.ndim:
AttributeError: 'float' object has no attribute 'ndim'
>>>
>>> hhdata.apply(lambda g: 42)
Traceback (most recent call last):
<<snip>>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.1-py2.7-linux-i686.egg/pandas/tools/merge.py", line 989, in __init__
if not 0 <= axis <= sample.ndim:
AttributeError: 'int' object has no attribute 'ndim'
我可以循环遍历groupby对象。我也可以将结果包装在Series或DataFrame中，但是包装值需要添加一个与原始索引元组的索引。遵循this answer的建议删除重复索引并没有按预期工作。由于我可以从该问题重现问题和解决方案，我<罢工>想知道是否认为它的行为有所不同，因为我正在将一个DateTimeIndex 分组为一个索引。
>>> for name, g in hhdata:
... print name, atan2(g['sonic_Ux'].mean(), g['sonic_Uy'].mean()), ' wd'
...
2014-04-30 14:00:00 0.13861912975 wd
2014-04-30 14:30:00 0.511709085506 wd
2014-04-30 15:00:00 -1.5088990774 wd
2014-04-30 15:30:00 0.13200013186 wd
<<snip>>
>>> def winddir(g):
... return pd.Series(atan2( np.mean(g['sonic_Ux']), np.mean(g['sonic_Uy']) ), name='wd')
...
>>> hhdata.apply(winddir)
2014-04-30 14:00:00 0 0.138619
2014-04-30 14:30:00 0 0.511709
2014-04-30 15:00:00 0 -1.508899
2014-04-30 15:30:00 0 0.132000
...
2014-05-05 14:00:00 0 -2.551593
2014-05-05 14:30:00 0 -2.523250
2014-05-05 15:00:00 0 -2.698828
Name: wd, Length: 243, dtype: float64
>>> hhdata.apply(winddir).index[0]
(Timestamp('2014-04-30 14:00:00', tz=None), 0)
>>> def winddir(g):
... return pd.DataFrame({'wd':atan2(g['sonic_Ux'].mean(), g['sonic_Uy'].mean())}, index=[g.name])
...
>>> hhdata.apply(winddir)
wd
2014-04-30 14:00:00 2014-04-30 14:00:00 0.138619
2014-04-30 14:30:00 2014-04-30 14:30:00 0.511709
2014-04-30 15:00:00 2014-04-30 15:00:00 -1.508899
2014-04-30 15:30:00 2014-04-30 15:30:00 0.132000
...
[243 rows x 1 columns]
>>> hhdata.apply(winddir).index[0]
(Timestamp('2014-04-30 14:00:00', tz=None), Timestamp('2014-04-30 14:00:00', tz=None))
>>>
>>> tsfast.groupby(TimeGrouper('30T')).apply(lambda g:
... Series({'wd': atan2(g.sonic_Ux.mean(), g.sonic_Uy.mean()),
... 'ws': np.sqrt(g.sonic_Ux.mean()**2 + g.sonic_Uy.mean()**2)}))
2014-04-30 14:00:00 wd 0.138619
ws 1.304311
2014-04-30 14:30:00 wd 0.511709
ws 0.143762
2014-04-30 15:00:00 wd -1.508899
ws 0.856643
...
2014-05-05 14:30:00 wd -2.523250
ws 3.317810
2014-05-05 15:00:00 wd -2.698828
ws 3.279520
Length: 486, dtype: float64
已编辑：注意返回Series或DataFrame时的额外列？并按照先前链接的答案的公式得出分层索引？
我原来的问题是：应该从apply ed函数返回什么样的值，以便groupby-apply操作产生1列DataFrame或系列，其长度等于组和组的数量用作索引值的名称（例如时间戳）？
反馈后＆amp;进一步调查，我真正要问的是为什么对索引进行分组的行为与对列进行分组的行为不同？观察将DatetimeIndex更改为具有字符串值的列，以实现与之相同的分组TimeGrouper('30T')导致我期待的行为：
>>> winddata.index.name = 'WASINDEX'
>>> data2 = winddata.reset_index()
>>> def to_hh(x): # <-- big hammer
... ts = x.isoformat()
... return ts[:14] + ('30:00' if int(ts[14:16]) >= 30 else '00:00')
...
>>> data2['TS'] = data2['WASINDEX'].apply(lambda x: to_hh(x))
>>> wd = data2.groupby('TS').apply(lambda df: Series({'wd': np.arctan2(df.x.mean(), df.y.mean())}))
>>> type(wd)
pandas.core.frame.DataFrame
>>> wd.columns
Index([u'wd'], dtype=object)
>>> wd.index
Index([u'2014-04-30T14:00:00', u'2014-04-30T14:30:00', <<snip>> dtype=object)
答案 0 :(得分：0)
In [31]: pd.set_option('max_rows',10)
In [32]: winddata = DataFrame({ 'x' : np.random.randn(N), 'y' : np.random.randn(N)+2, 'z' : np.random.randn(N) },pd.date_range('20140430 14:13:12',periods=N,freq='100ms'))
In [33]: winddata
Out[33]:
x y z
2014-04-30 14:13:12 -0.065350 0.567525 2.212534
2014-04-30 14:13:12.100000 -0.436498 2.591799 2.424359
2014-04-30 14:13:12.200000 -1.059038 3.120631 -0.645579
2014-04-30 14:13:12.300000 1.973474 0.630424 0.966405
2014-04-30 14:13:12.400000 0.575082 1.941845 -0.674695
... ... ... ...
2014-05-05 15:22:16.200000 0.601962 0.027834 -0.101967
2014-05-05 15:22:16.300000 0.741777 1.764745 0.991516
2014-05-05 15:22:16.400000 -0.494253 1.765930 2.493000
2014-05-05 15:22:16.500000 -2.643749 0.671604 0.275096
2014-05-05 15:22:16.600000 0.676698 0.958903 0.946942
[4361447 rows x 3 columns]
In [34]: winddata.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4361447 entries, 2014-04-30 14:13:12 to 2014-05-05 15:22:16.600000
Freq: 100L
Data columns (total 3 columns):
x float64
y float64
z float64
dtypes: float64(3)
In＆lt; 0.14.0，使用pd.TimeGrouper
In [35]: g = winddata.groupby(pd.Grouper(freq='30T'))
In [36]: results = DataFrame({'x' : g['x'].mean(), 'y' : g['y'].mean() })
In [37]: results['wd'] = np.arctan2(results['x'],results['y'])
In [38]: results['ws'] = np.sqrt(results['x']**2+results['y']**2)
In [39]: results
Out[39]:
x y wd ws
2014-04-30 14:00:00 0.005060 1.986778 0.002547 1.986784
2014-04-30 14:30:00 0.004922 2.015551 0.002442 2.015557
2014-04-30 15:00:00 -0.004209 1.988889 -0.002116 1.988893
2014-04-30 15:30:00 0.008410 2.003453 0.004198 2.003470
2014-04-30 16:00:00 0.004027 1.997369 0.002016 1.997373
... ... ... ... ...
2014-05-05 13:00:00 0.006901 1.991252 0.003466 1.991264
2014-05-05 13:30:00 0.005458 2.008731 0.002717 2.008739
2014-05-05 14:00:00 -0.000805 2.000045 -0.000402 2.000045
2014-05-05 14:30:00 -0.004556 1.997437 -0.002281 1.997443
2014-05-05 15:00:00 0.003444 2.000182 0.001722 2.000185
[243 rows x 4 columns]