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POHow to resample a python pandas TimeSeries containing dytpe Decimal values?
 

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  1. POHow to resample a python pandas TimeSeries containing dytpe Decimal values?
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    <p>I'm having a pandas <code>Series</code> object filled with decimal numbers of dtype Decimal. I'd like to use the new pandas 0.8 function to resample the decimal time series like this:</p> <pre><code>resampled = ts.resample('D', how = 'mean') </code></pre> <p>When trying this i get an "GroupByError: No numeric types to aggregate" error. I assume the problem is that np.mean is used internaly to resample the values and np.mean expects floats instead of Decimals. </p> <p>Thanks to the help of this forum i managed to solve a similar question using groupBy and the apply function but i would love to also use the cool resample function.</p> <p><a href="https://stackoverflow.com/questions/11459106/how-use-the-mean-method-on-a-pandas-timeseries-with-decimal-type-values">How use the mean method on a pandas TimeSeries with Decimal type values?</a></p> <p>Any idea how to solve this?</p> <p>Here is the complete ipython session creating the error:</p> <pre><code>In [37]: from decimal import Decimal In [38]: from pandas import * In [39]: rng = date_range('1.1.2012',periods=48, freq='H') In [40]: rnd = np.random.randn(len(rng)) In [41]: rnd_dec = [Decimal(x) for x in rnd] In [42]: ts = Series(rnd_dec, index=rng) In [43]: ts[0:3] Out[43]: 2012-01-01 00:00:00 -0.1020591335576267189022559023214853368699550628 2012-01-01 01:00:00 0.99245713975437366283216533702216111123561859130 2012-01-01 02:00:00 1.80080710727195758558139004890108481049537658691 Freq: H In [44]: type(ts[0]) Out[44]: decimal.Decimal In [45]: ts.resample('D', how = 'mean') --------------------------------------------------------------------------- GroupByError Traceback (most recent call last) C:\Users\THM\Documents\Python\&lt;ipython-input-45-09c898403ddd&gt; in &lt;module&gt;() ----&gt; 1 ts.resample('D', how = 'mean') C:\Python27\lib\site-packages\pandas\core\generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, l imit, base) 187 fill_method=fill_method, convention=convention, 188 limit=limit, base=base) --&gt; 189 return sampler.resample(self) 190 191 def first(self, offset): C:\Python27\lib\site-packages\pandas\tseries\resample.pyc in resample(self, obj) 65 66 if isinstance(axis, DatetimeIndex): ---&gt; 67 rs = self._resample_timestamps(obj) 68 elif isinstance(axis, PeriodIndex): 69 offset = to_offset(self.freq) C:\Python27\lib\site-packages\pandas\tseries\resample.pyc in _resample_timestamps(self, obj) 184 if len(grouper.binlabels) &lt; len(axlabels) or self.how is not None: 185 grouped = obj.groupby(grouper, axis=self.axis) --&gt; 186 result = grouped.aggregate(self._agg_method) 187 else: 188 # upsampling shortcut C:\Python27\lib\site-packages\pandas\core\groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs) 1215 """ 1216 if isinstance(func_or_funcs, basestring): -&gt; 1217 return getattr(self, func_or_funcs)(*args, **kwargs) 1218 1219 if hasattr(func_or_funcs,'__iter__'): C:\Python27\lib\site-packages\pandas\core\groupby.pyc in mean(self) 290 """ 291 try: --&gt; 292 return self._cython_agg_general('mean') 293 except GroupByError: 294 raise C:\Python27\lib\site-packages\pandas\core\groupby.pyc in _cython_agg_general(self, how) 376 377 if len(output) == 0: --&gt; 378 raise GroupByError('No numeric types to aggregate') 379 380 return self._wrap_aggregated_output(output, names) GroupByError: No numeric types to aggregate </code></pre> <p>Any help is appreciated. Thanks, Thomas</p>
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    1. COJust FYI you should try to avoid `Series` objects with `object` `dtype` as much as possible. Is there any particular reason why you need the unlimited precision of `Decimal` objects?
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