# pandas.core.resample.Resampler.aggregate

`Resampler.``aggregate`(arg, *args, **kwargs)

  Aggregate using callable, string, dict, or list of string/callables

  Parameters:

  func : callable, string, dictionary, or list of string/callables

  > Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.

  > Accepted Combinations are:

  > - string function name
  > - function
  > - list of functions
  > - dict of column names -> functions (or list of functions)

  Returns: | aggregated : DataFrame

  See also

  `pandas.DataFrame.groupby.aggregate`, `pandas.DataFrame.resample.transform`, `pandas.DataFrame.aggregate`

  Notes

  Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

  agg is an alias for aggregate. Use the alias.

  Examples

      >>> s = Series([1,2,3,4,5],
                      index=pd.date_range('20130101',
                                          periods=5,freq='s'))
      2013-01-01 00:00:00    1
      2013-01-01 00:00:01    2
      2013-01-01 00:00:02    3
      2013-01-01 00:00:03    4
      2013-01-01 00:00:04    5
      Freq: S, dtype: int64

      >>> r = s.resample('2s')
      DatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,
                              label=left, convention=start, base=0]

      >>> r.agg(np.sum)
      2013-01-01 00:00:00    3
      2013-01-01 00:00:02    7
      2013-01-01 00:00:04    5
      Freq: 2S, dtype: int64

      >>> r.agg(['sum','mean','max'])
                           sum  mean  max
      2013-01-01 00:00:00    3   1.5    2
      2013-01-01 00:00:02    7   3.5    4
      2013-01-01 00:00:04    5   5.0    5

      >>> r.agg({'result' : lambda x: x.mean() / x.std(),
                 'total' : np.sum})
                           total    result
      2013-01-01 00:00:00      3  2.121320
      2013-01-01 00:00:02      7  4.949747
      2013-01-01 00:00:04      5       NaN

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