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# pandas.DataFrame.apply

`DataFrame.``apply`(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)

  Applies function along input axis of DataFrame.

  Objects passed to functions are Series objects having index either the DataFrame’s index (axis=0) or the columns (axis=1). Return type depends on whether passed function aggregates, or the reduce argument if the DataFrame is empty.

  Parameters : | func : function Function to apply to each column/row axis : {0, 1} 0 : apply function to each column 1 : apply function to each row broadcast : boolean, default False For aggregation functions, return object of same size with values propagated reduce : boolean or None, default None Try to apply reduction procedures. If the DataFrame is empty, apply will use reduce to determine whether the result should be a Series or a DataFrame. If reduce is None (the default), apply’s return value will be guessed by calling func an empty Series (note: while guessing, exceptions raised by func will be ignored). If reduce is True a Series will always be returned, and if False a DataFrame will always be returned. raw : boolean, default False If False, convert each row or column into a Series. If raw=True the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance args : tuple Positional arguments to pass to function in addition to the array/series Additional keyword arguments will be passed as keywords to the function
  Returns : | applied : Series or DataFrame

  See also

  `DataFrame.applymap`
    For elementwise operations

  Notes

  In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.

  Examples

      >>> df.apply(numpy.sqrt) # returns DataFrame
      >>> df.apply(numpy.sum, axis=0) # equiv to df.sum(0)
      >>> df.apply(numpy.sum, axis=1) # equiv to df.sum(1)

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