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# pandas.Series.reindex

`Series.``reindex`(index=None, **kwargs)

  Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False

  Parameters : | index : array-like, optional (can be specified in order, or as keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data method : {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None Method to use for filling holes in reindexed DataFrame pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any “compatible” value limit : int, default None Maximum size gap to forward or backward fill takeable : boolean, default False treat the passed as positional values
  Returns : | reindexed : Series

  Examples

      >>> df.reindex(index=[date1, date2, date3], columns=['A', 'B', 'C'])

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