# pandas.DataFrame.reindex_axis

`DataFrame.``reindex_axis`(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)

  Conform input object 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:

  labels : array-like

  > New labels / index to conform to. Preferably an Index object to avoid duplicating data

  axis : {0, 1, ‘index’, ‘columns’}

  method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

  > Method to use for filling holes in reindexed DataFrame:
  >   - default: don’t fill gaps
  >   - pad / ffill: propagate last valid observation forward to next valid
  >   - backfill / bfill: use next valid observation to fill gap
  >   - nearest: use nearest valid observations 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

  limit : int, default None

  > Maximum number of consecutive elements to forward or backward fill

  tolerance : optional

  > Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation `abs(index[indexer] - target) <= tolerance`.

  > New in version 0.17.0.

  Returns: | reindexed : DataFrame

  See also

  `reindex`, `reindex_like`

  Examples

      >>> df.reindex_axis(['A', 'B', 'C'], axis=1)