# 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 or ‘index’, 1 or ‘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`.

  > Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

  > New in version 0.17.0.

  > New in version 0.21.0: (list-like tolerance)

  Returns: | reindexed : DataFrame

  See also

  `reindex`, `reindex_like`

  Examples

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

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