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

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


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)


reindexed : DataFrame

See also

reindex, reindex_like


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